Startseite Hybrid machine learning hydrological model for flood forecast purpose
Artikel Open Access

Hybrid machine learning hydrological model for flood forecast purpose

  • Guangyuan Kan EMAIL logo , Ke Liang EMAIL logo , Haijun Yu , Bowen Sun , Liuqian Ding , Jiren Li , Xiaoyan He und Chengji Shen
Veröffentlicht/Copyright: 18. September 2020
Artikel downloaden (PDF)
Veröffentlichen auch Sie bei De Gruyter Brill
Manuskript einreichen Informationen für Autor*innen
Open Geosciences
Aus der Zeitschrift Open Geosciences Band 12 Heft 1

Abstract

Machine learning-based data-driven models have achieved great success since their invention. Nowadays, the artificial neural network (ANN)-based machine learning methods have made great progress than ever before, such as the deep learning and reinforcement learning, etc. In this study, we coupled the ANN with the K-nearest neighbor method to propose a novel hybrid machine learning (HML) hydrological model for flood forecast purpose. The advantage of the proposed model over traditional neural network models is that it can predict discharge continuously without accuracy loss owed to its specially designed model structure. In order to overcome the local minimum issue of the traditional neural network training, a genetic algorithm and Levenberg–Marquardt-based multi-objective training method was also proposed. Real-world applications of the HML hydrological model indicated its satisfactory performance and reliable stability, which enlightened the possibility of further applications of the HML hydrological model in flood forecast problems.

Keywords: hybrid machine learning; hydrological model; flood forecast; artificial neural network; K-nearest neighbor method

1 Introduction

Machine learning is a bionics-based problem solution scheme which mimics the functionality of human brain [1,2,3,4,5,6,7,8,9,10,11,12,13]. It is usually constituted by a series of mathematic operations and transformations which are implemented by computer programs coupled with modern electronic computers. Among the multitude machine learning methods, the artificial neural networks (ANN) have been paid close attention to by many researchers and engineers for decades. Especially nowadays, with the further improvements of the back-propagation training algorithms and computing horsepower of the modern graphics processing unit (GPU), the ANN technology meets its second spring. Modern ANN technologies, such as the deep learning and the reinforcement learning, etc., have been successfully and widely applied in computer vision, text and speech recognition, self-driving cars, artificial intelligence, and even Go game [14,15,16].

Many researchers and scientists have tried to apply the ANN technology to solve flood forecast problems and have achieved meaningful results [17,18,19,20,21,22,23,24,25,26,27,28,29,30]. However, due to the high nonlinearity inherent in the flood forecast, the training and testing accuracies usually cannot be both satisfactory which indicate that the forecast capability and stability is not good enough. The poor results are mainly attributed to the following reasons: (a) the network topology and parameters cannot be optimized simultaneously and globally; (b) network with complex topology can satisfactorily simulate in training period, however, the testing results are usually not good. On the contrary, simple network cannot achieve good enough simulations in the training and testing periods. This means that compromise should be made between the network complexity and training accuracy; (c) network designers want to further improve the performance of ANN, however, they cannot achieve better performance by using ANN model alone.

In order to solve the above-mentioned three issues, we developed a HML hydrological model and apply it to a real-world hourly rainfall-runoff simulation application. The HML hydrological model is built by coupling ANN runoff estimation with a K-nearest neighbor (KNN) runoff error estimation module to further improve its performance. The advantage of the proposed model over traditional neural network models is that it can predict discharge continuously without accuracy loss owed to its specially designed model structure. In order to overcome the local minimum issue of the traditional neural network training, a novel genetic algorithm (GA) and Levenberg–Marquardt back-propagation (LMBP) algorithm-based multi-objective training method was also proposed to solve the training problems encountered in the ANN applications. Real-world application of the HML hydrological model indicated its satisfactory performance and reliable stability, which enlightened the possibility of the further application of the HML hydrological model in flood forecast problems.

2 Methods

2.1 Study area and data collection

The HML hydrological model was applied in the Tunxi watershed (locates in Anhui province of China) whose catchment area is 2697.76 km2. It locates adjacent to the southeast coast of China which is the subtropical monsoon climate zone. The annual mean temperature is 17°C. The winter prevails northwest wind with cold, dry, and sunny weather. The summer is full filled by southeast wind with high temperature, strong sunlight, and moist air. During spring and autumn seasons, the cyclone happens frequently and temperature usually changes between cold and hot. The frontal rain usually happens during spring and summer. The typhoon happens frequently in summer and fall. The monsoon circulation and the main mountain ranges direction obey the orthogonal interaction. The mountains block the northern cold wind and the typhoon. The Tunxi watershed goes downward from west to east. The maximum, minimum, and mean elevations are 1,398, 116, and 380 m, respectively. The relative elevation difference is large. The annual mean rainfall is 1,600 mm and uneven within the year and between different years. 50% of the floods often happen from April to June. 20% of the floods happen from July to September and drought also frequently happens during these months. The runoff changes largely within the year and between different years. The vegetation of the Tunxi watershed is good which mainly includes evergreen coniferous forest, deciduous broad-leaved forest, mixed forest, forest land, grassland, woodland, and pasture crops. The soil type is mainly clay loam.

The rain gauging station, river network, and DEM maps of the Tunxi watershed are demonstrated in Figure 1. The hourly rainfall–runoff simulation of the Tunxi watershed is carried based on 20 flood events for calibration and 9 flood events for validation. The flood events were selected from 1982 to 2002. Eleven rain gauging stations, including Wucheng, Shimen, Zuolong, Dalian, Tunxi, Shangxikou, Rucun, Yixian, Yanqian, Xiuning, and Liukou, are adopted for the calculation of areal mean rainfall.

Figure 1 The maps of the Tunxi watershed. (a) Rain gauging station and river network map. (b) DEM map.
Figure 1

The maps of the Tunxi watershed. (a) Rain gauging station and river network map. (b) DEM map.

2.2 Identification of the rainfall and antecedent runoff orders

The outlet discharge was simulated or forecasted by using the rainfall and antecedent runoff as model inputs. Rainfall is a key meteorological forcing to generate the runoff. The antecedent runoff functions like a proxy of the watershed soil moisture. With these two factors as model inputs, the discharge time series can be forecasted continuously.

The model inputs are rainfall values at present time step and previous time steps combined with the antecedent runoff values. How many previous time steps’ rainfall and antecedent runoff can be taken as model inputs should be identified and we named the identified number of time steps as the model order. In this research, the orders of rainfall and antecedent runoff are estimated by using the partial mutual information feature selection method [31,32,33].

2.3 ANN–KNN hybrid runoff and runoff error estimations

The HML hydrological model forecasts basin outlet discharge by using the rainfall and antecedent runoff. The modeling approach is as follows:

EQ(t)=FANNP(t),…,Pt−nP,AQt−1,…,AQt−nAQ
EE(t)=FKNNEQt,Pt,…,Pt−nP,AQt−1,…,AQt−nAQ
Qt=EQt+EEt

where EQ and EE denote estimated discharge and discharge error, respectively; FANN and FKNN denote ANN-based discharge estimation and KNN-based discharge error estimation, respectively; P and AQ denote rainfall and antecedent runoff, respectively; nP and nAQ denote orders of rainfall and antecedent runoff, respectively; Q denotes simulated discharge value; t denotes time step.

The simulation process of the HML hydrological model is as follows: At first, the EQ is estimated by the above equation. And then the EE is calculated. At last, the Q is computed by summing EQ and EE. By using the above-mentioned three equations, the discharge and discharge error are estimated and the final simulated value is obtained by summing these two estimated values together. There is one thing should be noted that the AQ used in the forecast process is not the observed antecedent runoff. We adopted simulated discharges at previous time steps as AQ for flood forecasting. This novel model structure is significantly different from traditional ANN flood forecasting models.

2.4 GA and LMBP-based multi-objective network topology and parameters optimizations

The ANN network topology and parameters are encoded by real numbers as described by Kan et al. [18,19]. These encoded real numbers are treated as decision variables. Therefore, the network topology and parameters can be optimized simultaneously and globally by evolving the decision variables using multi-objective GA (NSGA-II) algorithm. After decoding the evolved decision variables into a set of neural networks, we use the LMBP algorithm to further train the neural networks’ parameters to obtain the final optimal neural networks. The objective functions of the multi-objective optimization are training error and network complexity [18,19]. At last, we choose the network corresponding to the best training error as the final accepted neural network. After the neural network is obtained, the KNN algorithm is calibrated by using the leave-one-out cross-validation method [18].

3 Results and discussion

3.1 Runoff scatter plot analysis

The scatter plots of the observed and simulated discharges of the HML hydrological model are demonstrated in Figure 2. As we can see from Figure 2(a) and (b), the calibration and validation results are satisfactorily good. The regression R2 values of the calibration and validation periods are 0.9635 and 0.9515, respectively. The scatter plot of the calibration period demonstrates a good and even distribution. The scatter plot of the validation period is a bit worse than the calibration period. The scatters are dispersed and not concentrated along with the 45° line. The regression R2 value of the validation period (0.9515) is also worse than the calibration period (0.9635). Although the simulation result of the validation period is not as good as the calibration period, the HML hydrological model’s performance in validation period is also good enough. The accuracy decrement ratio from the calibration period to the validation period is 0.9515/0.9635 ≈ 0.9875 which is acceptable for real-world applications. It also can be found in Figure 2(b) that the simulation results of validation period are biased (underestimated). This phenomenon will be further discussed in the following error statistics sections.

Figure 2 Scatter plots of observed and simulated discharges. (a) Calibration period. (b) Validation period.
Figure 2

Scatter plots of observed and simulated discharges. (a) Calibration period. (b) Validation period.

3.2 Hydrograph analysis

The hydrographs simulated by the HML hydrological model are accurate and smooth. The satisfactory simulation property of the HML hydrological model is owed to the modeling approach and the proposed model calibration method. The good accuracy of the simulation results is owed to the multistep rainfall input variables. With continuous rainfall inputs, the water balance can be ensured. The ANN–KNN hybrid data-driven method can make full use of rainfall input information and simulates the flood accurately. Therefore, the simulation is accurate and stable. The good smoothness of the simulated hydrographs is related to the antecedent runoff values. The continuously antecedent runoff inputs ensure the continuity of the discharge time series and make the simulated hydrographs very smooth. For the reason of space limitation, we only demonstrate four typical hydrographs selected from the calibration and validation periods in Figure 3.

Figure 3 Hydrographs of the calibration and validation periods. (a) Flood event 1988061101 of calibration period. (b) Flood event 2001062008 of validation period. (c) Flood event 1993052700 of calibration period. (d) Flood event 1996060100 of validation period.
Figure 3

Hydrographs of the calibration and validation periods. (a) Flood event 1988061101 of calibration period. (b) Flood event 2001062008 of validation period. (c) Flood event 1993052700 of calibration period. (d) Flood event 1996060100 of validation period.

3.3 Error statistics of flood events

The error statistics of flood events simulations are listed in Table 1. As we can see in the table, the calibration and validation accuracies seem very satisfactory. The observed peak flows range from 633  to 6,490 m3/s. The observed total rainfall ranges from 74.2 to 1199.9 mm. The maximum and minimum peak flows of the calibration period are 4,700 and 633 m3/s, respectively. The maximum and minimum peak flows of the validation period are 6,490 and 1,410 m3/s, respectively. The maximum peak flow of the validation period exceeds the peak flow range of the calibration period. This may lead to a worse validation performance compared with the calibration period. Detailed analysis of other error statistic criteria can be found in the following paragraphs.

Table 1

Error statistics of flood events of calibration and validation periods

Flood IDCalibration or validation periodsTotal rainfall (mm)Observed peak flow (m3/s)Total volume relative error (%)Peak flow relative error (%)Peak flow present time error (h)Nash–Sutcliffe coefficient of efficiency
1982050108Calibration512.04,280−0.08−0.70−10.98
198305110875.51,3001.813.2000.96
198305142279.91,510−6.948.3000.98
1983060906119.42,170−0.38−8.7030.92
1984082620172.72,5131.99−4.50−40.98
1986061108511.62,260−3.10−8.2000.94
198705010874.26337.4159.60−10.52
1987061908110.4945−22.36−21.10−60.79
1988050704154.61,3903.689.5010.91
1988061101191.91,000−10.59−8.2010.87
1989050108312.91,740−12.42−7.0000.97
1989061206197.92,274−3.887.4000.98
1989063023161.61,740−4.65−6.9000.97
1989072208132.01,4703.88−5.6020.90
199005010889.51,7007.65−18.4010.96
1990061108355.52,500−7.379.70−30.94
1991051800504.22,220−10.7019.7030.91
1991063008355.12,060−4.58−30.3010.87
1992062000357.53,150−10.780.7010.96
19930527001074.44,700−2.7918.3000.96
1994050100Validation779.04,160−9.16−0.3010.97
19950515001010.94,070−7.97−13.7030.89
19960601001199.96,490−5.35−14.10−10.96
1997060600552.52,7303.81−3.5030.96
1999052108208.42,9600.10−6.10−10.98
1999062215617.03,780−14.47−10.2000.92
200105010898.11,41024.2418.1000.91
2001062008297.23,6407.090.5060.90
2002051308536.22,1205.890.4000.89

3.3.1 Total volume relative error

As for the calibration period, there are 14 negative relative errors and 6 positive relative errors. This result indicates that the model calibrations are preferred to underestimate the total volume of the runoff. As for the validation period, there are four negative relative errors and five positive relative errors. This result shows that the validation period do not overestimate or underestimate the total volume significantly. In the calibration period, only flood event 1987061908’s absolute total volume relative error exceeds 20%. In the validation period, only flood event 2001050108’s absolute total volume relative error exceeds 20%. These results prove that the HML hydrological model performs well in total volume estimations, especially in the validation period. The HML hydrological model has good generalization capability and obtains good results in the validation period.

3.3.2 Peak flow relative error

The peak flow relative errors of calibration period range from −30.3% to 59.6%. There are three flood events’ absolute peak flow relative errors exceed 20%. They are 1987050108, 1987061908, and 1991063008. As for the validation period, no flood event’s absolute peak flow relative error exceeds 20%. As for the calibration period, there are 11 negative relative errors and 9 positive relative errors. As for the validation period, there are six negative relative errors and three positive relative errors. These results indicate that the peak flow simulations are not significantly underestimated or overestimated in the calibration period, however, they are underestimated in the validation period for six flood events. As for the flood event 1996060166, which has the maximum peak flow value throughout the calibration and validation period, the HML hydrological model successfully forecasts the peak value within the 20% error threshold even it exceeds the maximum peak flow value of the calibration period. This result proves the excellent generalization capability of the HML hydrological model.

3.3.3 Peak flow present time error

There are two flood events exceeding the peak flow present time error threshold (3 h) in the calibration period. There is only one flood event exceeding the peak flow present time error threshold (3 h) in the validation period. Generally speaking, the HML hydrological model performs well in the peak flow present time estimation. We also notice that, as for the calibration period, there are five negative errors and eight positive errors and as for the validation period, there are two negative errors and four positive errors. The HML hydrological model seems inclined to forecast peak flow a bit ahead of time.

3.3.4 Nash–Sutcliffe coefficient of efficiency

The HML hydrological model performs very well considering the criterion of Nash–Sutcliffe coefficient of efficiency (NSCE). Most flood events achieve NSCE higher or equal to 0.9. Only flood event 1987050108 and 1987061908 obtain NSCE of 0.52 and 0.79, respectively. We notice that these two events’ peak flows are relatively small (633 and 945 m3/s) compared with other flood events. This may be the reason why the HML hydrological model performs not well in these two events’ simulations.

4 Conclusions

In this study, we coupled the ANN with KNN method to build a novel HML hydrological model for flood forecast purpose. The advantage of the proposed model over traditional neural network models is that it can predict discharge continuously without accuracy loss owed to its specially designed model structure. In order to overcome the local minimum issue of the network training, a GA and Levenberg–Marquardt-based multi-objective training method was also proposed. Real-world application of the HML hydrological model indicated its satisfactory performance and reliable stability, which enlightened the possibility of the further application of the HML hydrological model in flood forecast problems.

Acknowledgments

This research was funded by Beijing Natural Science Foundation (8181001), SKL of HESS-1914, National Natural Science Foundation of China (51909272), National Key R&D Program of China (2017YFC1502706, 2019YFC1510605), and IWHR Research & Development Support Program (JZ0145B022017). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 and TITAN V GPU used for this research. Guangyuan Kan and Ke Liang are corresponding authors. The authors declare that there is no conflict of interest regarding the publication of this paper.

  1. Author contribution: Guangyuan Kan designed the experiments and Ke Liang carried them out. Guangyuan Kan developed the model code and Ke Liang performed the simulations. Guangyuan Kan and Ke Liang prepared the manuscript with contributions from all co-authors. The authors applied the SDC approach for the sequence of authors.

References

[1] Mansouri I, Shariati M, Safa M, Ibrahim Z, Tahir M, Petkovi D. Analysis of influential factors for predicting the shear strength of a v-shaped angle shear connector in composite beams using an adaptive neuro-fuzzy technique. J Intell Manuf. 2017;30(3):1247–57.10.1007/s10845-017-1306-6Suche in Google Scholar

[2] Safa M, Shariati M, Ibrahim Z, Toghroli A, Petkovic D. Potential of adaptive neuro fuzzy inference system for evaluating the factors affecting steel-concrete composite beam’s shear strength. Steel Compos Struct. 2016;21(3):679–88.10.12989/scs.2016.21.3.679Suche in Google Scholar

[3] Toghroli A, Suhatril M, Ibrahim Z, Safa M, Shariati M, Shamshirband S. Potential of soft computing approach for evaluating the factors affecting the capacity of steel-concrete composite beam. J Intell Manuf. 2016;29(8):1793–801.10.1007/s10845-016-1217-ySuche in Google Scholar

[4] Toghroli A, Mohammadhassani M, Suhatril M, Shariati M, Ibrahim Z. Prediction of shear capacity of channel shear connectors using the ANFIS model. Steel Compos Struct. 2014;17(5):623–39.10.12989/scs.2014.17.5.623Suche in Google Scholar

[5] Mohammadhassani M, Nezamabadi-Pour H, Suhatril M, Shariati M. An evolutionary fuzzy modelling approach and comparison of different methods for shear strength prediction of high-strength concrete beams without stirrups. Smart Struct Syst. 2014;14(5):785–809.10.12989/sss.2014.14.5.785Suche in Google Scholar

[6] Mohammadhassani M, Nezamabadi-Pour H, Suhatril M, Shariati A. Identification of a suitable ANN architecture in predicting strain in tie section of concrete deep beams. Struct Eng Mech. 2013;46(6):853–68.10.12989/sem.2013.46.6.853Suche in Google Scholar

[7] Petković D, Ćojbašič Ž, Nikolić V. Adaptive neuro-fuzzy approach for wind turbine power coefficient estimation. Renew Sust Energ Rev. 2013;28(8):191–5.10.1016/j.rser.2013.07.049Suche in Google Scholar

[8] Petkovi D, Ojbai A, Nikoli V, Shamshirband S, Mat Kiah M, Anuar N, et al. Adaptive neuro-fuzzy maximal power extraction of wind turbine with continuously variable transmission. Energy. 2014;64:868–74.10.1016/j.energy.2013.10.094Suche in Google Scholar

[9] Petkovi D, Ab Hamid S, Ojbai A, Pavlovi N. Adapting project management method and ANFIS strategy for variables selection and analyzing wind turbine wake effect. Nat Hazards. 2014;74(2):463–75.10.1007/s11069-014-1189-1Suche in Google Scholar

[10] Petkovi D, Nikoli V, Miti V, Koci L. Estimation of fractal representation of wind speed fluctuation by artificial neural network with different training algorothms. Flow Meas Instrum. 2017;54:172–6.10.1016/j.flowmeasinst.2017.01.007Suche in Google Scholar

[11] Nikoli V, Miti V, Koci L, Petkovi D. Wind speed parameters sensitivity analysis based on fractals and neuro-fuzzy selection technique. Knowl Inf Syst. 2017;52(1):255–65.10.1007/s10115-016-1006-0Suche in Google Scholar

[12] Anicic O, Petkovi D, Cvetkovic S. Evaluation of wind turbine noise by soft computing methodologies: a comparative study. Renew Sust Energ Rev. 2016;56:1122–8.10.1016/j.rser.2015.12.028Suche in Google Scholar

[13] Petković D, Pavlović N, Ćojbašić Ž. Wind farm efficiency by adaptive neuro-fuzzy strategy. Int J Electr Power Energy Syst. 2016;81:215–21.10.1016/j.ijepes.2016.02.020Suche in Google Scholar

[14] Dong J, Zheng C, Kan G, Wen J, Zhao M, Yu J. Applying the ensemble artificial neural network-based hybrid data-driven model to daily total load forecasting. Neural Comput Appl. 2015;26(3):603–11.10.1007/s00521-014-1727-5Suche in Google Scholar

[15] Lei T, Pang Z, Wang X, Li L, Fu J, Kan G, et al. Drought and carbon cycling of grassland ecosystems under global change: a review. Water-sui. 2016;8:460.10.3390/w8100460Suche in Google Scholar

[16] Li C, Cheng X, Li N, Du X, Yu Q, Kan G. A framework for flood risk analysis and benefit assessment of flood control measures in urban areas. Int J Env Res Pub He. 2016;13:787.10.3390/ijerph13080787Suche in Google Scholar

[17] Li Z, Kan G, Yao C, Liu Z, Li Q, Yu S. An improved neural network model and its application in hydrological simulation. J Hydrol Eng. 2014;19(10):04014019-1–04014019-17.10.1061/(ASCE)HE.1943-5584.0000958Suche in Google Scholar

[18] Kan G, Yao C, Li Q, Li Z, Yu Z, Liu Z, et al. Improving event-based rainfall-runoff simulation using an ensemble artificial neural network based hybrid data-driven model. Stoch Env Res Risk A. 2015;29:1345–70.10.1007/s00477-015-1040-6Suche in Google Scholar

[19] Kan G, Li J, Zhang X, Ding L, He X, Liang K, et al. A new hybrid data-driven model for event-based rainfall-runoff simulation. Neural Comput Appl. 2015;28:2519–34.10.1007/s00521-016-2200-4Suche in Google Scholar

[20] Kan G, Liang K, Li J, Ding L, He X, Hu Y, et al. Accelerating the SCE-UA global optimization method based on multi-core CPU and many-core GPU. Adv Meteorol. 2016;8483728:1–10.10.1155/2016/8483728Suche in Google Scholar

[21] Kan G, Lei T, Liang K, Li J, Ding L, He X, et al. A multi-core CPU and many-core GPU based fast parallel shuffled complex evolution global optimization approach. IEEE T Parall Distr. 2016;28(2):332–44.10.1109/TPDS.2016.2575822Suche in Google Scholar

[22] Kan G, Zhang M, Liang K, Wang H, Jiang Y, Li J, et al. Improving water quantity simulation & forecasting to solve the energy-water-food nexus issue by using heterogeneous computing accelerated global optimization method. Appl Energ. 2016;210:420–33.10.1016/j.apenergy.2016.08.017Suche in Google Scholar

[23] Kan G, He X, Ding L, Li J, Lei T, Liang K, et al. An improved hybrid data-driven model and its application in daily rainfall-runoff simulation. IOP Conference Series: Earth and Environmental Science, 6th Digital Earth Summit, Beijing; 2016, vol. 46. p. 012029, 1–8.Suche in Google Scholar

[24] Kan G, He X, Li J, Ding L, Zhang D, Lei T, et al. A novel hybrid data-driven model for multi-input single-output system simulation. Neural Comput Appl. 2018;29:577–93.10.1007/s00521-016-2534-ySuche in Google Scholar

[25] Zuo D, Cai S, Xu Z, Li F, Sun W, Yang X, et al. Spatiotemporal patterns of drought at various time scales in Shandong Province of Eastern China. Theor Appl Climatol. 2018;131:271–84.10.1007/s00704-016-1969-5Suche in Google Scholar

[26] Chau K. Use of meta-heuristic techniques in rainfall-runoff modelling. Water-sui. 2017;9(3):186.10.3390/w9030186Suche in Google Scholar

[27] Shastri H, Ghosh S, Karmakar S. Improving global forecast system of extreme precipitation events with regional statistical model: application of quantile-based probabilistic forecasts. J Geophys Res-Atmos 2017;122(3):1617–34.10.1002/2016JD025489Suche in Google Scholar

[28] Wang J, Shi P, Jiang P, Hu J, Qu S, Chen X, et al. Application of BP neural network algorithm in traditional hydrological model for flood forecasting. Water-sui. 2017;9(1):48.10.3390/w9010048Suche in Google Scholar

[29] Matos J, Portela M, Schleiss A. Towards safer data-driven forecasting of extreme streamflows. Water Resour Manag. 2018;32(2):701–20.10.1007/s11269-017-1834-zSuche in Google Scholar

[30] Wang D, Borthwick A, He H, Wang Y, Zhu J, Lu Y, et al. A hybrid wavelet de-noising and rank-set pair analysis approach for forecasting hydro-meteorological time series. Env Res. 2018;160:269–81.10.1016/j.envres.2017.09.033Suche in Google Scholar

[31] Sharma A. Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: part 1-a strategy for system predictor identification. J Hydrol. 2000;239(1–4):232–9.10.1016/S0022-1694(00)00346-2Suche in Google Scholar

[32] Sharma A. Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: part 3-a nonparametric probabilistic forecast model. J Hydrol. 2000;239(1–4):249–58.10.1016/S0022-1694(00)00348-6Suche in Google Scholar

[33] Sharma A, Luk K, Cordery I, Lall U. Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: part 2-predictor identification of quarterly rainfall using ocean-atmosphere information. J Hydrol. 2000;239(1–4):240–8.10.1016/S0022-1694(00)00347-4Suche in Google Scholar

Received: 2018-12-19
Revised: 2020-05-26
Accepted: 2020-05-29
Published Online: 2020-09-18

© 2020 Guangyuan Kan et al., published by De Gruyter

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

Artikel in diesem Heft

  1. Regular Articles
  2. The simulation approach to the interpretation of archival aerial photographs
  3. The application of137Cs and210Pbexmethods in soil erosion research of Titel loess plateau, Vojvodina, Northern Serbia
  4. Provenance and tectonic significance of the Zhongwunongshan Group from the Zhongwunongshan Structural Belt in China: insights from zircon geochronology
  5. Analysis, Assessment and Early Warning of Mudflow Disasters along the Shigatse Section of the China–Nepal Highway
  6. Sedimentary succession and recognition marks of lacustrine gravel beach-bars, a case study from the Qinghai Lake, China
  7. Predicting small water courses’ physico-chemical status from watershed characteristics with two multivariate statistical methods
  8. An Overview of the Carbonatites from the Indian Subcontinent
  9. A new statistical approach to the geochemical systematics of Italian alkaline igneous rocks
  10. The significance of karst areas in European national parks and geoparks
  11. Geochronology, trace elements and Hf isotopic geochemistry of zircons from Swat orthogneisses, Northern Pakistan
  12. Regional-scale drought monitor using synthesized index based on remote sensing in northeast China
  13. Application of combined electrical resistivity tomography and seismic reflection method to explore hidden active faults in Pingwu, Sichuan, China
  14. Impact of interpolation techniques on the accuracy of large-scale digital elevation model
  15. Natural and human-induced factors controlling the phreatic groundwater geochemistry of the Longgang River basin, South China
  16. Land use/land cover assessment as related to soil and irrigation water salinity over an oasis in arid environment
  17. Effect of tillage, slope, and rainfall on soil surface microtopography quantified by geostatistical and fractal indices during sheet erosion
  18. Validation of the number of tie vectors in post-processing using the method of frequency in a centric cube
  19. An integrated petrophysical-based wedge modeling and thin bed AVO analysis for improved reservoir characterization of Zhujiang Formation, Huizhou sub-basin, China: A case study
  20. A grain size auto-classification of Baikouquan Formation, Mahu Depression, Junggar Basin, China
  21. Dynamics of mid-channel bars in the Middle Vistula River in response to ferry crossing abutment construction
  22. Estimation of permeability and saturation based on imaginary component of complex resistivity spectra: A laboratory study
  23. Distribution characteristics of typical geological relics in the Western Sichuan Plateau
  24. Inconsistency distribution patterns of different remote sensing land-cover data from the perspective of ecological zoning
  25. A new methodological approach (QEMSCAN®) in the mineralogical study of Polish loess: Guidelines for further research
  26. Displacement and deformation study of engineering structures with the use of modern laser technologies
  27. Virtual resolution enhancement: A new enhancement tool for seismic data
  28. Aeromagnetic mapping of fault architecture along Lagos–Ore axis, southwestern Nigeria
  29. Deformation and failure mechanism of full seam chamber with extra-large section and its control technology
  30. Plastic failure zone characteristics and stability control technology of roadway in the fault area under non-uniformly high geostress: A case study from Yuandian Coal Mine in Northern Anhui Province, China
  31. Comparison of swarm intelligence algorithms for optimized band selection of hyperspectral remote sensing image
  32. Soil carbon stock and nutrient characteristics of Senna siamea grove in the semi-deciduous forest zone of Ghana
  33. Carbonatites from the Southern Brazilian platform: I
  34. Seismicity, focal mechanism, and stress tensor analysis of the Simav region, western Turkey
  35. Application of simulated annealing algorithm for 3D coordinate transformation problem solution
  36. Application of the terrestrial laser scanner in the monitoring of earth structures
  37. The Cretaceous igneous rocks in southeastern Guangxi and their implication for tectonic environment in southwestern South China Block
  38. Pore-scale gas–water flow in rock: Visualization experiment and simulation
  39. Assessment of surface parameters of VDW foundation piles using geodetic measurement techniques
  40. Spatial distribution and risk assessment of toxic metals in agricultural soils from endemic nasopharyngeal carcinoma region in South China
  41. An ABC-optimized fuzzy ELECTRE approach for assessing petroleum potential at the petroleum system level
  42. Microscopic mechanism of sandstone hydration in Yungang Grottoes, China
  43. Importance of traditional landscapes in Slovenia for conservation of endangered butterfly
  44. Landscape pattern and economic factors’ effect on prediction accuracy of cellular automata-Markov chain model on county scale
  45. The influence of river training on the location of erosion and accumulation zones (Kłodzko County, South West Poland)
  46. Multi-temporal survey of diaphragm wall with terrestrial laser scanning method
  47. Functionality and reliability of horizontal control net (Poland)
  48. Strata behavior and control strategy of backfilling collaborate with caving fully-mechanized mining
  49. The use of classical methods and neural networks in deformation studies of hydrotechnical objects
  50. Ice-crevasse sedimentation in the eastern part of the Głubczyce Plateau (S Poland) during the final stage of the Drenthian Glaciation
  51. Structure of end moraines and dynamics of the recession phase of the Warta Stadial ice sheet, Kłodawa Upland, Central Poland
  52. Mineralogy, mineral chemistry and thermobarometry of post-mineralization dykes of the Sungun Cu–Mo porphyry deposit (Northwest Iran)
  53. Main problems of the research on the Palaeolithic of Halych-Dnister region (Ukraine)
  54. Application of isometric transformation and robust estimation to compare the measurement results of steel pipe spools
  55. Hybrid machine learning hydrological model for flood forecast purpose
  56. Rainfall thresholds of shallow landslides in Wuyuan County of Jiangxi Province, China
  57. Dynamic simulation for the process of mining subsidence based on cellular automata model
  58. Developing large-scale international ecological networks based on least-cost path analysis – a case study of Altai mountains
  59. Seismic characteristics of polygonal fault systems in the Great South Basin, New Zealand
  60. New approach of clustering of late Pleni-Weichselian loess deposits (L1LL1) in Poland
  61. Implementation of virtual reference points in registering scanning images of tall structures
  62. Constraints of nonseismic geophysical data on the deep geological structure of the Benxi iron-ore district, Liaoning, China
  63. Mechanical analysis of basic roof fracture mechanism and feature in coal mining with partial gangue backfilling
  64. The violent ground motion before the Jiuzhaigou earthquake Ms7.0
  65. Landslide site delineation from geometric signatures derived with the Hilbert–Huang transform for cases in Southern Taiwan
  66. Hydrological process simulation in Manas River Basin using CMADS
  67. LA-ICP-MS U–Pb ages of detrital zircons from Middle Jurassic sedimentary rocks in southwestern Fujian: Sedimentary provenance and its geological significance
  68. Analysis of pore throat characteristics of tight sandstone reservoirs
  69. Effects of igneous intrusions on source rock in the early diagenetic stage: A case study on Beipiao Formation in Jinyang Basin, Northeast China
  70. Applying floodplain geomorphology to flood management (The Lower Vistula River upstream from Plock, Poland)
  71. Effect of photogrammetric RPAS flight parameters on plani-altimetric accuracy of DTM
  72. Morphodynamic conditions of heavy metal concentration in deposits of the Vistula River valley near Kępa Gostecka (central Poland)
  73. Accuracy and functional assessment of an original low-cost fibre-based inclinometer designed for structural monitoring
  74. The impacts of diagenetic facies on reservoir quality in tight sandstones
  75. Application of electrical resistivity imaging to detection of hidden geological structures in a single roadway
  76. Comparison between electrical resistivity tomography and tunnel seismic prediction 303 methods for detecting the water zone ahead of the tunnel face: A case study
  77. The genesis model of carbonate cementation in the tight oil reservoir: A case of Chang 6 oil layers of the Upper Triassic Yanchang Formation in the western Jiyuan area, Ordos Basin, China
  78. Disintegration characteristics in granite residual soil and their relationship with the collapsing gully in South China
  79. Analysis of surface deformation and driving forces in Lanzhou
  80. Geochemical characteristics of produced water from coalbed methane wells and its influence on productivity in Laochang Coalfield, China
  81. A combination of genetic inversion and seismic frequency attributes to delineate reservoir targets in offshore northern Orange Basin, South Africa
  82. Explore the application of high-resolution nighttime light remote sensing images in nighttime marine ship detection: A case study of LJ1-01 data
  83. DTM-based analysis of the spatial distribution of topolineaments
  84. Spatiotemporal variation and climatic response of water level of major lakes in China, Mongolia, and Russia
  85. The Cretaceous stratigraphy, Songliao Basin, Northeast China: Constrains from drillings and geophysics
  86. Canal of St. Bartholomew in Seča/Sezza: Social construction of the seascape
  87. A modelling resin material and its application in rock-failure study: Samples with two 3D internal fracture surfaces
  88. Utilization of marble piece wastes as base materials
  89. Slope stability evaluation using backpropagation neural networks and multivariate adaptive regression splines
  90. Rigidity of “Warsaw clay” from the Poznań Formation determined by in situ tests
  91. Numerical simulation for the effects of waves and grain size on deltaic processes and morphologies
  92. Impact of tourism activities on water pollution in the West Lake Basin (Hangzhou, China)
  93. Fracture characteristics from outcrops and its meaning to gas accumulation in the Jiyuan Basin, Henan Province, China
  94. Impact evaluation and driving type identification of human factors on rural human settlement environment: Taking Gansu Province, China as an example
  95. Identification of the spatial distributions, pollution levels, sources, and health risk of heavy metals in surface dusts from Korla, NW China
  96. Petrography and geochemistry of clastic sedimentary rocks as evidence for the provenance of the Jurassic stratum in the Daqingshan area
  97. Super-resolution reconstruction of a digital elevation model based on a deep residual network
  98. Seismic prediction of lithofacies heterogeneity in paleogene hetaoyuan shale play, Biyang depression, China
  99. Cultural landscape of the Gorica Hills in the nineteenth century: Franciscean land cadastre reports as the source for clarification of the classification of cultivable land types
  100. Analysis and prediction of LUCC change in Huang-Huai-Hai river basin
  101. Hydrochemical differences between river water and groundwater in Suzhou, Northern Anhui Province, China
  102. The relationship between heat flow and seismicity in global tectonically active zones
  103. Modeling of Landslide susceptibility in a part of Abay Basin, northwestern Ethiopia
  104. M-GAM method in function of tourism potential assessment: Case study of the Sokobanja basin in eastern Serbia
  105. Dehydration and stabilization of unconsolidated laminated lake sediments using gypsum for the preparation of thin sections
  106. Agriculture and land use in the North of Russia: Case study of Karelia and Yakutia
  107. Textural characteristics, mode of transportation and depositional environment of the Cretaceous sandstone in the Bredasdorp Basin, off the south coast of South Africa: Evidence from grain size analysis
  108. One-dimensional constrained inversion study of TEM and application in coal goafs’ detection
  109. The spatial distribution of retail outlets in Urumqi: The application of points of interest
  110. Aptian–Albian deposits of the Ait Ourir basin (High Atlas, Morocco): New additional data on their paleoenvironment, sedimentology, and palaeogeography
  111. Traditional agricultural landscapes in Uskopaljska valley (Bosnia and Herzegovina)
  112. A detection method for reservoir waterbodies vector data based on EGADS
  113. Modelling and mapping of the COVID-19 trajectory and pandemic paths at global scale: A geographer’s perspective
  114. Effect of organic maturity on shale gas genesis and pores development: A case study on marine shale in the upper Yangtze region, South China
  115. Gravel roundness quantitative analysis for sedimentary microfacies of fan delta deposition, Baikouquan Formation, Mahu Depression, Northwestern China
  116. Features of terraces and the incision rate along the lower reaches of the Yarlung Zangbo River east of Namche Barwa: Constraints on tectonic uplift
  117. Application of laser scanning technology for structure gauge measurement
  118. Calibration of the depth invariant algorithm to monitor the tidal action of Rabigh City at the Red Sea Coast, Saudi Arabia
  119. Evolution of the Bystrzyca River valley during Middle Pleistocene Interglacial (Sudetic Foreland, south-western Poland)
  120. A 3D numerical analysis of the compaction effects on the behavior of panel-type MSE walls
  121. Landscape dynamics at borderlands: analysing land use changes from Southern Slovenia
  122. Effects of oil viscosity on waterflooding: A case study of high water-cut sandstone oilfield in Kazakhstan
  123. Special Issue: Alkaline-Carbonatitic magmatism
  124. Carbonatites from the southern Brazilian Platform: A review. II: Isotopic evidences
  125. Review Article
  126. Technology and innovation: Changing concept of rural tourism – A systematic review
https://doi.org/10.1515/geo-2020-0166
Schlagwörter für diesen Artikel
hybrid machine learning; hydrological model; flood forecast; artificial neural network; K-nearest neighbor method
Creative Commons
BY 4.0

Artikel in diesem Heft

  1. Regular Articles
  2. The simulation approach to the interpretation of archival aerial photographs
  3. The application of137Cs and210Pbexmethods in soil erosion research of Titel loess plateau, Vojvodina, Northern Serbia
  4. Provenance and tectonic significance of the Zhongwunongshan Group from the Zhongwunongshan Structural Belt in China: insights from zircon geochronology
  5. Analysis, Assessment and Early Warning of Mudflow Disasters along the Shigatse Section of the China–Nepal Highway
  6. Sedimentary succession and recognition marks of lacustrine gravel beach-bars, a case study from the Qinghai Lake, China
  7. Predicting small water courses’ physico-chemical status from watershed characteristics with two multivariate statistical methods
  8. An Overview of the Carbonatites from the Indian Subcontinent
  9. A new statistical approach to the geochemical systematics of Italian alkaline igneous rocks
  10. The significance of karst areas in European national parks and geoparks
  11. Geochronology, trace elements and Hf isotopic geochemistry of zircons from Swat orthogneisses, Northern Pakistan
  12. Regional-scale drought monitor using synthesized index based on remote sensing in northeast China
  13. Application of combined electrical resistivity tomography and seismic reflection method to explore hidden active faults in Pingwu, Sichuan, China
  14. Impact of interpolation techniques on the accuracy of large-scale digital elevation model
  15. Natural and human-induced factors controlling the phreatic groundwater geochemistry of the Longgang River basin, South China
  16. Land use/land cover assessment as related to soil and irrigation water salinity over an oasis in arid environment
  17. Effect of tillage, slope, and rainfall on soil surface microtopography quantified by geostatistical and fractal indices during sheet erosion
  18. Validation of the number of tie vectors in post-processing using the method of frequency in a centric cube
  19. An integrated petrophysical-based wedge modeling and thin bed AVO analysis for improved reservoir characterization of Zhujiang Formation, Huizhou sub-basin, China: A case study
  20. A grain size auto-classification of Baikouquan Formation, Mahu Depression, Junggar Basin, China
  21. Dynamics of mid-channel bars in the Middle Vistula River in response to ferry crossing abutment construction
  22. Estimation of permeability and saturation based on imaginary component of complex resistivity spectra: A laboratory study
  23. Distribution characteristics of typical geological relics in the Western Sichuan Plateau
  24. Inconsistency distribution patterns of different remote sensing land-cover data from the perspective of ecological zoning
  25. A new methodological approach (QEMSCAN®) in the mineralogical study of Polish loess: Guidelines for further research
  26. Displacement and deformation study of engineering structures with the use of modern laser technologies
  27. Virtual resolution enhancement: A new enhancement tool for seismic data
  28. Aeromagnetic mapping of fault architecture along Lagos–Ore axis, southwestern Nigeria
  29. Deformation and failure mechanism of full seam chamber with extra-large section and its control technology
  30. Plastic failure zone characteristics and stability control technology of roadway in the fault area under non-uniformly high geostress: A case study from Yuandian Coal Mine in Northern Anhui Province, China
  31. Comparison of swarm intelligence algorithms for optimized band selection of hyperspectral remote sensing image
  32. Soil carbon stock and nutrient characteristics of Senna siamea grove in the semi-deciduous forest zone of Ghana
  33. Carbonatites from the Southern Brazilian platform: I
  34. Seismicity, focal mechanism, and stress tensor analysis of the Simav region, western Turkey
  35. Application of simulated annealing algorithm for 3D coordinate transformation problem solution
  36. Application of the terrestrial laser scanner in the monitoring of earth structures
  37. The Cretaceous igneous rocks in southeastern Guangxi and their implication for tectonic environment in southwestern South China Block
  38. Pore-scale gas–water flow in rock: Visualization experiment and simulation
  39. Assessment of surface parameters of VDW foundation piles using geodetic measurement techniques
  40. Spatial distribution and risk assessment of toxic metals in agricultural soils from endemic nasopharyngeal carcinoma region in South China
  41. An ABC-optimized fuzzy ELECTRE approach for assessing petroleum potential at the petroleum system level
  42. Microscopic mechanism of sandstone hydration in Yungang Grottoes, China
  43. Importance of traditional landscapes in Slovenia for conservation of endangered butterfly
  44. Landscape pattern and economic factors’ effect on prediction accuracy of cellular automata-Markov chain model on county scale
  45. The influence of river training on the location of erosion and accumulation zones (Kłodzko County, South West Poland)
  46. Multi-temporal survey of diaphragm wall with terrestrial laser scanning method
  47. Functionality and reliability of horizontal control net (Poland)
  48. Strata behavior and control strategy of backfilling collaborate with caving fully-mechanized mining
  49. The use of classical methods and neural networks in deformation studies of hydrotechnical objects
  50. Ice-crevasse sedimentation in the eastern part of the Głubczyce Plateau (S Poland) during the final stage of the Drenthian Glaciation
  51. Structure of end moraines and dynamics of the recession phase of the Warta Stadial ice sheet, Kłodawa Upland, Central Poland
  52. Mineralogy, mineral chemistry and thermobarometry of post-mineralization dykes of the Sungun Cu–Mo porphyry deposit (Northwest Iran)
  53. Main problems of the research on the Palaeolithic of Halych-Dnister region (Ukraine)
  54. Application of isometric transformation and robust estimation to compare the measurement results of steel pipe spools
  55. Hybrid machine learning hydrological model for flood forecast purpose
  56. Rainfall thresholds of shallow landslides in Wuyuan County of Jiangxi Province, China
  57. Dynamic simulation for the process of mining subsidence based on cellular automata model
  58. Developing large-scale international ecological networks based on least-cost path analysis – a case study of Altai mountains
  59. Seismic characteristics of polygonal fault systems in the Great South Basin, New Zealand
  60. New approach of clustering of late Pleni-Weichselian loess deposits (L1LL1) in Poland
  61. Implementation of virtual reference points in registering scanning images of tall structures
  62. Constraints of nonseismic geophysical data on the deep geological structure of the Benxi iron-ore district, Liaoning, China
  63. Mechanical analysis of basic roof fracture mechanism and feature in coal mining with partial gangue backfilling
  64. The violent ground motion before the Jiuzhaigou earthquake Ms7.0
  65. Landslide site delineation from geometric signatures derived with the Hilbert–Huang transform for cases in Southern Taiwan
  66. Hydrological process simulation in Manas River Basin using CMADS
  67. LA-ICP-MS U–Pb ages of detrital zircons from Middle Jurassic sedimentary rocks in southwestern Fujian: Sedimentary provenance and its geological significance
  68. Analysis of pore throat characteristics of tight sandstone reservoirs
  69. Effects of igneous intrusions on source rock in the early diagenetic stage: A case study on Beipiao Formation in Jinyang Basin, Northeast China
  70. Applying floodplain geomorphology to flood management (The Lower Vistula River upstream from Plock, Poland)
  71. Effect of photogrammetric RPAS flight parameters on plani-altimetric accuracy of DTM
  72. Morphodynamic conditions of heavy metal concentration in deposits of the Vistula River valley near Kępa Gostecka (central Poland)
  73. Accuracy and functional assessment of an original low-cost fibre-based inclinometer designed for structural monitoring
  74. The impacts of diagenetic facies on reservoir quality in tight sandstones
  75. Application of electrical resistivity imaging to detection of hidden geological structures in a single roadway
  76. Comparison between electrical resistivity tomography and tunnel seismic prediction 303 methods for detecting the water zone ahead of the tunnel face: A case study
  77. The genesis model of carbonate cementation in the tight oil reservoir: A case of Chang 6 oil layers of the Upper Triassic Yanchang Formation in the western Jiyuan area, Ordos Basin, China
  78. Disintegration characteristics in granite residual soil and their relationship with the collapsing gully in South China
  79. Analysis of surface deformation and driving forces in Lanzhou
  80. Geochemical characteristics of produced water from coalbed methane wells and its influence on productivity in Laochang Coalfield, China
  81. A combination of genetic inversion and seismic frequency attributes to delineate reservoir targets in offshore northern Orange Basin, South Africa
  82. Explore the application of high-resolution nighttime light remote sensing images in nighttime marine ship detection: A case study of LJ1-01 data
  83. DTM-based analysis of the spatial distribution of topolineaments
  84. Spatiotemporal variation and climatic response of water level of major lakes in China, Mongolia, and Russia
  85. The Cretaceous stratigraphy, Songliao Basin, Northeast China: Constrains from drillings and geophysics
  86. Canal of St. Bartholomew in Seča/Sezza: Social construction of the seascape
  87. A modelling resin material and its application in rock-failure study: Samples with two 3D internal fracture surfaces
  88. Utilization of marble piece wastes as base materials
  89. Slope stability evaluation using backpropagation neural networks and multivariate adaptive regression splines
  90. Rigidity of “Warsaw clay” from the Poznań Formation determined by in situ tests
  91. Numerical simulation for the effects of waves and grain size on deltaic processes and morphologies
  92. Impact of tourism activities on water pollution in the West Lake Basin (Hangzhou, China)
  93. Fracture characteristics from outcrops and its meaning to gas accumulation in the Jiyuan Basin, Henan Province, China
  94. Impact evaluation and driving type identification of human factors on rural human settlement environment: Taking Gansu Province, China as an example
  95. Identification of the spatial distributions, pollution levels, sources, and health risk of heavy metals in surface dusts from Korla, NW China
  96. Petrography and geochemistry of clastic sedimentary rocks as evidence for the provenance of the Jurassic stratum in the Daqingshan area
  97. Super-resolution reconstruction of a digital elevation model based on a deep residual network
  98. Seismic prediction of lithofacies heterogeneity in paleogene hetaoyuan shale play, Biyang depression, China
  99. Cultural landscape of the Gorica Hills in the nineteenth century: Franciscean land cadastre reports as the source for clarification of the classification of cultivable land types
  100. Analysis and prediction of LUCC change in Huang-Huai-Hai river basin
  101. Hydrochemical differences between river water and groundwater in Suzhou, Northern Anhui Province, China
  102. The relationship between heat flow and seismicity in global tectonically active zones
  103. Modeling of Landslide susceptibility in a part of Abay Basin, northwestern Ethiopia
  104. M-GAM method in function of tourism potential assessment: Case study of the Sokobanja basin in eastern Serbia
  105. Dehydration and stabilization of unconsolidated laminated lake sediments using gypsum for the preparation of thin sections
  106. Agriculture and land use in the North of Russia: Case study of Karelia and Yakutia
  107. Textural characteristics, mode of transportation and depositional environment of the Cretaceous sandstone in the Bredasdorp Basin, off the south coast of South Africa: Evidence from grain size analysis
  108. One-dimensional constrained inversion study of TEM and application in coal goafs’ detection
  109. The spatial distribution of retail outlets in Urumqi: The application of points of interest
  110. Aptian–Albian deposits of the Ait Ourir basin (High Atlas, Morocco): New additional data on their paleoenvironment, sedimentology, and palaeogeography
  111. Traditional agricultural landscapes in Uskopaljska valley (Bosnia and Herzegovina)
  112. A detection method for reservoir waterbodies vector data based on EGADS
  113. Modelling and mapping of the COVID-19 trajectory and pandemic paths at global scale: A geographer’s perspective
  114. Effect of organic maturity on shale gas genesis and pores development: A case study on marine shale in the upper Yangtze region, South China
  115. Gravel roundness quantitative analysis for sedimentary microfacies of fan delta deposition, Baikouquan Formation, Mahu Depression, Northwestern China
  116. Features of terraces and the incision rate along the lower reaches of the Yarlung Zangbo River east of Namche Barwa: Constraints on tectonic uplift
  117. Application of laser scanning technology for structure gauge measurement
  118. Calibration of the depth invariant algorithm to monitor the tidal action of Rabigh City at the Red Sea Coast, Saudi Arabia
  119. Evolution of the Bystrzyca River valley during Middle Pleistocene Interglacial (Sudetic Foreland, south-western Poland)
  120. A 3D numerical analysis of the compaction effects on the behavior of panel-type MSE walls
  121. Landscape dynamics at borderlands: analysing land use changes from Southern Slovenia
  122. Effects of oil viscosity on waterflooding: A case study of high water-cut sandstone oilfield in Kazakhstan
  123. Special Issue: Alkaline-Carbonatitic magmatism
  124. Carbonatites from the southern Brazilian Platform: A review. II: Isotopic evidences
  125. Review Article
  126. Technology and innovation: Changing concept of rural tourism – A systematic review
Jetzt anmelden und 20% sparen
Institutioneller Zugang
Wie funktioniert Authentifizierung?
Haben Sie eine Idee, wie wir unsere Website verbessern können?
Bitte schreiben Sie uns.
© 2025 De Gruyter Brill
Heruntergeladen am 5.11.2025 von https://www.degruyterbrill.com/document/doi/10.1515/geo-2020-0166/html?lang=de
Button zum nach oben scrollen