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Predicting stability factors for rotational failures in earth slopes and embankments using artificial intelligence techniques

  • Ahmed Cemiloglu , Yingying Cao , Arkan K. S. Sabonchi and Yaser A. Nanehkaran EMAIL logo
Published/Copyright: November 28, 2024
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Open Geosciences
From the journal Open Geosciences Volume 16 Issue 1

Abstract

This study focuses on slope stability analysis, a critical process for understanding the conditions, durability, mass properties, and failure mechanisms of slopes. The research specifically addresses rotational-type failure, the primary instability mechanism affecting earth slopes. Identifying and understanding key factors such as slope height, slope angle, density, cohesion, friction, water pore pressure, and tensile cracks are essential for effective stabilization strategies. The objective of this study is to develop accurate predictive models for slope stability analysis using advanced intelligent techniques, including data mining mapping and complex decision tree regression (DTR). The models were validated using performance metrics such as mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE), and the coefficient of determination (R²). Additionally, overall accuracy was assessed using a confusion matrix. The predictive model was tested on a dataset of 120 slope cases, achieving an accuracy of approximately 91.07% with DTR. The error rates for the training set were MAE = 0.1242, MSE = 0.1722, and RMSE = 0.1098, demonstrating the model’s capability to effectively analyze and predict slope stability in earth slopes and embankments. The study concludes that these intelligent techniques offer a reliable approach for stability analysis, contributing to safer and more efficient slope management.

Keywords: slope stability; earth-slopes; rotational-type failure; AI algorithms; machine learning‎

1 Introduction

Slope stability is one of the most important and greatest challenges in geotechnical ‎engineering during various engineering designs and constructions [1,2,3,4,5,6,7]. Slope stability refers to ‎the ability of a soil or rock slope to remain in place and resist failure due to the force of ‎gravity [8,9,10]. The stability of a slope depends on factors such as the strength of the material, the ‎slope angle, the amount and type of vegetation, and the presence of water or other fluids, ‎geo-unit condition, the type of soil or rock, the presence of cracks or other defects, erosion ‎or other processes, and slope internal specification [11,12,13]. A slope that is not stable can result in ‎sliding, rockfalls, toppling, and other types of mass wasting [14,15,16]. To assess slope stability, ‎geo-engineers use a combination of field observations, laboratory testing, and modeling to ‎evaluate the factors that affect stability and to determine the likelihood of failures [17]. To ‎ensure stability, engineers may use a variety of techniques, such as soil nailing, anchoring ‎systems, terracing, retaining walls, or the creation of vegetative cover. In some cases, it may ‎be necessary to modify the slope to reduce its angle or to reinforce it with materials such as ‎concrete or steel. The design of these mitigation measures depends on the specific conditions ‎and characteristics of the site and the stability issues it presents [18].‎

It is important to note that slope stability is a complex and interdisciplinary field, and the ‎assessment and design of slope stability solutions require the expertise of geo-engineers ‎who provide appropriate stabilizations [17]. The goal of slope stability analysis and design is ‎to ensure public safety and prevent damage to property, infrastructure, and the ‎environment [19]. Thus, providing a detailed and accurate stability analysis as well as a proper ‎understanding of the instability mechanism that might happen in slopes can be useful. ‎Geometry, stress–strain history, structural and tectonic conditions, geomorphologic status, ‎regional climate, seismic activity, water conditions, vegetation, weathering, drainage ‎pattern, construction activities, and special occasions all have a direct impact on the type of ‎slope failures and sliding mechanisms [20,21]. Depending on nature of the sliding mass, the slope’s ‎failure mechanisms can be classified as wedge, planar, toppling, and rotational (or circular-‎type) failures [8]. ‎

Rotational failure is a common type of slope failure in which a soil mass or embankment ‎material rotates along a curved surface, typically a circular or spiral-shaped failure plane. ‎This type of failure is typically caused by a combination of factors such as soil strength, ‎slope angle, and the presence of water or other fluid in the slope material. The movement of ‎the slope mass can be triggered by changes in the slope conditions such as heavy rainfall, ‎earthquakes, or changes in the water table [22,23]. Also, rotational failure can result in the creation ‎of large circular or spiral-shaped depressions in the slope, which can be shallow or deep and ‎can be several to hundreds of meters in diameter. Figure 1 illustrates the main concept of ‎rotational failure, which occurred in slopes and embankments. The rotational failure process ‎can be gradual or rapid and can cause significant damage to infrastructure and property, as ‎well as pose a risk to public safety [24]. So, accurate estimation of the slope stability can be ‎considered as the main duty for slope or embankment design [25,26]. ‎

Figure 1 Factored parameters on slope stability subjected to rotational failures.
Figure 1

Factored parameters on slope stability subjected to rotational failures.

Numerous approaches have been introduced and used to analyze or predict slope stability ‎with a background of more than 300 years, which include a range of simple evaluations, ‎planar failure, limit state criteria, limit equilibrium analysis, numerical methods, hybrid and ‎high-order approaches, which are implemented in two and three dimensions [27]. These ‎methods have their own certain advantages and limitations that directly affect the stability ‎results [28]. Also, there are various uncertainties that are imposed from stability factors, which have ‎an effect on stability decisions. In this regard, professionals are always looking for new and ‎efficient methods that are capable of reducing the uncertainty rate and increase the accuracy of the ‎calculations. Recently, with the advancement of intelligent technology applications in ‎geotechnics, various machine learning-based methods was successfully applied to predict ‎slope stability and factored parameters subjected to different failure types [29,30]. Table 1 provides a summary of some relative machine learning-based methods that are used in slope ‎stability analysis by researchers. The main objective of the literature on rotational failures ‎on slopes is based on factored parameters that lead to calculation by these methodologies ‎, which are presented in Figure 1 properly. These factors are considered in predictive models by ‎developers to conduct more accurate results. In such cases, the machine learning-based ‎methods are chasing several goals like safety factor (FS), durability status, and stability ‎class or reliability predictions for slopes and embankments. These methods cover extended ‎aspects of machine learning and data mining mapping procedures. In the meantime, complex ‎decision tree regression (DTR) has received remarkable success recently in stability analysis for ‎geotechnical assessments [31,32,33,34]. ‎

Table 1

A summary of recent slope stability prediction techniques based on machine ‎learning

Reference Computational learning methods Other methods
MLP Fuzzy SVM GA ANFIS RF DT k-NN PSO MC GNB ClM LR DNN
Suman et al. [35] * *
Hoang and Pham [36] * * *
Xue [37] * *
Kang et al. [38] * * * *
Fattahi [39] * * *
Feng et al. [40] *
Rukhaiyar et al. [41] * *
Qi and Tang [42]
Xu et al. [43] *
Bui et al. [44] * * *
Koopialipoor et al. [45] * * * Imperialist competitive
Sari et al. [46] * *
Zhou et al. [47] * * * Gradient boosting
Yuan and Moayedi [48] * * * * *
Gao et al. [49] * Imperialist competitive
Zheng et al. [50] * Limit equilibrium
Palazzolo et al. [51] *
Azmoon et al. [52] * * *
Zhou et al. [53] * *
Mahmoodzadeh et al. [54] * * * * * *
Lin et al. [55] * * Gradient boosting
Nanehkaran et al. [56] * * * * *
Mu’azu [57] * * *
Nanehkaran et al. [58] * * * * Limit equilibrium

MLP: multilayer perceptron, Fuzzy: fuzzy logic, SVM: support-vector machines, GA: genetic algorithm, ANFIS: adaptive neuro-fuzzy inference system, RF: random forest, DT: decision tree, k-NN: k-nearest neighbour, PSO: particle-swarm optimization, MC: Monte-Carlo simulations, GNB: gaussian naïve bayes, ClM: clustering method, LR logistic regression, DNN: deep neural networks.

DTR is a type of regression analysis method that uses a tree-‎like model of decisions and their possible consequences. The goal of DTR is to create a ‎model that predicts a target numerical value based on certain input features. Each internal ‎node of the tree represents a test on an input feature, each branch represents the outcome of ‎the test, and each leaf node represents a predicted value for the target variable. The ‎prediction of the DTR model is the average value of the target variable of the samples in the ‎corresponding leaf node [59]. DRT models overcome the shortcomings of other data mining ‎mapping techniques by producing a transparent and structural model that explicitly ‎represents the relationship between input and output parameters [60]. The presented study attempted ‎to use a complex DTR predictive model to provide stability prediction based on factored ‎parameters, which was implemented on 120 slope cases in Iran.‎

Considering the wide range of studies on advanced technologies in slope stability and soil embankment stability analysis, researchers have explored various approaches. These include intelligent systems, decision-making techniques, and machine learning methodologies [61,62,63,64,65,66,67], as well as numerous numerical modeling techniques [68,69,70,71,72,73]. With reviewing the variety of literature, a main gap that professionals face with where this study addresses is the need for more accurate and reliable predictive models for slope stability analysis, specifically focusing on rotational-type failures. While previous studies have explored various methods for slope stability assessment, there has been a lack of research applying advanced intelligent techniques like data mining and complex DTR. This study fills that gap by developing and validating a model that significantly enhances the prediction accuracy and reliability of slope stability assessments.

2 Methods

2.1 DTR principles

The DTR is a tree-like model which uses regression analysis ‎procedures for decisions and their possible consequences [59]. Figure 2 provides a basic schematic diagram of DTR regression trees. DTR is used for both linear and ‎non-linear relationships between the target variable and input features. It can handle high-‎dimensional input data and complex interactions between features. One important aspect of ‎decision trees is the process of “pruning” the tree, which involves removing branches that do ‎not contribute much to the accuracy of the model to prevent overfitting [75]. Another aspect to ‎consider is the choice of splitting criterion, such as mean squared error (MSE) or mean absolute ‎error, which determines how the tree splits the data based on the input features. DTR can be ‎sensitive to small variations in the data and can be improved by using ensemble methods ‎such as random forest regressions [59]. DTR has several advantages and disadvantages, which ‎can be used in different aspects of decision-based evaluations [76,77]. Some of the advantages of DTR ‎can be presented as follows [75]:‎

  1. Easy to understand and interpret: The tree structure of a Decision Tree makes it easy ‎to understand and interpret the relationships between the features and the target ‎variable.

  2. Handles non-linear relationships: Decision Trees can handle complex non-linear ‎relationships between features and target variable.

  3. Can handle missing values: Unlike many other machine learning algorithms, ‎Decision Trees can handle missing values in the data.‎

  4. Not sensitive to outliers: Decision Trees are not sensitive to outliers in the data, as ‎the splits in the tree are based on the overall distribution of the data.‎

Figure 2 A schematic diagram for a basic DTR [74].
Figure 2

A schematic diagram for a basic DTR [74].

Also, disadvantages of the DTR are provided as follows [74]:‎

  1. ‎Overfitting: If a decision tree is not pruned, it can easily overfit the data and perform ‎poorly on new, unseen data,‎

  2. ‎Instability: Decision Trees can be unstable, as small changes in the data can lead to ‎large changes in the structure of the tree.‎

  3. Bias towards features with many categories: Decision Trees tend to split more ‎frequently on features with many categories, which can lead to a bias towards these ‎features.‎

Overall, DTR is a powerful and flexible machine-learning algorithm that can be useful for ‎many regression problems. However, it is important to carefully evaluate the advantages and ‎disadvantages and determine if it is the best approach for a given problem. One of the good ‎aspects of DTR is chasing various scenarios and variables to provide accurate result based ‎on existed information, which are proper circumstances to using in geotechnical aspect and slope stability ‎assessments. In this regard, DTR works by dividing the input data into smaller subsets based ‎on the values of the features and then making predictions based on the average target value ‎of the samples in each subset. The process of dividing the data into subsets is repeated until a ‎stopping criterion is met, such as a minimum number of samples in a leaf node or a minimum ‎decrease in the variance of the target variable. The basic steps in DTR modeling can be ‎presented as follows:‎

  1. Select the best feature to split the data: The algorithm selects the feature that ‎provides the largest decrease in the variance of the target variable.

  2. Divide the data into subsets: The data is divided into subsets based on the values of ‎the selected feature.

  3. Create a new internal node for the selected feature: The algorithm creates a new ‎internal node for the selected feature and adds the subsets as branches.‎

  4. Repeat the process for each subset: The process is repeated for each subset until a ‎stopping criterion is met.‎

  5. Create a leaf node for each subset: Once the stopping criterion is met, the algorithm ‎creates a leaf node for each subset and assigns a predicted target value based on the ‎average of the target values of the samples in that subset.‎

  6. The final result of the DTR is a tree-like model that can be used to make predictions ‎for new, unseen data.‎

2.2 Data acquisition and structured database

The rotational failure for earth slopes and embankments is targeted in this research. The ‎rotational failure is the main failure mechanism that threatens soil slope worldwide, and ‎providing an alternative stability prediction model can be used frequently to cover the ‎analytical gap in traditional stability analysis. As presented in Figure 1, there are several ‎certain factored parameters that have a direct impact on soil slope and embankment’s stability‎, which include slope height, slope angle, density, cohesion, friction, water pore pressure, ‎and tensile crack. These parameters can be categorized into several main triggering groups ‎such as geometric partial factors, geo-materials partial factors, strength partial factors, and ‎water conditions. These factored parameters are imposed on slope stability status, and ‎the durability of slope, which leads to stable or unstable conditions was considered as input ‎parameters. The FS, slope durability status (SDS), and stability class were considered as ‎main output values. ‎

The prepared database from 120 different earth-slope and embankment cases contains ‎seven input variables and three output variables data. The input parameters consist of slope ‎height (H), slope angle (β), density (γ), cohesion (c′), friction (ϕ′), water pore pressure (U), ‎tensile crack (V), and output parameters are FS, SDS, and stability class. The prepared ‎database was randomly divided into testing (30% for the main database) and training (70% for the main ‎database) datasets. The database contains 120 cases, which are segregated into 84 cases for the training ‎dataset and 36 cases for the testing dataset which both stable and unstable slopes are considered. It should be noted that the primary dataset used in this study comprises a variety of case reports from different regions of Iran. These reports were compiled by researchers and professionals and include both literature reviews and technical notes. ‎The input and output parameters, histograms, and statistical features of the discussed database ‎are shown in Table 2. The main statistical indexes used in this study are the minimum ‎‎(max), maximum (min), mean, standard deviation (Std.Dev.), variance, and skewness values. ‎The main limit state circumstance was selected for FS variation (FS = 1.0), which represents ‎stability class and slope durability. So, for all stability calculations, FS is greater than 1.0, ‎slope is considered as stable; and for FS less than 1.0, slope is considered as unstable. It should be noted that ‎the slope at the F.S is equal to 1.0, the probability of stability status is equal to 50%; so, ‎the state is considered as the critical state. ‎

Table 2

Statistical features analysis for input and output factored parameters

Parameter Max Min Mean Std.Dev. Variance Skewness VAF (%)
Input
Slope height (m) 25.0 3.50 14.25 8.77 84.59 1.92 88.1
Slope angle (degree) 90.0 32.0 61.00 23.67 90.88 2.21 75.8
Density (kN/m3) 21.0 17.3 19.15 1.51 80.06 1.84 78.5
Cohesion (kPa) 62.0 0.00 31.0 25.31 64.87 2.20 89.0
Friction (degree) 39.2 30.0 34.6 3.755 25.19 2.13 93.1
Water pore-pressure 0.50 0.00 0.25 0.204 0.042 0.82 71.6
Tensile crack (m) 1.20 0.10 0.65 0.449 0.211 1.05 77.8
Output
F.S 2.0 0.0 1.0 — — — —
Stability class 1.0 0.0 1.0 — — — —
SDS Stable Unstable Critical — — — —

Additionally, the Variance Accounted For (VAF) was used to indicate how much of the variance in a dependent variable can be explained by one or more independent input variables in a predictive model. In practical terms, VAF helps to understand the proportion of the total variability in the outcome variable that is attributable to the predictors or explanatory variables being studied. So, higher VAF values suggest that a larger proportion of the variability in the dependent variable is accounted for by the model, which generally indicates a better fit of the model to the data. It should be noted that VAF is used in machine learning in slightly different ways depending on the context and the specific type of model. The presented study used explained variance value which indicates the proportion of the total variance in the target variable that is captured by the model. It helps evaluate how well the model explains the variability of the target.

2.3 Predictive model implementation

DTR regression is a decision tree algorithm used for the prediction of continuous variables. ‎The algorithm works by recursively splitting the data into subsets based on the features that ‎best explain the target variable. The tree is constructed by repeating the following steps (‎Figure 3):‎

  1. Step 1: Select the feature that provides the highest reduction in variance or mean squared ‎error as the split criteria.‎

  2. Step 2: Partition the data based on the selected feature and assign the resulting subsets to ‎child nodes of the current node.‎

  3. Step 3: At each leaf node, the prediction is made by computing the mean of the target ‎variable for the samples in that node.‎

  4. Step 4: The algorithm continues until a stopping criterion is reached, such as a maximum ‎depth of the tree, a minimum number of samples required at a leaf node, or a minimum ‎reduction in variance or MSE at each split.‎

  5. Step 5: Once the tree is built, predictions can be made by traversing the tree to find the ‎leaf node that best fits the input data and returning the mean prediction for that node.‎

Figure 3 Process flowchart of the implemented DTR method.
Figure 3

Process flowchart of the implemented DTR method.

These steps are implemented in stability analysis based on input parameters to get output ‎results, which was implemented in the Python high-level programming language. Used ‎hyperparameters for DTR include the maximum depth of the tree, the minimum number of ‎samples required at a leaf node, and the criteria used to determine the best split at each node ‎‎(such as MSE or variance reduction). Hyperparameters are commonly used to ‎optimize the fitting process which can increase the machine learning model prediction ‎accuracy. These hyperparameters can be tuned through methods such as cross-validation to ‎find the optimal combination for a given problem. Using hyperparameters increases the ‎learning rate and overall accuracy estimation in machine learning algorithms. The model ‎learning rate (test/train ratio) is a response to estimated errors each time the model weights are ‎updated. In fact, how quickly the model adapts to the problem is controlled by the learning ‎rate. While larger learning rates result in rapid changes and require fewer training epochs, ‎smaller learning rates require more training epochs due to smaller changes to the weights at ‎each update. In particular, the learning rate is used configurable hyperparameters [78]. It has a ‎small positive value, usually between 0.0 and 1.0. The learning rate used in this study was ‎selected by optimizers, which for 0.01 and no momentum was scheduled via callbacks in ‎Keras support. Pearson’s Phi coefficient was estimated for each input and output parameter, which is presented in Figure 4. Pearson’s Phi coefficient is a measure of association between two ‎nominal variables. It ranges from −1 to 1 and provides the strength and direction of the ‎relationship between the two variables. A value of 1 indicates a strong positive association, a ‎value of −1 indicates a strong negative association, and a value of 0 indicates no association. ‎Pearson’s Phi is commonly used in contingency table analysis [78].‎

Figure 4 Pearson’s coefficient for each factored parameters.
Figure 4

Pearson’s coefficient for each factored parameters.

Figures 5 and 6 provide the scatterplots and statistical analysis for normalization for ‎input data ‎used in this study, which is included H, β, γ, c′, ϕ′, V parameters. A scatterplot is a ‎type of data visualization that displays the relationship between two variables [79], which ‎normally input parameters for a specific type of analysis. It consists of points plotted on a ‎two-dimensional plane, where each point represents the value of one variable relative to the ‎other [78]. Scatterplots are particularly useful for identifying patterns, trends, and potential ‎correlations between variables. For instance, if the points on the plot form a clear pattern, ‎such as a straight line, it suggests a strong relationship between the variables, while a more ‎scattered distribution indicates a weaker relationship or no relationship at all. By visually ‎examining the scatterplot, analysts can gain insights into the nature and strength of the ‎relationship between the variables, helping them make informed decisions in the process of ‎modeling [79]. Statistical normalization, in other words, refers to the process of ‎transforming data into a standard format to make it more interpretable or comparable [78]. ‎Normalization techniques are commonly used in data preprocessing to address issues such as ‎varying scales, units, or distributions among different variables. One of the most common ‎normalization techniques is z-score normalization (standardization), where each data point is ‎transformed to have a mean of zero and a standard deviation of one [78]. This method allows ‎analysts to compare variables on a common scale and facilitates the interpretation of ‎statistical measures such as means and standard deviations. Other normalization techniques ‎include min–max scaling, where data is scaled to a fixed range (e.g., between 0 and 1), and ‎robust scaling, which is less sensitive to outliers compared to standardization [79].‎

Figure 5 The scatterplot for input data ‎used in this study.
Figure 5

The scatterplot for input data ‎used in this study.

Figure 6 The statistical analysis for normalization of input parameters: (a) H, (b) β, (c) γ, (d) c′, (e) ϕ′, ‎‎(f) V.
Figure 6

The statistical analysis for normalization of input parameters: (a) H, (b) β, (c) γ, (d) c′, (e) ϕ′, ‎‎(f) V.

2.4 Model validations

To assess the DTR method rigorously, its accuracy was evaluated using statistics from the ‎confusion matrix and statistical error indexes. A confusion matrix is a table used to evaluate ‎the performance of a classification model. It summarizes the true positive, false positive, true negative, and false negative predictions made by the model. The entries ‎in the matrix are used to calculate various evaluation metrics such as accuracy, precision, ‎recall, and F1 score, which provide insight into the model’s performance and help identify ‎areas for improvement. Confusion matrices are commonly used in fields such as machine ‎learning to provide performance analysis for different learning algorithms [78]. The ‎coordination of the positivity and negativity of the variables, as well as the evaluation ‎criteria estimations in a confusion matrix [79], is presented in Figure 7.‎ In accordance with this figure, the evaluation ‎criteria estimated via the confusion matrix can be calculated as follows:

(1) Accuracy = ∑ TP + TN ∑ TP + FP + FN + TN ,

(2) Precision = ∑ TP ∑ TP + FP ,

(3) Recall = ∑ TP ∑ TP + FN .

Figure 7 The confusion matrix and evaluation criteria [79‎].
Figure 7

The confusion matrix and evaluation criteria [79‎].

Additionally, the mean absolute error (MAE), MSE, and root-mean-‎square error (RMSE) were considered as error evaluation indexes, which provide the DTR-‎based model errors during prediction. MAE is a commonly used metric for evaluating the ‎accuracy of a prediction algorithm. It measures the average of the absolute differences ‎between the predicted values and the actual values. It is defined as MAE = (1/n) × Σ|actual – ‎predicted|, where n is the number of samples and actual and predicted are the actual and ‎predicted values, respectively. MAE provides a robust measure of prediction accuracy, ‎as it is insensitive to outliers. MSE is a common metric for evaluating the accuracy of a ‎prediction algorithm. It measures the average of the squared differences between the ‎predicted values and the actual values. It is defined as MSE = (1/n) × Σ(actual − predicted)2. ‎The squared differences amplify larger errors compared to the absolute differences in MAE, ‎making MSE a more sensitive measure of prediction accuracy. RMSE is a commonly used ‎metric for evaluating the accuracy of a prediction algorithm. It is the square root of the mean ‎of the squared differences between the predicted values and the actual values. It is defined as ‎RMSE = √(MSE) = √((1/n) × Σ (actual – predicted)2). RMSE provides a more interpretable ‎measure of prediction accuracy, as it is expressed in the same units as the actual and ‎predicted values.‎ Conversely,

(4) MAE = 1 n × ∑ ∣ Actual-Predicted ∣ ,

(5) MSE = 1 n × ∑ ( Actual - Predicted ) 2 ,

(6) RMSE = MSE = 1 n × ∑ ( Actual - Predicted ) 2 .

3 Results

Based on the methodology previously discussed, this research attempted to use the DTR ‎classification algorithm to investigate rotational failure in earth-slopes and ‎embankments. In this regard, the main factored parameters conclude H, β, γ, c′, ϕ′, U, and V was ‎considered as input data that directly affected on slope’s stability, F.S, and SDS are used for ‎prediction modelling by DTR. Results were validated using statistical error indices ‎and a confusion matrix, which are commonly used performance controllers for machine ‎learning algorithms. The modeling results were plotted, and the coefficient of ‎determination (R 2) was estimated for all the predictions in both testing and training datasets. ‎As presented in Table 2, the output of the model was shown as FS, SDS, and stability class ‎for slope, which are discrete quantities. These discrete quantities are adopted and estimated ‎from continuous input quantities, which is consistent with the functional nature of the ‎forecasting algorithm used. This issue will also have a significant impact on increasing the ‎accuracy of model implementation. Figures 8 and 9 provides the predictive model results ‎for both the testing and training databases for estimating FS, SDS, and stability classes. Table 3 illustrates the results of the linear relationship between FS and factored parameters, as ‎predicted by the model-studied slope cases for both testing and training datasets. Also, Table ‎‎4 provides information about the model validation process. Figures 10–12 provide model accuracy and errors during the process as well as decision levels that ‎consider calculating stability classes with the DTR algorithm.‎

Figure 8 F.S variations between actual-predicted values in train and test datasets.
Figure 8

F.S variations between actual-predicted values in train and test datasets.

Figure 9 SDS and stability class variations between actual-predicted values in train and test ‎datasets.
Figure 9

SDS and stability class variations between actual-predicted values in train and test ‎datasets.

Table 3

DTR regression relationships obtained by predictive model

Index Dataset Relation
F.S Train F.S = 0.0172γ – 0.0392β – 0.00428H + 0.085c′ + 0.0054ϕ′ – 0.31U – 0.21V + 0.9452
Test F.S = 0.0083γ – 0.042β – 0.0061H + 0.077c′ + 0.002ϕ′ – 0.56U –0.32V + 1.068
Stability class/SDS Train SC or SDS = 1 (stable) if F.S is x > 1.0001
Test SC or SDS = 0 (unstable) if F.S is x < 1.0000
Table 4

Validation table for prediction process estimated by DTR model

Parameter Dataset Confusion matrix MAE MSE RMSE
Precision Recall Accuracy
F.S Train 0.9025 0.9025 0.9107 0.1242 0.1722 0.1098
Test 0.8811 0.8825 0.8711 0.2360 0.2441 0.2209
SDS Train 0.8629 0.8535 0.8629 0.2571 0.2663 0.2344
Test 0.8077 0.8044 0.8044 0.2700 0.2736 0.2557
Stability class Train 0.8956 0.8960 0.8960 0.1925 0.1932 0.1650
Test 0.8673 0.8649 0.8648 0.2102 0.1988 0.1920
Figure 10 Predictive model loss function and accuracy for training and test dataset.
Figure 10

Predictive model loss function and accuracy for training and test dataset.

Figure 11 The SDS, F.S and stability class variations for test-train datasets based on decision ‎frequency.
Figure 11

The SDS, F.S and stability class variations for test-train datasets based on decision ‎frequency.

Figure 12 The results statistical normalization for predict and calculated F.S values.
Figure 12

The results statistical normalization for predict and calculated F.S values.

Figures 8 and 9 are provide detailed variations of actual FS, SDS, and stability ‎class for earth-slope versus predictive values for both testing and training datasets which is resulted from ‎main database contained 120 slope cases. The results were measured by the coefficient of ‎determination (R 2) and linear regression to understand the scattering ‎status of the obtained results. The figures show that R 2 values for testing ‎and training datasets are 0.9645 and 0.9589, respectively. R 2 results indicate ‎that the model has good agreements with the prediction of actual data in both testing and ‎training sets.‎

Table 3 shows the relationship between the DTR prediction ‎model with FS, SDS, and stability class. Slope stability is ‎directly increased by c′, ϕ′, γ and decreased by β, H, V, U. These factored parameters also ‎show a linear relationship with slope durability and slope classes. Table 3 shows that γ, ‎c′, ϕ′, have positive effects on FS, while U, V, H and β have negative effects. So, the stabilization process has to be considered based on increases ‎positive partial factors (like enhancing the geo-material propitiates and modifying geometry ‎and reinforce slope mass) by considering all negative parameters (like geometrical ‎modification with efficient drainages). Table 4 presents the estimated confusion matrix and statistical error indexes for the DTR ‎predictive model. The model achieves 90.25% precision and 91.07% accuracy in calculating the FS. The SDS and stability class have 86.29 and 86.29% precision and 89.56 and 89.60% ‎accuracy values, respectively. The FS ‎prediction has MAE, MSE, and RMSE values of 0.1242, 0.1722, and 0.1098, whereas SDS and stability class have values of 0.2571, 0.2663, ‎‎0.2344 and 0.1925, 0.1932, 0.1650. These findings demonstrate, from a practical ‎standpoint, that the DTR algorithm is effective and useful for predicting the sfactored-based ‎stability analysis for soil slopes and embankments.‎

4 Discussion

This article highlights the importance of input factors in affecting the performance of DTR models. . Some key parameters are:

  1. ‎Maximum tree depth: Controls the size of the tree and limits the number of splits in ‎the tree, which affects the model’s ability to capture complex relationships between ‎features and the target.

  2. Minimum samples per leaf node: Specifies the minimum number of samples required ‎to form a leaf node, which affects the model’s ability to generalize to new data.‎

  3. Splitting criterion: Determines how the model selects the best feature to split on at ‎each node, such as MSE or MAE.

  4. Maximum number of features to consider when splitting: Controls the number of ‎features considered when making a split, which can help reduce overfitting by ‎limiting the model’s ability to memorize the training data.

  5. Regularization: This parameter controls the complexity of the tree by adding ‎penalties to the cost function that the model is trying to minimize. Common ‎regularization techniques include L1 (Lasso) and L2 (Ridge) penalties.

  6. Pruning: This technique involves removing branches of the tree that do not contribute ‎much to the overall performance of the model. Pruning helps to reduce overfitting ‎and improve the interpretability of the model.

  7. Random seed: Specifying a random seed value for the model can ensure that the same ‎results are obtained each time the model is trained, even with the same data and ‎parameters.‎

  8. The results from the DTR model showed high accuracy and precision in predicting the FS, SDS, and stability class for slopes and embankments. The model’s R² values (0.9645 for testing and 0.9589 for training datasets) indicate a strong correlation between the predicted and actual values, suggesting that the model is reliable for practical applications. The influence of the parameters on slope stability was consistent with geotechnical principles: parameters like cohesion (c′), internal friction angle (ϕ′), and unit weight (γ) positively impacted the FS, while height (H), slope angle (β), pore-water pressure (U), and surcharge (V) negatively impacted it. This aligns with the understanding that improving soil strength and reducing destabilizing forces enhances slope stability.

  9. Proper tuning of these parameters can result in better model performance, improved ‎accuracy, and reduced overfitting. So, it is important to keep in mind that overfitting is a ‎common problem in regression trees and can be mitigated by carefully choosing the input ‎parameters, as well as by using cross-validation to evaluate the performance of the model on ‎unseen data. Additionally, it is a good practice to compare the performance of multiple ‎models with different input parameters to find the best-performing model. Even though the ‎DTR model performed well in the prediction of slope stability conditions, overfitting has to be ‎considered each time using DTR. Generally, regression trees are prone to overfitting, ‎which means that the model becomes too complex and fits the training data too well, ‎resulting in poor generalization to new data. Overfitting occurs when the tree has too many ‎splits and becomes too deep, leading to a model that is too complex to effectively capture ‎the underlying patterns in the data. To combat overfitting in DTR, there are several ‎techniques that can be used such as pruning, regularization, cross-validation, and feature ‎selections. The presented study used cross-validation, pruning, and regularization techniques ‎to reduce the overfitting in the learning procedure of the model.‎

In comparing these findings with previous research, it is essential to highlight both the similarities and differences (e.g., [80,81,82,83,84,85,86,87,88,89]):

  1. Accuracy and Model Performance: The precision and accuracy of the DTR model (over 90% for FS and around 86% for SDS and stability class) are comparable to or even exceed those reported in previous studies using different machine learning models, such as support vector machines (SVM) or neural networks, which typically report accuracy in the range of 80–90%.

  2. Parameter Influence: Previous studies also consistently report the positive impact of cohesion (c′), internal friction angle (ϕ′), and unit weight (γ) on slope stability, and the negative impact of factors like slope height (H) and slope angle (β). However, the DTR model’s ability to explicitly show the linear relationships between these factors and stability indicators enhances interpretability, which might not be as clear in more complex models like neural networks.

  3. Model Validation Metrics: The use of MAE, MSE, and RMSE for validation is standard in the field. The relatively low error values in this study suggest that the DTR model is particularly well-suited for this type of prediction, potentially offering an advantage over more computationally intensive methods like deep learning, which may require larger datasets and more computational resources.

  4. Practical Implications: The practical utility of the DTR model, given its balance of accuracy and interpretability, may surpass that of more complex models, particularly in engineering applications where understanding the influence of specific parameters is crucial. This can lead to better-informed decisions in slope stabilization efforts.

As compared to selected studies, the DTR model not only aligns with findings from previous studies but also offers clear advantages in terms of accuracy, interpretability, and practical applicability. Future research could explore combining DTR with other models or hybrid approaches to further enhance prediction accuracy and broaden the model’s applicability to different types of slopes and geotechnical conditions.

5 Conclusion

This study introduces an advanced predictive model that utilizes data mining and complex DTR algorithms to assess slope stability based on a comprehensive database of 120 slope cases from Iran. The model demonstrates significant effectiveness, achieving an accuracy of 91.07% and a precision of 90.25% for predicting the FS. For SDS, the model’s accuracy is 86.29%, and it performs with an accuracy range of 89.56–89.60% for stability class predictions. The model’s performance is further validated by error metrics, with MAE, MSE, and RMSE values indicating robust prediction capabilities. A notable finding of this study is the substantial impact of parameters such as cohesion (c′), friction (ϕ′), and density (γ) on slope stability, where increased values enhance stability. Conversely, higher slope angle (β), height (H), tensile crack (V), and water pore-pressure (U) are found to negatively influence stability. This insight provides a clear understanding of how various factors interact to affect slope stability. To maximize the utility of the DTR model, it is recommended that it be applied in real-world slope stability assessments, especially in regions with geological conditions similar to those of the Iranian slopes studied. Future research should focus on adapting the model to different geographical locations and soil types to enhance its generalizability. Additionally, integrating real-time monitoring data could further improve the model’s accuracy and responsiveness. However, the study acknowledges certain limitations. The model is based on a specific dataset from Iran, which may restrict its applicability to other regions with different environmental and geological conditions. Additionally, the model’s effectiveness depends on the accuracy of input parameters and may not account for all variables affecting slope stability. Ongoing refinement and validation with diverse datasets are crucial to address these limitations and fully exploit the model’s potential across various contexts.

Acknowledgments

The authors would like to thank the anonymous reviewers for providing invaluable ‎review comments and recommendations for improving the scientific level of the article.‎

  1. Funding information: This research was funded by the National Nature Sciences Foundation of China (Grant No. ‎‎42250410321‎).‎

  2. Author contributions: A.C., Y.C., A.K.S.S.: data collection, data curation, formal analysis, investigation, software, validation, writing–original draft; Y.A.N.: analysis tools, validation, supervision, writing–review and editing.

  3. Conflict of interest: The authors declare that they have no conflicts of interest to report regarding the ‎present study.‎

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Received: 2024-04-14
Revised: 2024-10-17
Accepted: 2024-10-20
Published Online: 2024-11-28

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

Articles in the same Issue

  1. Regular Articles
  2. Theoretical magnetotelluric response of stratiform earth consisting of alternative homogeneous and transitional layers
  3. The research of common drought indexes for the application to the drought monitoring in the region of Jin Sha river
  4. Evolutionary game analysis of government, businesses, and consumers in high-standard farmland low-carbon construction
  5. On the use of low-frequency passive seismic as a direct hydrocarbon indicator: A case study at Banyubang oil field, Indonesia
  6. Water transportation planning in connection with extreme weather conditions; case study – Port of Novi Sad, Serbia
  7. Zircon U–Pb ages of the Paleozoic volcaniclastic strata in the Junggar Basin, NW China
  8. Monitoring of mangrove forests vegetation based on optical versus microwave data: A case study western coast of Saudi Arabia
  9. Microfacies analysis of marine shale: A case study of the shales of the Wufeng–Longmaxi formation in the western Chongqing, Sichuan Basin, China
  10. Multisource remote sensing image fusion processing in plateau seismic region feature information extraction and application analysis – An example of the Menyuan Ms6.9 earthquake on January 8, 2022
  11. Identification of magnetic mineralogy and paleo-flow direction of the Miocene-quaternary volcanic products in the north of Lake Van, Eastern Turkey
  12. Impact of fully rotating steel casing bored pile on adjacent tunnels
  13. Adolescents’ consumption intentions toward leisure tourism in high-risk leisure environments in riverine areas
  14. Petrogenesis of Jurassic granitic rocks in South China Block: Implications for events related to subduction of Paleo-Pacific plate
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  26. Permian tectonic switch of the southern Central Asian Orogenic Belt: Constraints from magmatism in the southern Alxa region, NW China
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  30. Classification and logging identification of reservoir space near the upper Ordovician pinch-out line in Tahe Oilfield
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  137. Erratum
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  150. Urban geoheritage and degradation risk assessment of the Sokograd fortress (Sokobanja, Eastern Serbia)
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https://doi.org/10.1515/geo-2022-0730
Keywords for this article
slope stability; earth-slopes; rotational-type failure; AI algorithms; machine learning‎
Creative Commons
BY 4.0

Articles in the same Issue

  1. Regular Articles
  2. Theoretical magnetotelluric response of stratiform earth consisting of alternative homogeneous and transitional layers
  3. The research of common drought indexes for the application to the drought monitoring in the region of Jin Sha river
  4. Evolutionary game analysis of government, businesses, and consumers in high-standard farmland low-carbon construction
  5. On the use of low-frequency passive seismic as a direct hydrocarbon indicator: A case study at Banyubang oil field, Indonesia
  6. Water transportation planning in connection with extreme weather conditions; case study – Port of Novi Sad, Serbia
  7. Zircon U–Pb ages of the Paleozoic volcaniclastic strata in the Junggar Basin, NW China
  8. Monitoring of mangrove forests vegetation based on optical versus microwave data: A case study western coast of Saudi Arabia
  9. Microfacies analysis of marine shale: A case study of the shales of the Wufeng–Longmaxi formation in the western Chongqing, Sichuan Basin, China
  10. Multisource remote sensing image fusion processing in plateau seismic region feature information extraction and application analysis – An example of the Menyuan Ms6.9 earthquake on January 8, 2022
  11. Identification of magnetic mineralogy and paleo-flow direction of the Miocene-quaternary volcanic products in the north of Lake Van, Eastern Turkey
  12. Impact of fully rotating steel casing bored pile on adjacent tunnels
  13. Adolescents’ consumption intentions toward leisure tourism in high-risk leisure environments in riverine areas
  14. Petrogenesis of Jurassic granitic rocks in South China Block: Implications for events related to subduction of Paleo-Pacific plate
  15. Differences in urban daytime and night block vitality based on mobile phone signaling data: A case study of Kunming’s urban district
  16. Random forest and artificial neural network-based tsunami forests classification using data fusion of Sentinel-2 and Airbus Vision-1 satellites: A case study of Garhi Chandan, Pakistan
  17. Integrated geophysical approach for detection and size-geometry characterization of a multiscale karst system in carbonate units, semiarid Brazil
  18. Spatial and temporal changes in ecosystem services value and analysis of driving factors in the Yangtze River Delta Region
  19. Deep fault sliding rates for Ka-Ping block of Xinjiang based on repeating earthquakes
  20. Improved deep learning segmentation of outdoor point clouds with different sampling strategies and using intensities
  21. Platform margin belt structure and sedimentation characteristics of Changxing Formation reefs on both sides of the Kaijiang-Liangping trough, eastern Sichuan Basin, China
  22. Enhancing attapulgite and cement-modified loess for effective landfill lining: A study on seepage prevention and Cu/Pb ion adsorption
  23. Flood risk assessment, a case study in an arid environment of Southeast Morocco
  24. Lower limits of physical properties and classification evaluation criteria of the tight reservoir in the Ahe Formation in the Dibei Area of the Kuqa depression
  25. Evaluation of Viaducts’ contribution to road network accessibility in the Yunnan–Guizhou area based on the node deletion method
  26. Permian tectonic switch of the southern Central Asian Orogenic Belt: Constraints from magmatism in the southern Alxa region, NW China
  27. Element geochemical differences in lower Cambrian black shales with hydrothermal sedimentation in the Yangtze block, South China
  28. Three-dimensional finite-memory quasi-Newton inversion of the magnetotelluric based on unstructured grids
  29. Obliquity-paced summer monsoon from the Shilou red clay section on the eastern Chinese Loess Plateau
  30. Classification and logging identification of reservoir space near the upper Ordovician pinch-out line in Tahe Oilfield
  31. Ultra-deep channel sand body target recognition method based on improved deep learning under UAV cluster
  32. New formula to determine flyrock distance on sedimentary rocks with low strength
  33. Assessing the ecological security of tourism in Northeast China
  34. Effective reservoir identification and sweet spot prediction in Chang 8 Member tight oil reservoirs in Huanjiang area, Ordos Basin
  35. Detecting heterogeneity of spatial accessibility to sports facilities for adolescents at fine scale: A case study in Changsha, China
  36. Effects of freeze–thaw cycles on soil nutrients by soft rock and sand remodeling
  37. Vibration prediction with a method based on the absorption property of blast-induced seismic waves: A case study
  38. A new look at the geodynamic development of the Ediacaran–early Cambrian forearc basalts of the Tannuola-Khamsara Island Arc (Central Asia, Russia): Conclusions from geological, geochemical, and Nd-isotope data
  39. Spatio-temporal analysis of the driving factors of urban land use expansion in China: A study of the Yangtze River Delta region
  40. Selection of Euler deconvolution solutions using the enhanced horizontal gradient and stable vertical differentiation
  41. Phase change of the Ordovician hydrocarbon in the Tarim Basin: A case study from the Halahatang–Shunbei area
  42. Using interpretative structure model and analytical network process for optimum site selection of airport locations in Delta Egypt
  43. Geochemistry of magnetite from Fe-skarn deposits along the central Loei Fold Belt, Thailand
  44. Functional typology of settlements in the Srem region, Serbia
  45. Hunger Games Search for the elucidation of gravity anomalies with application to geothermal energy investigations and volcanic activity studies
  46. Addressing incomplete tile phenomena in image tiling: Introducing the grid six-intersection model
  47. Evaluation and control model for resilience of water resource building system based on fuzzy comprehensive evaluation method and its application
  48. MIF and AHP methods for delineation of groundwater potential zones using remote sensing and GIS techniques in Tirunelveli, Tenkasi District, India
  49. New database for the estimation of dynamic coefficient of friction of snow
  50. Measuring urban growth dynamics: A study in Hue city, Vietnam
  51. Comparative models of support-vector machine, multilayer perceptron, and decision tree ‎predication approaches for landslide ‎susceptibility analysis
  52. Experimental study on the influence of clay content on the shear strength of silty soil and mechanism analysis
  53. Geosite assessment as a contribution to the sustainable development of Babušnica, Serbia
  54. Using fuzzy analytical hierarchy process for road transportation services management based on remote sensing and GIS technology
  55. Accumulation mechanism of multi-type unconventional oil and gas reservoirs in Northern China: Taking Hari Sag of the Yin’e Basin as an example
  56. TOC prediction of source rocks based on the convolutional neural network and logging curves – A case study of Pinghu Formation in Xihu Sag
  57. A method for fast detection of wind farms from remote sensing images using deep learning and geospatial analysis
  58. Spatial distribution and driving factors of karst rocky desertification in Southwest China based on GIS and geodetector
  59. Physicochemical and mineralogical composition studies of clays from Share and Tshonga areas, Northern Bida Basin, Nigeria: Implications for Geophagia
  60. Geochemical sedimentary records of eutrophication and environmental change in Chaohu Lake, East China
  61. Research progress of freeze–thaw rock using bibliometric analysis
  62. Mixed irrigation affects the composition and diversity of the soil bacterial community
  63. Examining the swelling potential of cohesive soils with high plasticity according to their index properties using GIS
  64. Geological genesis and identification of high-porosity and low-permeability sandstones in the Cretaceous Bashkirchik Formation, northern Tarim Basin
  65. Usability of PPGIS tools exemplified by geodiscussion – a tool for public participation in shaping public space
  66. Efficient development technology of Upper Paleozoic Lower Shihezi tight sandstone gas reservoir in northeastern Ordos Basin
  67. Assessment of soil resources of agricultural landscapes in Turkestan region of the Republic of Kazakhstan based on agrochemical indexes
  68. Evaluating the impact of DEM interpolation algorithms on relief index for soil resource management
  69. Petrogenetic relationship between plutonic and subvolcanic rocks in the Jurassic Shuikoushan complex, South China
  70. A novel workflow for shale lithology identification – A case study in the Gulong Depression, Songliao Basin, China
  71. Characteristics and main controlling factors of dolomite reservoirs in Fei-3 Member of Feixianguan Formation of Lower Triassic, Puguang area
  72. Impact of high-speed railway network on county-level accessibility and economic linkage in Jiangxi Province, China: A spatio-temporal data analysis
  73. Estimation model of wild fractional vegetation cover based on RGB vegetation index and its application
  74. Lithofacies, petrography, and geochemistry of the Lamphun oceanic plate stratigraphy: As a record of the subduction history of Paleo-Tethys in Chiang Mai-Chiang Rai Suture Zone of Thailand
  75. Structural features and tectonic activity of the Weihe Fault, central China
  76. Application of the wavelet transform and Hilbert–Huang transform in stratigraphic sequence division of Jurassic Shaximiao Formation in Southwest Sichuan Basin
  77. Structural detachment influences the shale gas preservation in the Wufeng-Longmaxi Formation, Northern Guizhou Province
  78. Distribution law of Chang 7 Member tight oil in the western Ordos Basin based on geological, logging and numerical simulation techniques
  79. Evaluation of alteration in the geothermal province west of Cappadocia, Türkiye: Mineralogical, petrographical, geochemical, and remote sensing data
  80. Numerical modeling of site response at large strains with simplified nonlinear models: Application to Lotung seismic array
  81. Quantitative characterization of granite failure intensity under dynamic disturbance from energy standpoint
  82. Characteristics of debris flow dynamics and prediction of the hazardous area in Bangou Village, Yanqing District, Beijing, China
  83. Rockfall mapping and susceptibility evaluation based on UAV high-resolution imagery and support vector machine method
  84. Statistical comparison analysis of different real-time kinematic methods for the development of photogrammetric products: CORS-RTK, CORS-RTK + PPK, RTK-DRTK2, and RTK + DRTK2 + GCP
  85. Hydrogeological mapping of fracture networks using earth observation data to improve rainfall–runoff modeling in arid mountains, Saudi Arabia
  86. Petrography and geochemistry of pegmatite and leucogranite of Ntega-Marangara area, Burundi, in relation to rare metal mineralisation
  87. Prediction of formation fracture pressure based on reinforcement learning and XGBoost
  88. Hazard zonation for potential earthquake-induced landslide in the eastern East Kunlun fault zone
  89. Monitoring water infiltration in multiple layers of sandstone coal mining model with cracks using ERT
  90. Study of the patterns of ice lake variation and the factors influencing these changes in the western Nyingchi area
  91. Productive conservation at the landslide prone area under the threat of rapid land cover changes
  92. Sedimentary processes and patterns in deposits corresponding to freshwater lake-facies of hyperpycnal flow – An experimental study based on flume depositional simulations
  93. Study on time-dependent injectability evaluation of mudstone considering the self-healing effect
  94. Detection of objects with diverse geometric shapes in GPR images using deep-learning methods
  95. Behavior of trace metals in sedimentary cores from marine and lacustrine environments in Algeria
  96. Spatiotemporal variation pattern and spatial coupling relationship between NDVI and LST in Mu Us Sandy Land
  97. Formation mechanism and oil-bearing properties of gravity flow sand body of Chang 63 sub-member of Yanchang Formation in Huaqing area, Ordos Basin
  98. Diagenesis of marine-continental transitional shale from the Upper Permian Longtan Formation in southern Sichuan Basin, China
  99. Vertical high-velocity structures and seismic activity in western Shandong Rise, China: Case study inspired by double-difference seismic tomography
  100. Spatial coupling relationship between metamorphic core complex and gold deposits: Constraints from geophysical electromagnetics
  101. Disparities in the geospatial allocation of public facilities from the perspective of living circles
  102. Research on spatial correlation structure of war heritage based on field theory. A case study of Jinzhai County, China
  103. Formation mechanisms of Qiaoba-Zhongdu Danxia landforms in southwestern Sichuan Province, China
  104. Magnetic data interpretation: Implication for structure and hydrocarbon potentiality at Delta Wadi Diit, Southeastern Egypt
  105. Deeply buried clastic rock diagenesis evolution mechanism of Dongdaohaizi sag in the center of Junggar fault basin, Northwest China
  106. Application of LS-RAPID to simulate the motion of two contrasting landslides triggered by earthquakes
  107. The new insight of tectonic setting in Sunda–Banda transition zone using tomography seismic. Case study: 7.1 M deep earthquake 29 August 2023
  108. The critical role of c and φ in ensuring stability: A study on rockfill dams
  109. Evidence of late quaternary activity of the Weining-Shuicheng Fault in Guizhou, China
  110. Extreme hydroclimatic events and response of vegetation in the eastern QTP since 10 ka
  111. Spatial–temporal effect of sea–land gradient on landscape pattern and ecological risk in the coastal zone: A case study of Dalian City
  112. Study on the influence mechanism of land use on carbon storage under multiple scenarios: A case study of Wenzhou
  113. A new method for identifying reservoir fluid properties based on well logging data: A case study from PL block of Bohai Bay Basin, North China
  114. Comparison between thermal models across the Middle Magdalena Valley, Eastern Cordillera, and Eastern Llanos basins in Colombia
  115. Mineralogical and elemental analysis of Kazakh coals from three mines: Preliminary insights from mode of occurrence to environmental impacts
  116. Chlorite-induced porosity evolution in multi-source tight sandstone reservoirs: A case study of the Shaximiao Formation in western Sichuan Basin
  117. Predicting stability factors for rotational failures in earth slopes and embankments using artificial intelligence techniques
  118. Origin of Late Cretaceous A-type granitoids in South China: Response to the rollback and retreat of the Paleo-Pacific plate
  119. Modification of dolomitization on reservoir spaces in reef–shoal complex: A case study of Permian Changxing Formation, Sichuan Basin, SW China
  120. Geological characteristics of the Daduhe gold belt, western Sichuan, China: Implications for exploration
  121. Rock physics model for deep coal-bed methane reservoir based on equivalent medium theory: A case study of Carboniferous-Permian in Eastern Ordos Basin
  122. Enhancing the total-field magnetic anomaly using the normalized source strength
  123. Shear wave velocity profiling of Riyadh City, Saudi Arabia, utilizing the multi-channel analysis of surface waves method
  124. Effect of coal facies on pore structure heterogeneity of coal measures: Quantitative characterization and comparative study
  125. Inversion method of organic matter content of different types of soils in black soil area based on hyperspectral indices
  126. Detection of seepage zones in artificial levees: A case study at the Körös River, Hungary
  127. Tight sandstone fluid detection technology based on multi-wave seismic data
  128. Characteristics and control techniques of soft rock tunnel lining cracks in high geo-stress environments: Case study of Wushaoling tunnel group
  129. Influence of pore structure characteristics on the Permian Shan-1 reservoir in Longdong, Southwest Ordos Basin, China
  130. Study on sedimentary model of Shanxi Formation – Lower Shihezi Formation in Da 17 well area of Daniudi gas field, Ordos Basin
  131. Multi-scenario territorial spatial simulation and dynamic changes: A case study of Jilin Province in China from 1985 to 2030
  132. Review Articles
  133. Major ascidian species with negative impacts on bivalve aquaculture: Current knowledge and future research aims
  134. Prediction and assessment of meteorological drought in southwest China using long short-term memory model
  135. Communication
  136. Essential questions in earth and geosciences according to large language models
  137. Erratum
  138. Erratum to “Random forest and artificial neural network-based tsunami forests classification using data fusion of Sentinel-2 and Airbus Vision-1 satellites: A case study of Garhi Chandan, Pakistan”
  139. Special Issue: Natural Resources and Environmental Risks: Towards a Sustainable Future - Part I
  140. Spatial-temporal and trend analysis of traffic accidents in AP Vojvodina (North Serbia)
  141. Exploring environmental awareness, knowledge, and safety: A comparative study among students in Montenegro and North Macedonia
  142. Determinants influencing tourists’ willingness to visit Türkiye – Impact of earthquake hazards on Serbian visitors’ preferences
  143. Application of remote sensing in monitoring land degradation: A case study of Stanari municipality (Bosnia and Herzegovina)
  144. Optimizing agricultural land use: A GIS-based assessment of suitability in the Sana River Basin, Bosnia and Herzegovina
  145. Assessing risk-prone areas in the Kratovska Reka catchment (North Macedonia) by integrating advanced geospatial analytics and flash flood potential index
  146. Analysis of the intensity of erosive processes and state of vegetation cover in the zone of influence of the Kolubara Mining Basin
  147. GIS-based spatial modeling of landslide susceptibility using BWM-LSI: A case study – city of Smederevo (Serbia)
  148. Geospatial modeling of wildfire susceptibility on a national scale in Montenegro: A comparative evaluation of F-AHP and FR methodologies
  149. Geosite assessment as the first step for the development of canyoning activities in North Montenegro
  150. Urban geoheritage and degradation risk assessment of the Sokograd fortress (Sokobanja, Eastern Serbia)
  151. Multi-hazard modeling of erosion and landslide susceptibility at the national scale in the example of North Macedonia
  152. Understanding seismic hazard resilience in Montenegro: A qualitative analysis of community preparedness and response capabilities
  153. Forest soil CO2 emission in Quercus robur level II monitoring site
  154. Characterization of glomalin proteins in soil: A potential indicator of erosion intensity
  155. Power of Terroir: Case study of Grašac at the Fruška Gora wine region (North Serbia)
  156. Special Issue: Geospatial and Environmental Dynamics - Part I
  157. Qualitative insights into cultural heritage protection in Serbia: Addressing legal and institutional gaps for disaster risk resilience
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