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Agriculture and aquaculture land-use change prediction in five central coastal provinces of Vietnam using ANN, SVR, and SARIMA models

  • YuRen Wang and Nguyen Hong Giang EMAIL logo
Published/Copyright: December 31, 2022
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Open Geosciences
From the journal Open Geosciences Volume 14 Issue 1

Abstract

Vietnam’s economy with agriculture and aquaculture still account for roughly 26% of the country’s gross domestic product, and nearly 70% of the Vietnamese population lives in rural areas; therefore, agriculture and aquaculture land use play a crucial role in the development process of Vietnam. Rapidly increasing population and infrastructure in rural areas and industrial zones lead to these land-use changes. Hence, these land-use change predictions are crucial for local authorities and the local people to make land-resource funds and set up planning. This article suggests support vector regression (SVR), artificial neural network (ANN), and seasonal autoregressive integrated moving average (SARIMA) methods to predict land-use change. By comparing the three models, the results indicate that almost all of the SVR models improve the accurate performance more than ANN and SARIMA in Quangtri, ThuThienHue, Danang, and Quảngnam provinces. Furthermore, the ANN model indicates more accurate forecasting than the SVR and SARIMA models in Quan Binh province. The result may be support for the Ministry of Natural Resources and Environment to conduct the land-use inventory and upgrade agriculture and aquaculture land-use change maps every 5 years. Afterward, the Department of Natural Resources and Environment’s provinces use the estimating database and update it manually.

Keywords: agriculture land use; aquaculture land use; ANN; prediction; SARIMA; SVR

1 Introduction

Spatial distributions of each land use, associated food security, environmental circumstances, socioeconomic conditions, and institutional and cultural issues may all significantly impact the evolution of regional land-use patterns [1,2,3,4,5,6]. Hence, several land, atmospheric, and environmental science applications are interested in the spatial distribution of land use as agriculture, aquaculture, forest, and urban [7,8]. Urbanization in Vietnam has extended residential and industrial zones into previously open, low-populated areas that were originally natural places for aquaculture and agricultural land uses during the last three decades [9,10]. On the other hand, agriculture and aquaculture still account for roughly 26% of the gross domestic product, and nearly 70% of the Vietnamese population lives in rural areas. As a result, land plays a critical role in Vietnam’s growth [11,12].

Land-use change prediction methodologies include three main groups: Geographic Information System (GIS), statistical, and artificial intelligence (AI) methods [13,14,15,16,17,18,19,20,21,22]. Apart from the GIS method, the boundary between the statistical and artificial neural network (ANN) methods is quite complicated. The literature uses several techniques that consist of a statistical technique, namely, seasonal autoregressive integrated moving average (SARIMA), or ARIMA models [23,24], and two AI techniques, namely, ANN and support vector machines [25,26,27,28,29,30]. Recently, many studies have applied machine learning to analyze land-use changes, as shown below. Wang et al. [28] used various kernels to forecast the correlations between cultivated land area and impact land-use change factors, including radial basis function, polynomial, and sigmoid kernels. The result indicated that it could be best used to estimate the cultivated land areas. Jiang et al. [31] used three models, named SARIMA, dynamic harmonics regression (DHR), and seasonal-trend decomposition procedure based on loss (STL) to simulate the moderate resolution imaging spectroradiometer leaf area index (LAI) from 2001 to 2006 and predict short-term LAI values in the future (2007). The study result showed that the SARIMA model was better at estimating R 2 values in all land cover types than the DHR or STL models. Li et al. [32] employed three different models of the previous 5 years’ weekly average, seasonal decomposition (SD) and Box–Jenkins multiplicative SARIMA models to predict the annual cycle of the weekly soil dryness index (SDI) series in the southwest of Western Australia. The result showed that the SARIMA model outstripped the other models in estimating the annual cycle of SDI data. Kong et al. [33] used GIS and ANN methodologies to assess geo-environmental suitability for agricultural land use in Hangzhou, China’s rural–urban periphery. The result showed the two models successfully criticized agricultural land-use suitability based on the geomorphology, soil conditions, basic infrastructure, and geological environment. Zhang and Chen [34] evaluated the suitability of urban land use in Hefei using the ANN model. The research suggests that the model might be used to assess the feasibility of urban land use. Hence, these support vector regression (SVR), ANN, and SARIMA models are suitably deployed for land-use change prediction in Vietnam’s five central coastal provinces (Quangbinh, Quangtri, ThuaThienHue, Danang, and Quang Nam).

The study aims to describe the forecast of agriculture and aquaculture land-use change using SVR, ANN, and SARIMA algorithms. The models’ input vectors are based on 44 quarters of land usage across 11 years in five central coastal provinces in Vietnam (Quangbinh, Quangtri, ThuaThienHue, Danang, and Quang Nam). This study also highlights a comparison among SVR, ANN, and SARIMA that is based on the results of statistical accuracy parameters such as mean (M), root mean square error (RMSE), mean absolute error (MAE), standard deviation (St Dev), Pearson correlation coefficient (R), kurtosis coefficient (Kurt), skewness coefficient (Skew), minimum (Min), maximum (Max), and correlation of determination (R 2). The results of these three models may show the working efficiency of the models for land-use prediction. In addition, by projecting these types of land-use changes, future scenarios that can aid land-use planning and decision-making can be developed.

The following is the content of the article. The first section of the article is an introduction. The SVR, ANN, and SARIMA models are introduced in Section 2 and explained in detail for use throughout this study. The study findings and discussions are explained in Sections 3 and 4. Finally, the conclusions are presented in Section 5.

2 Study area and methodology

2.1 Study area

Quangbinh, Quangtri, ThuaThienHue, Danang, and Quảngnam provinces are located in the central coastal area of Vietnam (see Figure 1). This area’s topography has a lower elevation, starting from the mountainous range down to the midland hills, to the plains inside the coastal dunes, and finally to the coastal islands. The delta area has a coastal-mountainous nature, and it is divided by mountain branches close to the sea as Hoanh Son mountain range – Ngang pass, Bach Ma mountain range – Hai Van pass [35]. These provinces’ total land area is 2,950,669 ha, in which, Quangbinh, Quangtri, ThuaThienHue, Danang, and Quảngnam occupy 799,876, 470,123, 494,711, 128,473, and 1,057,486 ha, respectively.

Figure 1 Five provinces’ locations.
Figure 1

Five provinces’ locations.

2.2 ANN model

An ANN model proposed in this study was created using Keras Neural Network, a highly modular neural network library. TensorFlow or Theano may be chosen as the backend [36]. Hence, Keras also contains plentiful modules that include activation function, layer, preprocessing, objective function, optimization method selection, etc. Deploying these modules indicates that network models may be easily set up, and core parameters of neural networks may be better enhanced [37]. The structure of the Keras Neural Network consists of a three-layer neuronal architecture as an input layer, hidden layer, and output layer [38]. The hidden layer may contain one or multilayers. The backpropagation learning algorithm of Levenberg–Marquardt has been used for the data feature extraction contained in the model’s inputs [39,40]. Figure 2 shows a structural view of the ANN model, as the below-input layer has m input nodes from i 1 to i m , one neuron of the output layer. There are n hidden layers as the first hidden layer contains neurons from H 11 to H 1m , the second one is from H 21 to H 2m , and the last one is from H n1 to H nm . Each neuron has a resemble Weight and Bias in the hidden layer, and the output layer, such as W 11 ( 2 ) , B 1 ( 1 ) and W n 1 ( n + 1 ) , B m ( n ) are the Weight and Bias to communicate with neuron H 11, and neuron H nm , respectively. The Sigmoid or Linear functions are commonly utilized equations for the activation functions for the hidden layers [41,42].

Figure 2 The architecture of ANN for land-use prediction.
Figure 2

The architecture of ANN for land-use prediction.

2.3 SVR model

Regarding a set of training data {(x 1 , y 1), (x 2 , y 2)… (x n , y n )}, where x i, y i, and n are an input, a target output, and a number of samples, respectively. Assuming SVR is to demonstrate a function of f(x) that may estimate accurate future values [43], and it is supposed by:

(1) f ( x ) = ∑ i = 1 n ( α i ⁎ − α i ) K ( x i ⋅ x ) + c ,

where K is the kernel function (named linear, sigmoid, radial basis, and polynomial functions), which nonlinearly maps the input data into higher feature space, c is the bias, α* and α are the Lagrange multipliers that are optimized by a maximum of the quadratic function as follows:

(2) W ( α i ⁎ , α i ) = − 1 2 ∑ i , j = 1 n ( α i ⁎ − α i ) ( α j − α j ⁎ ) K ( x i , x j ) + ∑ i = 1 n y i ( α i − α i ⁎ ) + ε ∑ i = 1 m y i ( α i − α i ⁎ ) ,

s . t . ∑ i = 1 n ( α i − α i ⁎ ) α i , α i ⁎ ∈ [ 0 , C ] ,

where C is called the penalty indicator, which commands the sanctioned degree on samples whose errors are beyond ε. Normally, only certain parts of the samples may suit the property: α i ⁎ − α i ≠ 0 , they are named support vectors since they only contribute to the model.

The kernel-based SVR is a powerful tool for solving regression problems. First, the kernel is chosen in a data-dependent way. Then, the applied kernel is a commonly linear function. They then describe different mappings from lower space to higher feature space [44,45].

2.4 SARIMA model

If the time series X t is considered as the SARIMA (p, d, q) × (P, D, Q, s) model with (p, d, q) nonseasonal order terms, (P, D, Q) seasonal order terms have the structure as follows [46,47,48]:

(3) ∅ p ( B ) Φ P ( B s ) W t = θ q ( B ) Θ Q ( B s ) ω t ,

where p and seasonal P denote several autoregressive terms (lags of the stationary series); d and seasonal D denote differencing that must be done to stationary series, q and seasonal Q demonstrate a number of moving average terms (lags of the forecast errors), s demonstrates the seasonal length in the data, ω t and B denote the backward shift operator and the white noise value at time t.

Equation (3) may be rewritten mathematically after replacing the value of W t = ∇ d ( B ) ∇ s D ( B ) X t .

(4) ∅ p ( B ) Φ P ( B s ) ( 1 − B ) d ( 1 − B s ) D X t = θ q ( B ) Θ Q ( B s ) ω t .

The SARIMA components may be rewritten as follows:

(5) Non seasonal auto regression ( A .R . ) : ∅ p ( B ) = 1 −   ∅ 1 B −   ∅ 2 B 2 −   ∅ 3 B 3 − … −   ∅ p B p ,

(6) Non seasonal moving average ( M .A . ) : θ q ( B ) = 1 − θ q B − θ q B 2 − θ q B 3 − … −  θ q B q ,

(7) Seasonal auto regression ( A .R . ) : Φ P ( B s ) = 1 − Φ 1 B s − Φ 2 B 2 s − Φ 3 B 3 s − … − Φ P B P s ,

(8) Seasonal moving average ( M .A . ) : Θ Q ( B s ) = 1 − Θ 1 B s − Θ Q B 2 s − Θ Q B 3 s − … − Θ Q B Q s ,

and

(9) ( B ) X t = X t − s ,

(10) ∇ s X t = ∇ s ( B ) X t = ( 1 −  B s ) X t = X t − B s X t = X t − X t − s ,

(11) ∇ d ( B ) X t = ( 1 − B ) d X t ,

(12) ∇ s D ( B ) X t = ( 1 − B s ) d X t .

The d and D point out the order of the nonseasonal and seasonal differencing, and their values are usually less than 1 and 2 totals of seasonal difference, respectively (i.e., 0 ≤ d; D ≤ 1) [49]. Fitting the data of the SARIMA model begins with selecting the normal and periodic differencing schemes. Auto-correlation function selects the best model orders, partial auto-correlation function, and Akaike’s information criteria [50,51,52].

2.5 Performance metrics

Predicting outputs rely most on estimating and comparing the actual values to the predicted values. These indicators of the accuracy measurement parameters include the MAE, RMSE, R 2, and R. In addition, the error metrics are defined as follows [53,54,55]:

(13) MAE  = ∑ t = 1 n ∣ x t − x t ′ ∣ n ,

(14) RMSE = ∑ t = 1 n ( x t − x t ′ ) 2 n ,

(15) R 2 = 1 − ∑ t = 1 n ( x t − x t ′ ) 2 ∑ t = 1 n x t − 1 n ∑ t = 1 n x t 2 ,

(16) R = ∑ t = 1 n ( x t − x ¯ ) ( x t ′ − x ′ ¯ ) ∑ t = 1 n ( x t − x ¯ ) 2 ∑ t = 1 n ( x t ′ − x ′ ¯ ) 2 ,

where x t and x t ′ are estimated and observed outputs in the period time t, and n is the number of the observed values in the testing data. x ¯ and x ′ ¯   are the mean of the predicted and observed outputs. The R 2 and R should be approaching 1 to indicate strong model performance, and the MAE and RMSE should be as close to zero as possible.

The diagram describes the methodology of this study in Figure 3. First, the collected data on agriculture and aquaculture land uses are preprocessed and checked by statistical methods in the input layer. Next, the data is separated into first quarter to thirty-second quarter for the training phase, and from thirty-third quarter to forty-fourth quarter for the testing phase; simultaneously, the SVR, ANN, and SARIMA algorithms are applied to learn the training samples and choose the optimal network parameters in the process of the methods. Last, the three models’ implementations showed the basis functions and were compared using the accuracy measurement parameters, namely RMSE, MAE, R, and R 2, in the possible result stage and looking for the most fitted estimation model for the study in the output layer.

Figure 3 Diagram of the research steps used in this study.
Figure 3

Diagram of the research steps used in this study.

3 Results

3.1 Database collection

This study uses a database containing land-use change in 44 quarters from 2010 to 2020. Two input variables, agriculture (Agri) land use and aquaculture (Aqua) land use, were gathered from five provinces at the Department of Natural Resources and Environment (DONRE). The typical statistical outcomes are also highlighted in Table 1. The Mean, Min, Max, St Dev, Skew, and Kurt values were calculated from the recorded quarters per year. Five provinces’ input data patterns are chosen in two stages. The first section is utilized for the training phase, which lasted from the first quarter to the thirty-second quarter. The testing phase, which lasted from the thirty-third quarter to the forty-fourth quarter, is the second section. Moreover, this division supports the models to optimize the accuracy parameters, and the univariate time series data forecasting fits the SARIMA model.

Table 1

Descriptive statistics of land use in five provinces

Parameter Quangbinh Quangtri ThuaThienHue Danang Quảngnam
Agri Aqua Agri Aqua Agri Aqua Agri Aqua Agri Aqua
Kurtosis −0.73 −0.51 −0.62 −0.22 −0.61 −0.87 0.85 1.19 −0.42 −0.52
Skewness −1.23 −1.51 −1.64 −1.50 −1.66 0.07 −0.88 0.01 −1.71 −1.63
St Dev 4,631 263 14,931 150 4,418 81 605 33 46,793 102
Mean (ha) 87,133 3,157 109,946 2,851 65,561 6,012 7,222 147 173,836 3,582
Min (ha) 79,222 2,763 88,282 2,651 58,815 5,835 6,641 117 113,495 3,431
Max (ha) 91,858 3,468 122,583 3,058 69,258 6,133 8,295 211 220,104 3,692

3.2 Experimental results

Find the best ANN, SVR, and SARIMA models for the land use of five provinces after many tests. With the values of the fundamental parameters shown in Table 2, this investigation discovered the best model.

Table 2

Basic configuration of ANN, SVR, SARIMA models

Model Item Quangbinh Quangtri ThuaThienHue Danang Quảngnam
Agri config Aqua config Agri config Aqua config Agri config Aqua config Agri config Aqua config Agri config Aqua config
ANN Model type Seq Seq Seq Seq Seq Seq Seq Seq Seq Seq
Layer type Dense Dense Dense Dense Dense Dense Dense Dense Dense Dense
Number of inputs 32 32 32 32 32 32 32 32 32 32
Number of hidden 2 2 2 2 2 2 2 2 2 2
Hidden layer size 200/200 100/100 200/200 100/100 200/200 100/100 200/200 100/100 200/200 100/100
Number of output 1 1 1 1 1 1 1 1 1 1
Activation function Relu Relu Relu Relu Relu Relu Relu Relu Relu Relu
Optimizer control Adam Adam Adam Adam Adam Adam Adam Adam Adam Adam
Epoch 1,000 1,000 1,000 1,000 1,000 1,000 1,000 1,000 1,000 1,000
SVR Kernel Linear Linear Linear Linear Linear Linear Linear Linear Linear Linear
C 100 1,000 100 1,000 100 1,000 100 1,000 100 1,000
Gamma 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05
Epsilon 1.98 2.98 1.98 2.98 1.98 2.98 1.98 2.98 1.98 2.98
SARIMA p, d, q 0,1,0 0,1,0 0,1,0 0,1,0 0,1,0 0,1,0 0,1,0 0,1,0 0,1,0 0,1,0
P, D, Q, s 0,1,0,4 0,1,1,4 1,1,1,4 0,1,1,4 0,1,0,4 1,1,1,4 0,1,1,4 1,1,0,4 1,1,1,4 0,1,1,4
Akaike information criterion 682 461 777 411 700 332 558 302 866 391
Bayesian information criterion 681 464 782 415 701 337 561 305 863 395

Note: Seq, Sequential; config, configuration.

3.3 Analysis and comparison of simulation results among ANN, SVR, and SARIMA models

With regard to the obtained results from the data prediction for land use in Table 3 and Figure 4, it can be verified that the best forecasting models are in Quangtri province. The second-best models are ThuaThienHue province. Danang, Quangbinh, and Quảngnam provinces show the predicted reliabilities to gain third, fourth, and fifth positions, respectively. The specific forecast results for two types of land use in each location are as follows. The SVR models for agriculture land use (with R 2 = 0.95, MAE = 1.03 [ha], and RMSE = 1.03 [ha]) and aquaculture land use (with R 2 = 0.93, MAE = 4.87 [ha], and RMSE = 5.01 [ha]) supply a better fit than the other models for Quangtri. In the implementation of the other models, the hierarchy shows to consider the order of SVRAgriculture_land_use > ANNAgriculture_land_use > SARIMAAgriculture_land_use, and SVRAgriculture_land_use > ANNAquacultute_land_use > SARIMAAquaculture_land_use. The land-use prediction for ThuaThienHue province shows that the SVRAgricultute_land_use and SVRAquacultute_land_use models are the best forecasting accuracy, and the hierarchical order of other models are ANNAgriculture_land_use > SARIMAAquaculture_land_use and ANNAquacultute_land_use > SARIMAAquaculture_land_use. The forecasting land-use models for Quangbinh and Danang indicating hierarchical order are SVRAgricultute_land_use > ANNAgriculture_land_use > SARIMAAgriculture_land_use and SVRAquaculture_land_use > ANNAquacultute_land_use > SARIMAAquaculture_land_use for Quangbinh, and SVRAgricultute_land_use > ANNAgriculture_land_use > SARIMAAgriculture_land_use and SVRAquaculture_land_use > ANNAquacultute_land_use > SARIMAAquaculture_land_use for Danang, respectively. At the same time, the model of SARIMAAquaculture_land_use with MAE = 39.66 (ha), RMSE = 85.44 (ha), and R 2 = 0.67 for Quangbinh is also the worst prediction compared to others. The prediction result of land use in Quảngnam shows that the order of hierarchical models with the accuracy of performance is SVRAgricultute_land_use > ANNAgriculture_land_use > SARIMAAgriculture_land_use and SVRAquaculture_land_use > ANNAquacultute_land_use > SARIMAAquaculture_land_use. In addition, the forecasting of SARIMAAgriculture_land_use model for Quảngnam gives the lowest accurate level with MAE, RMSE, R 2, and R values are 480 (ha), 610 (ha), 0.61, and 0.75, respectively.

Table 3

Accuracy parameters for agriculture land-use and aquaculture land-use prediction using three models across five provinces

Province Parameter Agriculture land use Aquaculture land use
SVR SARIMA ANN SVR SARIMA ANN
Quangbinh MAE (ha) 3.41 85.75 36.97 3.87 39.66 13.40
RMSE (ha) 3.45 51.58 37.1 3.96 85.44 13.42
R 2 0.91 0.72 0.93 0.92 0.67 0.93
R 0.92 0.85 0.94 0.92 0.73 0.92
Quangtri MAE (ha) 1.03 293.94 67.16 4.87 10.41 9.91
RMSE (ha) 1.03 649.75 67.51 5.01 25.32 9.91
R 2 0.95 0.71 0.94 0.93 0.81 0.93
R 0.94 0.68 0.92 0.94 0.89 0.94
ThuaThienHue MAE (ha) 1.73 61.64 43.95 3.44 9.39 10.12
RMSE (ha) 1.72 166.84 43.94 3.63 25.01 15.29
R 2 0.94 0.72 0.92 0.94 0.87 0.88
R 0.95 0.68 0.93 0.92 0.84 0.91
Danang MAE (ha) 3.01 178.31 42.23 8.75 9.01 12.52
RMSE (ha) 3.09 502.31 45.35 10.43 21.45 13.8
R 2 0.93 0.82 0.94 0.86 0.83 0.84
R 0.94 0.73 0.95 0.89 0.79 0.81
Quangnam MAE (ha) 1.35 480 44.03 2.31 9.21 13.08
RMSE (ha) 1.38 610 45.15 2.45 21.27 13.11
R 2 0.94 0.61 0.91 0.93 0.72 0.91
R 0.94 0.75 0.93 0.94 0.81 0.87

Furthermore, the forecasting implementation of the models is also visualized and considered by the Taylor diagram. Taylor's diagram summarizes the St Dev and correlation coefficient (CC) that are comprised concomitantly in the assessment of the respective model [56,57]. The St Dev and CC between the observed and predicted datasets for all the land-use models of the provinces are described in the Taylor diagram, as shown in Figure 5. It may be observed for ANN, SVR, and SARIMA models in Quangbinh (CCSVR_agricuture_land_use = 0.92, CCSARIMA_agriculture_land_use = 0.85, CCANN_agriculture_land_use = 0.94, CCSVR_aquacuture_land_use = 0.92, CCSARIMA_aquaculture_land_use = 0.79, and CCANN_aquaculture_land_use = 0.92), in Quangtri (CCSVR_agricuture_land_use = 0.94, CCSARIMA_agriculture_land_use = 0.68, CCANN_agriculture_land_use = 0.92, CCSVR_aquacuture_land_use = 0.94, CCSARIMA_aquaculture_land_use = 0.89, and CCANN_aquaculture_land_use = 0.94), in ThuaThienHue (CCSVR_agricuture_land_use = 0.95, CCSARIMA_agriculture_land_use = 0.68, CCANN_agriculture_land_use = 0.93, CCSVR_aquacuture_land_use = 0.92, CCSARIMA_aquaculture_land_use = 0.84, and CCANN_aquaculture_land_use = 0.91), in Danang (CCSVR_agriculture_land_use = 0.94, CCSARIMA_agriculture_land_use = 0.73, CCANN_agriculture_land_use = 0.95, CCSVR_aquaculture_land_use = 0.89, CCSARIMA_aquaculture_land_use = 0.73, and CCANN_aquaculture_land_use = 0.81), and in Quảngnam (CCSVR_agriculture_land_use = 0.94, CCSARIMA_agriculture_land_use = 0.75, CCANN_agriculture_land_use = 0.93, CCSVR_aquaculture_land_use = 0.94, CCSARIMA_aquaculture_land_use = 0.81, and CCANN_aquaculture_land_use = 0.87). This diagram demonstrates that these models were optimal accuracies of almost all models’ outcomes were significantly closer to 1. Moreover, it can result in overestimation when the SD of the observation values is lower than the SD of estimation values and vice versa. On the other hand, the SARIMA model for agriculture land-use prediction with CCSARIMA_agriculture_land_use = 0.68 in Quangtri, CCSARIMA_agriculture_land_use = 0.68 in ThuaThienHue point out that the level of gaining accuracy is only above medium; at the same time, the lowest estimations for agriculture and aquaculture land-use changes are SARIMAAgriculture_land_use model with CC for Quảngnam, and model of SARIMAAquaculture_land_use for Quangbinh, respectively.

4 Discussions

The central coastal area has a slower socioeconomic development rate than that of Hanoi and Ho Chi Minh cities. Therefore, agricultural land use and aquatic land use in agriculture and aquaculture are still crucial sources of income for the people living in the region. The urbanization and industrialization process gradually narrow down the land-use squares in recent years. Therefore, the assessment, analysis, and forecasting of land use are very important for local authorities and stakeholders.

This study used SVR, SARIMA, and ANN models for agriculture and aquaculture land-use change in the Central Coast Region of Vietnam to assess the impact of urbanization speed and industrialization processing on the two types of land use. Three algorithms showed highly accurate results for estimating the land-use change, in which the SVR and ANN models indicated more accuracy for Quangbinh, Quangtri, ThuaThienHue, Danang, and Quảngnam, respectively, compared with the SARIMA model. In addition, the predicted values of land-use changes created by the SARIMA model also supply acceptable results.

In order to evaluate the level of predicted accuracy of these results, this study is compared with other studies based on the accurate parameters. Jamali (2019) [58] used ANN to predict land-use/land-cover mapping using Landsat 8 O.L.I. in the northern region of Iran. The RMSE and MAE values for the testing phase were 6.0 and 6.0, respectively, and the values of the two parameters are lower than this study’s results for ANN models. Talukdar et al. [59] employed ANN and SVR models to predict the land-use land-cover classification of the river Ganga from Rajmahal to Farakka barrage in India. The RMSE values of the two models were 0.09 and 0.11 for ANN and SVR, respectively. The parameter values are lower than this study’s results, in which the lowest values of accurate parameters are 9.91 and 1.03 for ANN and SVR models. Soltani et al. [60] deployed the SARIMA model to estimate lake surface area in the Tashk-Bakhtegan lakes, Iran. The simulation result showed that the RMSE = 70.217, MAE = 49.425, and the indicator values are equivalent to this study result.

Although classification accuracies for land use are still important for the provinces, these results may support the provinces’ authorities and other stakeholders in decision-making and planning regarding two types of land use. The result can help the Ministry of Natural Resources and Environment to carry out the land-uses inventory and upgrade agriculture and aquaculture land-use change maps every 5 years. At the same time, the DONREs use the predicting database and update the kinds of land-use change manually. Moreover, evaluating the land-uses change will give policymakers regarding labor reorganization in the agriculture and aquaculture fields, also changing production methodology.

5 Conclusions

In this study, the SVR, SARIMA, and ANN models are presented to forecasting land-use change in Quangbinh, Quangtri, ThuaThienHue, Danang, and Quảngnam provinces of Vietnam. The observed data consisting of agriculture and aquaculture land use were collected from the start of 2010 to the end of 2020 for prediction. The data is also divided into two phases as the training and testing phases occupied from first quarter to thirty-second quarter and from thirty-third quarter to forty-fourth quarter, respectively. The studied and observed values are compared using four widely used statistical indicators (i.e., RMSE, MAE, R, and R 2). The study results show that almost SVR models improve the accurate performance more than ANN and SARIMA in Quangtri, ThuaThienHue, Danang, and Quảngnam provinces. The ANN model indicates more accurate forecasting implementation than the SVR and SARIMA models in Quangbinh province. Hence, the SVR and ANN models may be successfully precited for better land-use change. Furthermore, predicting land-use change can support land planning and decision-making for the local authorities.

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Acknowledgments

We are thankful to the Department of Civil Engineering at the National Kaohsiung University of Science and Technology, Taiwan, and Thu Dau Mot University in Vietnam.

  1. Author contributions: Conceptualization, material and methods, writing, original draft preparation: Nguyen Hong Giang; writing, review, editing: YuRen Wang.

  2. Conflict of interest: The authors declare no conflict of interest.

  3. Data availability statement: The data used to support the findings of this study are available from the corresponding author upon request.

Reference

[1] Verburg PH, Soepboer W, Veldkamp A, Limpiada R, Espaldon V, Mastura SS. Modeling the spatial dynamics of regional land use: the CLUE-S model. Environ Manag. 2002;30:391–405.10.1007/s00267-002-2630-xSearch in Google Scholar PubMed

[2] Tayyebi AH, Delavar MR, Tayyebi A, Golobi M. Combining multi criteria decision making and Dempster Shafer theory for landfill site selection. Int Arch Photogramm Remote Sens Spat Inf Sci. 2010;38:1073–8.Search in Google Scholar

[3] Geist H, McConnell W, Lambin EF, Moran E, Alves D, Rudel T. Causes and trajectories of land-use/cover change. Land-use and land-cover change. Berlin, Heidelberg: Springer; 2006. p. 41–70.10.1007/3-540-32202-7_3Search in Google Scholar

[4] Godfray HCJ, Beddington JR, Crute IR, Haddad L, Lawrence D, Muir JF, et al. Food security: the challenge of feeding 9 billion people. Science. 2010;327(5967):812–8.10.1126/science.1185383Search in Google Scholar PubMed

[5] Li MW, Wang YT, Geng J, Hong WC. Chaos cloud quantum bat hybrid optimization algorithm. Nonlinear Dyn. 2021;103(1):1167–93.10.1007/s11071-020-06111-6Search in Google Scholar

[6] Veldkamp A, Fresco LO. Exploring land use scenarios, an alternative approach based on actual land use. Agric Syst. 1997;55(1):1–17.10.1016/S0308-521X(95)00079-KSearch in Google Scholar

[7] National Research Council, & Climate Research Committee. Radiative forcing of climate change: Expanding the concept and addressing uncertainties. National Academies Press; 2005.Search in Google Scholar

[8] Board SS, National Research Council. Earth science and applications from space: national imperatives for the next decade and beyond. Washington DC, USA: National Academies Press; 2007.Search in Google Scholar

[9] Grădinaru SR, Fan P, Iojă CI, Niță MR, Suditu B, Hersperger AM. Impact of national policies on patterns of built-up development: an assessment over three decades. Land Use Policy. 2020;94:104510.10.1016/j.landusepol.2020.104510Search in Google Scholar

[10] Fan P, Ouyang Z, Nguyen DD, Nguyen TTH, Park H, Chen J. Urbanization, economic development, environmental and social changes in transitional economies: Vietnam after Doimoi. Landsc Urban Plan. 2019;187:145–55.10.1016/j.landurbplan.2018.10.014Search in Google Scholar

[11] Rutten M, Van Dijk M, Van Rooij W, Hilderink H. Land use dynamics, climate change, and food security in Vietnam: a global-to-local modeling approach. World Dev. 2014;59:29–46.10.1016/j.worlddev.2014.01.020Search in Google Scholar

[12] Khanh Nguyen HT, Nang Thu TT, Lebailly P, Azadi H. Economic challenges of the export-oriented aquaculture sector in Vietnam. J Appl Aquaculture. 2019;31(4):367–83.10.1080/10454438.2019.1576568Search in Google Scholar

[13] Wang SW, Gebru BM, Lamchin M, Kayastha RB, Lee WK. Land use and land cover change detection and prediction in the Kathmandu district of Nepal using remote sensing and G.I.S. Sustainability. 2020;12(9):3925.10.3390/su12093925Search in Google Scholar

[14] Liping C, Yujun S, Saeed S. Monitoring and predicting land use and land cover changes using remote sensing and G.I.S. techniques – A case study of a hilly area, Jiangle, China. PLoS One. 2018;13:e0200493.10.1371/journal.pone.0200493Search in Google Scholar PubMed PubMed Central

[15] Saputra MH, Lee HS. Prediction of land use and land cover changes for north Sumatra, Indonesia, using an artificial neural network based cellular automaton. Sustainability. 2019;11(11):3024.10.3390/su11113024Search in Google Scholar

[16] Veldkamp A, Lambin EF. Predicting land-use change. Agriculture, ecosystems & environment. 2001;85(1–3):1–6.10.1016/S0167-8809(01)00199-2Search in Google Scholar

[17] Viney NR, BormANN H, Breuer L, Bronstert A, Croke BFW, Frede H, et al. Assessing the impact of land use change on hydrology by ensemble modelling (L.U.C.H.E.M.) II: Ensemble combinations and predictions. Adv Water Resour. 2009;32(2):147–58.10.1016/j.advwatres.2008.05.006Search in Google Scholar

[18] Su L, Zhu JH, Wang W, Liu M. Application of A.R.M.A. Model in prediction of land use demand take Farmland in Jin-Hu Coastal Area as Example. Hunan Agric Sci. 2012;5:61–3Search in Google Scholar

[19] Zhang P, Ke Y, Zhang Z, Wang M, Li P, Zhang S. Urban land use and land cover classification using novel deep learning models based on high spatial resolution satellite imagery. Sensors. 2018;18(11):3717.10.3390/s18113717Search in Google Scholar PubMed PubMed Central

[20] Luus FP, Salmon BP, Van den Bergh F, Maharaj BTJ. Multiview deep learning for land-use classification. IEEE Geosci Remote Sens Lett. 2015;12(12):2448–52.10.1109/LGRS.2015.2483680Search in Google Scholar

[21] Mu L, Wang L, Wang Y, Chen X, Han W. Urban land use and land cover change prediction via self-adaptive cellular based deep learning with multisourced data. IEEE J Sel Top Appl Earth Obs Remote Sens. 2019;12(12):5233–47.10.1109/JSTARS.2019.2956318Search in Google Scholar

[22] Azad A, Wang X. Land use change ontology and traffic prediction through recurrent neural networks: A case study in Calgary, Canada. ISPRS Int J Geo-Information. 2021;10(6):358.10.3390/ijgi10060358Search in Google Scholar

[23] Malarvizhi K, Kumar SV, Porchelvan P. Urban sprawl modelling and prediction using regression and Seasonal A.R.I.M.A.: A case study for Vellore. India Model Earth Syst Environ. 2022;8(2):1597–615.10.1007/s40808-021-01170-zSearch in Google Scholar

[24] Rizeei HM, Pradhan B, Saharkhiz MA. Surface runoff prediction regarding L.U.L.C. and climate dynamics using coupled LTM, optimized A.R.I.M.A., and GIS-based SCS-CN models in tropical region. Arab J Geosci. 2018;11:1–16.10.1007/s12517-018-3397-6Search in Google Scholar

[25] Tayyebi A, Pijanowski BC. Modeling multiple land use changes using ANN, CART and MARS: Comparing tradeoffs in goodness of fit and explanatory power of data mining tools. Int J Appl Earth Obs Geoinf. 2014;28:102–16.10.1016/j.jag.2013.11.008Search in Google Scholar

[26] Bounouh O, Essid H, Farah IR. Prediction of land use/land cover change methods: A study. 2017 International Conference on Advanced Technologies for Signal and Image Processing (A.T.S.I.P.). Fez, Morocco: IEEE; 2017, May. p. 1–7.10.1109/ATSIP.2017.8075511Search in Google Scholar

[27] Lin C, Wang K, Wu D, Gong B. Passenger flow prediction based on land use around metro stations: A case study. Sustainability. 2020;12(17):6844.10.3390/su12176844Search in Google Scholar

[28] Wang X, Wang Z, Jin G, Yang J. Land reserve prediction using different kernel based support vector regression. Trans Chin Soc Agric Eng. 2014;30(4):204–11.Search in Google Scholar

[29] Huang B, Xie C, Tay R, Wu B. Land-use-change modeling using unbalanced support-vector machines. Environ Plan B: Plan Des. 2009;36:398–416.10.1068/b33047Search in Google Scholar

[30] Zhang Z, Hong WC. Application of variational mode decomposition and chaotic grey wolf optimizer with support vector regression for forecasting electric loads. Knowl Syst. 2021;228:107297.10.1016/j.knosys.2021.107297Search in Google Scholar

[31] Jiang B, Liang S, Wang J, Xiao Z. Modeling MODIS LAI time series using three statistical methods. Remote Sens Environ. 2010;114(7):1432–44.10.1016/j.rse.2010.01.026Search in Google Scholar

[32] Li Y, Campbell EP, Haswell D, Sneeuwjagt RJ, Venables WN. Statistical forecasting of soil dryness index in the southwest of Western Australia. For Ecol Manag. 2003;183(1–3):147–57.10.1016/S0378-1127(03)00103-8Search in Google Scholar

[33] Kong C, Lan H, Yang G, Xu K. Geo-environmental suitability assessment for agricultural land in the rural–urban fringe using BPNN and GIS: a case study of Hangzhou. Environ Earth Sci. 2016;75(15):1–14.10.1007/s12665-016-5956-zSearch in Google Scholar

[34] Zhang XR, Chen G. Application of neural network in urban land use suitability evaluation. Key Engineering Materials. Vol. 474. Chengdu, China: Trans Tech Publications Ltd; 2011. p. 681–6.10.4028/www.scientific.net/KEM.474-476.681Search in Google Scholar

[35] Nguyen TV, Tran TQ. Forestland and rural household livelihoods in the North Central Provinces, Vietnam. Land Use Policy. 2018;79:10–9.10.1016/j.landusepol.2018.07.046Search in Google Scholar

[36] Ang LI, Li YX, Li XH. Tensorflow and Keras-based convolutional neural network in cat image recognition. DEStech Transactions on Computer Science and Engineering, (CMSAM); 2017. p. 5.10.12783/dtcse/cmsam2017/16428Search in Google Scholar

[37] Jiang Z, Shen G. )Prediction of house price based on the back propagation neural network in the keras deep learning framework. 2019 6th International Conference on Systems and Informatics (I.C.S.A.I.). Shanghai, China: IEEE; 2019, November. p. 1408–12.10.1109/ICSAI48974.2019.9010071Search in Google Scholar

[38] Samarawickrama AJP, Fernando TGI. A recurrent neural network approach in predicting daily stock prices an application to the Sri Lankan stock market. 2017 IEEE International Conference on Industrial and Information Systems (I.C.I.I.S.). Peradeniya, Sri Lanka: IEEE; 2017, December. p. 1–6.10.1109/ICIINFS.2017.8300345Search in Google Scholar

[39] Lv C, Xing Y, Zhang J, Na X, Li Y, Liu T, et al. Levenberg–Marquardt backpropagation training of multilayer neural networks for state estimation of a safety-critical cyber-physical system. IEEE Trans Ind Inform. 2017;14:3436–46.10.1109/TII.2017.2777460Search in Google Scholar

[40] Du YC, Stephanus A. Levenberg-Marquardt neural network algorithm for degree of arteriovenous fistula stenosis classification using a dual optical photoplethysmography sensor. Sensors. 2018;18:2322.10.3390/s18072322Search in Google Scholar PubMed PubMed Central

[41] Sharma S, Sharma S. Activation functions in neural networks. Towards Data Sci. 2017;6(12):310–6.10.33564/IJEAST.2020.v04i12.054Search in Google Scholar

[42] Feng J, Lu S. Performance analysis of various activation functions in artificial neural networks. Journal of Physics: Conference Series. Vol. 1237, Issue 2. Xi’an, China: I.O.P. Publishing; 2019, June. p. 022030.10.1088/1742-6596/1237/2/022030Search in Google Scholar

[43] Xiang Y, Gou L, He L, Xia S, Wang W. A SVR–ANN combined model based on ensemble E.M.D. for rainfall prediction. Appl Soft Comput. 2018;73:874–83.10.1016/j.asoc.2018.09.018Search in Google Scholar

[44] Dash CSK, Sahoo P, Dehuri S, Cho SB. An empirical analysis of evolved radial basis function networks and support vector machines with mixture of kernels. Int J Artif Intell Tools. 2015;24(4):1550013.10.1142/s021821301550013xSearch in Google Scholar

[45] García-Floriano A, López-Martín C, Yáñez-Márquez C, Abran A. Support vector regression for predicting software enhancement effort. Inf Softw Technol. 2018;97:99–109.10.1016/j.infsof.2018.01.003Search in Google Scholar

[46] Kardakos EG, Alexiadis MC, Vagropoulos SI, Simoglou CK, Biskas PN, Bakirtzis AG. Application of time series and artificial neural network models in short-term forecasting of P.V. power generation. 2013 48th International Universities’ Power Engineering Conference (U.P.E.C.). Dublin, Ireland: IEEE; 2013, September. p. 1–6.10.1109/UPEC.2013.6714975Search in Google Scholar

[47] Mao Q, Zhang K, Yan W, Cheng C. Forecasting the incidence of tuberculosis in China using the seasonal auto-regressive integrated moving average (SARIMA) model. J Infect public health. 2018;11(5):707–12.10.1016/j.jiph.2018.04.009Search in Google Scholar PubMed PubMed Central

[48] Vagropoulos SI, Chouliaras GI, Kardakos EG, Simoglou CK, Bakirtzis AG. Comparison of SARIMAX., SARIMA, modified SARIMA and ANN-based models for short-term P.V. generation forecasting. 2016 IEEE International Energy Conference (ENERGYCON). Leuven, Belgium: IEEE; 2016, April. p. 1–6.10.1109/ENERGYCON.2016.7514029Search in Google Scholar

[49] Farsi M, Hosahalli D, Manjunatha BR, Gad I, Atlam ES, Ahmed A, et al. Parallel genetic algorithms for optimizing the SARIMA model for better forecasting of the NCDC weather data. Alex Eng J. 2021;60(1):1299–1316.10.1016/j.aej.2020.10.052Search in Google Scholar

[50] He X, Xu H, Sabetamal H, Sheng D. Machine learning aided stochastic reliability analysis of spatially variable slopes. Comput Geotech. 2020;126:103711.10.1016/j.compgeo.2020.103711Search in Google Scholar

[51] Maxwell O, Chinedu IU, Ifeanyi AC, Chinonso NC. On modeling murder crimes in Nigeria. Sci Rev. 2019;5:157–62.10.33552/SJRR.2019.02.000528Search in Google Scholar

[52] Li HL, Zheng RJ, Zheng Q, Jiang W, Zhang XL, Wang WM, et al. Predicting the number of visceral leishmaniasis cases in Kashgar, Xinjiang, China using the ARIMA-EGARCH model. Asian Pac J Trop Med. 2020;13(2):81.10.4103/1995-7645.275416Search in Google Scholar

[53] Kardani N, Zhou A, Nazem M, Shen SL. Estimation of bearing capacity of piles in cohesionless soil using optimised machine learning approaches. Geotech Geol Eng. 2020;38(2):2271–91.10.1007/s10706-019-01085-8Search in Google Scholar

[54] Touzani S, Granderson J, Fernandes S. Gradient boosting machine for modeling the energy consumption of commercial buildings. Energy Build. 2018;158:1533–43.10.1016/j.enbuild.2017.11.039Search in Google Scholar

[55] Yang JH, Yang MS. A control chart pattern recognition system using a statistical correlation coefficient method. Comput Ind Eng. 2005;48(2):205–21.10.1016/j.cie.2005.01.008Search in Google Scholar

[56] Taylor KE. Summarizing multiple aspects of model performance in a single diagram. J Geophys Res Atmos. 2001;106(D7):7183–92.10.1029/2000JD900719Search in Google Scholar

[57] Ghorbani MA, Deo RC, Yaseen ZM, Kashani MH, Mohammadi B. Pan evaporation prediction using a hybrid multilayer perceptron-firefly algorithm (MLP-FFA) model: case study in North Iran. Theor Appl Climatol. 2018;133:1119–31.10.1007/s00704-017-2244-0Search in Google Scholar

[58] Jamali A. Evaluation and comparison of eight machine learning models in land use/land cover mapping using Landsat 8 OLI: A case study of the northern region of Iran. SN Appl. Sci. 2019;1:1448.10.1007/s42452-019-1527-8Search in Google Scholar

[59] Talukdar S, Singha P, Mahato S, Pal S, Liou YA, Rahman A. Land-use land-cover classification by machine learning classifiers for satellite observations – A review. Remote Sens. 2020;12(7):1135.10.3390/rs12071135Search in Google Scholar

[60] Soltani K, Azari A, Zeynoddin M, Amiri A, Ebtehaj I, Ouarda TB, et al. Lake surface area forecasting using integrated satellite-SARIMA-long-short-term memory model. Res. Sq. 2021.10.21203/rs.3.rs-631247/v1Search in Google Scholar
Received: 2021-09-02
Revised: 2022-07-02
Accepted: 2022-10-05
Published Online: 2022-12-31

© 2022 the author(s), published by De Gruyter

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

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https://doi.org/10.1515/geo-2022-0428
Keywords for this article
agriculture land use; aquaculture land use; ANN; prediction; SARIMA; SVR
Creative Commons
BY 4.0

Articles in the same Issue

  1. Regular Articles
  2. Study on observation system of seismic forward prospecting in tunnel: A case on tailrace tunnel of Wudongde hydropower station
  3. The behaviour of stress variation in sandy soil
  4. Research on the current situation of rural tourism in southern Fujian in China after the COVID-19 epidemic
  5. Late Triassic–Early Jurassic paleogeomorphic characteristics and hydrocarbon potential of the Ordos Basin, China, a case of study of the Jiyuan area
  6. Application of X-ray fluorescence mapping in turbiditic sandstones, Huai Bo Khong Formation of Nam Pat Group, Thailand
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  12. Insights into origins of the natural gas in the Lower Paleozoic of Ordos basin, China
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  19. High-precision remote sensing mapping of aeolian sand landforms based on deep learning algorithms
  20. Experimental study on reservoir characteristics and oil-bearing properties of Chang 7 lacustrine oil shale in Yan’an area, China
  21. Estimating the volume of the 1978 Rissa quick clay landslide in Central Norway using historical aerial imagery
  22. Spatial accessibility between commercial and ecological spaces: A case study in Beijing, China
  23. Curve number estimation using rainfall and runoff data from five catchments in Sudan
  24. Urban green service equity in Xiamen based on network analysis and concentration degree of resources
  25. Spatio-temporal analysis of East Asian seismic zones based on multifractal theory
  26. Delineation of structural lineaments of Southeast Nigeria using high resolution aeromagnetic data
  27. 3D marine controlled-source electromagnetic modeling using an edge-based finite element method with a block Krylov iterative solver
  28. A comprehensive evaluation method for topographic correction model of remote sensing image based on entropy weight method
  29. Quantitative discrimination of the influences of climate change and human activity on rocky desertification based on a novel feature space model
  30. Assessment of climatic conditions for tourism in Xinjiang, China
  31. Attractiveness index of national marine parks: A study on national marine parks in coastal areas of East China Sea
  32. Effect of brackish water irrigation on the movement of water and salt in salinized soil
  33. Mapping paddy rice and rice phenology with Sentinel-1 SAR time series using a unified dynamic programming framework
  34. Analyzing the characteristics of land use distribution in typical village transects at Chinese Loess Plateau based on topographical factors
  35. Management status and policy direction of submerged marine debris for improvement of port environment in Korea
  36. Influence of Three Gorges Dam on earthquakes based on GRACE gravity field
  37. Comparative study of estimating the Curie point depth and heat flow using potential magnetic data
  38. The spatial prediction and optimization of production-living-ecological space based on Markov–PLUS model: A case study of Yunnan Province
  39. Major, trace and platinum-group element geochemistry of harzburgites and chromitites from Fuchuan, China, and its geological significance
  40. Vertical distribution of STN and STP in watershed of loess hilly region
  41. Hyperspectral denoising based on the principal component low-rank tensor decomposition
  42. Evaluation of fractures using conventional and FMI logs, and 3D seismic interpretation in continental tight sandstone reservoir
  43. U–Pb zircon dating of the Paleoproterozoic khondalite series in the northeastern Helanshan region and its geological significance
  44. Quantitatively determine the dominant driving factors of the spatial-temporal changes of vegetation-impacts of global change and human activity
  45. Can cultural tourism resources become a development feature helping rural areas to revitalize the local economy under the epidemic? An exploration of the perspective of attractiveness, satisfaction, and willingness by the revisit of Hakka cultural tourism
  46. A 3D empirical model of standard compaction curve for Thailand shales: Porosity in function of burial depth and geological time
  47. Attribution identification of terrestrial ecosystem evolution in the Yellow River Basin
  48. An intelligent approach for reservoir quality evaluation in tight sandstone reservoir using gradient boosting decision tree algorithm
  49. Detection of sub-surface fractures based on filtering, modeling, and interpreting aeromagnetic data in the Deng Deng – Garga Sarali area, Eastern Cameroon
  50. Influence of heterogeneity on fluid property variations in carbonate reservoirs with multistage hydrocarbon accumulation: A case study of the Khasib formation, Cretaceous, AB oilfield, southern Iraq
  51. Designing teaching materials with disaster maps and evaluating its effectiveness for primary students
  52. Assessment of the bender element sensors to measure seismic wave velocity of soils in the physical model
  53. Appropriated protection time and region for Qinghai–Tibet Plateau grassland
  54. Identification of high-temperature targets in remote sensing based on correspondence analysis
  55. Influence of differential diagenesis on pore evolution of the sandy conglomerate reservoir in different structural units: A case study of the Upper Permian Wutonggou Formation in eastern Junggar Basin, NW China
  56. Planting in ecologically solidified soil and its use
  57. National and regional-scale landslide indicators and indexes: Applications in Italy
  58. Occurrence of yttrium in the Zhijin phosphorus deposit in Guizhou Province, China
  59. The response of Chudao’s beach to typhoon “Lekima” (No. 1909)
  60. Soil wind erosion resistance analysis for soft rock and sand compound soil: A case study for the Mu Us Sandy Land, China
  61. Investigation into the pore structures and CH4 adsorption capacities of clay minerals in coal reservoirs in the Yangquan Mining District, North China
  62. Overview of eco-environmental impact of Xiaolangdi Water Conservancy Hub on the Yellow River
  63. Response of extreme precipitation to climatic warming in the Weihe river basin, China and its mechanism
  64. Analysis of land use change on urban landscape patterns in Northwest China: A case study of Xi’an city
  65. Optimization of interpolation parameters based on statistical experiment
  66. Late Cretaceous adakitic intrusive rocks in the Laimailang area, Gangdese batholith: Implications for the Neo-Tethyan Ocean subduction
  67. Tectonic evolution of the Eocene–Oligocene Lushi Basin in the eastern Qinling belt, Central China: Insights from paleomagnetic constraints
  68. Geographic and cartographic inconsistency factors among different cropland classification datasets: A field validation case in Cambodia
  69. Distribution of large- and medium-scale loess landslides induced by the Haiyuan Earthquake in 1920 based on field investigation and interpretation of satellite images
  70. Numerical simulation of impact and entrainment behaviors of debris flow by using SPH–DEM–FEM coupling method
  71. Study on the evaluation method and application of logging irreducible water saturation in tight sandstone reservoirs
  72. Geochemical characteristics and genesis of natural gas in the Upper Triassic Xujiahe Formation in the Sichuan Basin
  73. Wehrlite xenoliths and petrogenetic implications, Hosséré Do Guessa volcano, Adamawa plateau, Cameroon
  74. Changes in landscape pattern and ecological service value as land use evolves in the Manas River Basin
  75. Spatial structure-preserving and conflict-avoiding methods for point settlement selection
  76. Fission characteristics of heavy metal intrusion into rocks based on hydrolysis
  77. Sequence stratigraphic filling model of the Cretaceous in the western Tabei Uplift, Tarim Basin, NW China
  78. Fractal analysis of structural characteristics and prospecting of the Luanchuan polymetallic mining district, China
  79. Spatial and temporal variations of vegetation coverage and their driving factors following gully control and land consolidation in Loess Plateau, China
  80. Assessing the tourist potential of cultural–historical spatial units of Serbia using comparative application of AHP and mathematical method
  81. Urban black and odorous water body mapping from Gaofen-2 images
  82. Geochronology and geochemistry of Early Cretaceous granitic plutons in northern Great Xing’an Range, NE China, and implications for geodynamic setting
  83. Spatial planning concept for flood prevention in the Kedurus River watershed
  84. Geophysical exploration and geological appraisal of the Siah Diq porphyry Cu–Au prospect: A recent discovery in the Chagai volcano magmatic arc, SW Pakistan
  85. Possibility of using the DInSAR method in the development of vertical crustal movements with Sentinel-1 data
  86. Using modified inverse distance weight and principal component analysis for spatial interpolation of foundation settlement based on geodetic observations
  87. Geochemical properties and heavy metal contents of carbonaceous rocks in the Pliocene siliciclastic rock sequence from southeastern Denizli-Turkey
  88. Study on water regime assessment and prediction of stream flow based on an improved RVA
  89. A new method to explore the abnormal space of urban hidden dangers under epidemic outbreak and its prevention and control: A case study of Jinan City
  90. Milankovitch cycles and the astronomical time scale of the Zhujiang Formation in the Baiyun Sag, Pearl River Mouth Basin, China
  91. Shear strength and meso-pore characteristic of saturated compacted loess
  92. Key point extraction method for spatial objects in high-resolution remote sensing images based on multi-hot cross-entropy loss
  93. Identifying driving factors of the runoff coefficient based on the geographic detector model in the upper reaches of Huaihe River Basin
  94. Study on rainfall early warning model for Xiangmi Lake slope based on unsaturated soil mechanics
  95. Extraction of mineralized indicator minerals using ensemble learning model optimized by SSA based on hyperspectral image
  96. Lithofacies discrimination using seismic anisotropic attributes from logging data in Muglad Basin, South Sudan
  97. Three-dimensional modeling of loose layers based on stratum development law
  98. Occurrence, sources, and potential risk of polycyclic aromatic hydrocarbons in southern Xinjiang, China
  99. Attribution analysis of different driving forces on vegetation and streamflow variation in the Jialing River Basin, China
  100. Slope characteristics of urban construction land and its correlation with ground slope in China
  101. Limitations of the Yang’s breaking wave force formula and its improvement under a wider range of breaker conditions
  102. The spatial-temporal pattern evolution and influencing factors of county-scale tourism efficiency in Xinjiang, China
  103. Evaluation and analysis of observed soil temperature data over Northwest China
  104. Agriculture and aquaculture land-use change prediction in five central coastal provinces of Vietnam using ANN, SVR, and SARIMA models
  105. Leaf color attributes of urban colored-leaf plants
  106. Application of statistical and machine learning techniques for landslide susceptibility mapping in the Himalayan road corridors
  107. Sediment provenance in the Northern South China Sea since the Late Miocene
  108. Drones applications for smart cities: Monitoring palm trees and street lights
  109. Double rupture event in the Tianshan Mountains: A case study of the 2021 Mw 5.3 Baicheng earthquake, NW China
  110. Review Article
  111. Mobile phone indoor scene features recognition localization method based on semantic constraint of building map location anchor
  112. Technical Note
  113. Experimental analysis on creep mechanics of unsaturated soil based on empirical model
  114. Rapid Communications
  115. A protocol for canopy cover monitoring on forest restoration projects using low-cost drones
  116. Landscape tree species recognition using RedEdge-MX: Suitability analysis of two different texture extraction forms under MLC and RF supervision
  117. Special Issue: Geoethics 2022 - Part I
  118. Geomorphological and hydrological heritage of Mt. Stara Planina in SE Serbia: From river protection initiative to potential geotouristic destination
  119. Geotourism and geoethics as support for rural development in the Knjaževac municipality, Serbia
  120. Modeling spa destination choice for leveraging hydrogeothermal potentials in Serbia
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