77 citations as recorded by crossref.

1. Assessment of groundwater potential in terms of the availability and quality of the resource: a case study from Iraq A. Al-Abadi et al. 10.1007/s12665-021-09725-0
2. Spatial mapping of groundwater springs potentiality using grid search-based and genetic algorithm-based support vector regression A. Al-Fugara et al. 10.1080/10106049.2020.1716396
3. Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer W. Chen et al. 10.1016/j.gsf.2020.07.012
4. Bedload transport rate prediction: Application of novel hybrid data mining techniques K. Khosravi et al. 10.1016/j.jhydrol.2020.124774
5. Determination of compound channel apparent shear stress: application of novel data mining models Z. Khozani et al. 10.2166/hydro.2019.037
6. Multi-hazard probability assessment and mapping in Iran H. Pourghasemi et al. 10.1016/j.scitotenv.2019.07.203
7. Hybrids of Support Vector Regression with Grey Wolf Optimizer and Firefly Algorithm for Spatial Prediction of Landslide Susceptibility R. Liu et al. 10.3390/rs13244966
8. Point and interval forecasting for carbon price based on an improved analysis-forecast system C. Tian & Y. Hao 10.1016/j.apm.2019.10.022
9. A fuzzy geospatial approach for delineation of groundwater potential zones in Raipur district, India S. Singha et al. 10.1016/j.gsd.2020.100529
10. Flood Spatial Modeling in Northern Iran Using Remote Sensing and GIS: A Comparison between Evidential Belief Functions and Its Ensemble with a Multivariate Logistic Regression Model D. Tien Bui et al. 10.3390/rs11131589
11. K-Fold and State-of-the-Art Metaheuristic Machine Learning Approaches for Groundwater Potential Modelling A. Arabameri et al. 10.1007/s11269-021-02815-5
12. Stochastic Modeling of Groundwater Fluoride Contamination: Introducing Lazy Learners K. Khosravi et al. 10.1111/gwat.12963
13. Modeling cyclone-induced multi-hazard risk assessment using analytical hierarchical processing and GIS for coastal West Bengal, India M. Mondal et al. 10.1016/j.rsma.2021.101779
14. Application of novel framework approach for prediction of nitrate concentration susceptibility in coastal multi-aquifers, Bangladesh A. Islam et al. 10.1016/j.scitotenv.2021.149811
15. Groundwater potential assessment of the Sero plain using bivariate models of the frequency ratio, Shannon entropy and evidential belief function S. Khoshtinat et al. 10.1007/s12040-019-1155-0
16. A tree-based intelligence ensemble approach for spatial prediction of potential groundwater M. Avand et al. 10.1080/17538947.2020.1718785
17. New DRASTIC framework for groundwater vulnerability assessment: bivariate and multi-criteria decision-making approach coupled with metaheuristic algorithm B. Lakshminarayanan et al. 10.1007/s11356-021-15966-0
18. Comparisons of Diverse Machine Learning Approaches for Wildfire Susceptibility Mapping K. Gholamnia et al. 10.3390/sym12040604
19. GIS-Based Landslide Susceptibility Mapping Using Information, Frequency Ratio, and Artificial Neural Network Methods in Qinghai Province, Northwestern China B. Li et al. 10.1155/2021/4758062
20. Flood susceptibility mapping with machine learning, multi-criteria decision analysis and ensemble using Dempster Shafer Theory T. Gudiyangada Nachappa et al. 10.1016/j.jhydrol.2020.125275
21. Multi-Hazard Exposure Mapping Using Machine Learning for the State of Salzburg, Austria T. Nachappa et al. 10.3390/rs12172757
22. Daily streamflow prediction using optimally pruned extreme learning machine R. Adnan et al. 10.1016/j.jhydrol.2019.123981
23. Strategies for Learning Groundwater Potential Modelling Indices under Sparse Data with Supervised and Unsupervised Techniques V. Karimi et al. 10.1007/s11269-020-02555-y
24. A coupled novel framework for assessing vulnerability of water resources using hydrochemical analysis and data-driven models A. Islam et al. 10.1016/j.jclepro.2022.130407
25. Identification of artificial groundwater recharge zones in semi-arid region of southern India using geospatial and integrated decision-making approaches M. Rajasekhar et al. 10.1016/j.envc.2021.100278
26. Meteorological data mining and hybrid data-intelligence models for reference evaporation simulation: A case study in Iraq K. Khosravi et al. 10.1016/j.compag.2019.105041
27. Application of a GIS-Based Slope Unit Method for Landslide Susceptibility Mapping in Helong City: Comparative Assessment of ICM, AHP, and RF Model C. Yu & J. Chen 10.3390/sym12111848
28. Enhanced machine learning model to estimate groundwater spring potential based on digital elevation model parameters M. Msaddek et al. 10.1080/10106049.2021.2007292
29. Spatial Prediction of Landslide Susceptibility Using GIS-Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO) W. Chen et al. 10.3390/app9183755
30. New hybrid-based approach for improving the accuracy of coastal aquifer vulnerability assessment maps K. Khosravi et al. 10.1016/j.scitotenv.2021.145416
31. River Water Salinity Prediction Using Hybrid Machine Learning Models A. Melesse et al. 10.3390/w12102951
32. Groundwater Spring Potential Mapping Using Artificial Intelligence Approach Based on Kernel Logistic Regression, Random Forest, and Alternating Decision Tree Models W. Chen et al. 10.3390/app10020425
33. Application of GIS-based models of weights of evidence, weighting factor, and statistical index in spatial modeling of groundwater S. Khoshtinat et al. 10.2166/hydro.2019.127
34. Is multi-hazard mapping effective in assessing natural hazards and integrated watershed management? H. Pourghasemi et al. 10.1016/j.gsf.2019.10.008
35. Groundwater Potential Mapping Using GIS ‐Based Hybrid Artificial Intelligence Methods T. Phong et al. 10.1111/gwat.13094
36. Radon potential mapping in Jangsu-gun, South Korea using probabilistic and deep learning algorithms F. Rezaie et al. 10.1016/j.envpol.2021.118385
37. A comparison of machine learning models for the mapping of groundwater spring potential A. Al-Fugara et al. 10.1007/s12665-020-08944-1
38. Evaluating the application of metaheuristic approaches for flood simulation using GIS: A case study of Baitarani river Basin, India S. Samantaray et al. 10.1016/j.matpr.2021.11.561
39. Monthly suspended sediment load prediction using artificial intelligence: testing of a new random subspace method V. Nhu et al. 10.1080/02626667.2020.1754419
40. A Google Earth Engine Approach for Wildfire Susceptibility Prediction Fusion with Remote Sensing Data of Different Spatial Resolutions S. Tavakkoli Piralilou et al. 10.3390/rs14030672
41. Integration of statistical models and ensemble machine learning algorithms (MLAs) for developing the novel hybrid groundwater potentiality models: a case study of semi-arid watershed in Saudi Arabia J. Mallick et al. 10.1080/10106049.2021.1939439
42. SEVUCAS: A Novel GIS-Based Machine Learning Software for Seismic Vulnerability Assessment S. Lee et al. 10.3390/app9173495
43. Groundwater spring potential assessment using new ensemble data mining techniques S. Yousefi et al. 10.1016/j.measurement.2020.107652
44. Flood risk assessment using geospatial data and multi-criteria decision approach: a study from historically active flood-prone region of Himalayan foothill, India S. Roy et al. 10.1007/s12517-021-07324-8
45. A general framework and guidelines for benchmarking computational intelligence algorithms applied to forecasting problems derived from an application domain-oriented survey M. Oprea 10.1016/j.asoc.2020.106103
46. Improving groundwater potential mapping using metaheuristic approaches S. Razavi-Termeh et al. 10.1080/02626667.2020.1828589
47. Optimization of statistical and machine learning hybrid models for groundwater potential mapping P. Yariyan et al. 10.1080/10106049.2020.1870164
48. Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India A. Arora et al. 10.1016/j.scitotenv.2020.141565
49. Optimization of an adaptive neuro-fuzzy inference system for groundwater potential mapping S. Termeh et al. 10.1007/s10040-019-02017-9
50. A novel hybrid of support vector regression and metaheuristic algorithms for groundwater spring potential mapping S. Paryani et al. 10.1016/j.scitotenv.2021.151055
51. A novel per pixel and object-based ensemble approach for flood susceptibility mapping T. Gudiyangada Nachappa & S. Meena 10.1080/19475705.2020.1833990
52. Groundwater-Potential Mapping Using a Self-Learning Bayesian Network Model: A Comparison among Metaheuristic Algorithms S. Karimi-Rizvandi et al. 10.3390/w13050658
53. Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier H. Shahabi et al. 10.3390/rs12020266
54. A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model J. Duan et al. 10.1007/s00366-020-01003-0
55. Enhancing nitrate and strontium concentration prediction in groundwater by using new data mining algorithm D. Bui et al. 10.1016/j.scitotenv.2020.136836
56. Evaluation of the prediction capability of AHP and F-AHP methods in flood susceptibility mapping of Ernakulam district (India) R. Vilasan & V. Kapse 10.1007/s11069-022-05248-4
57. Spring protection and sustainable management of groundwater resources in a spring field Q. Luo et al. 10.1016/j.jhydrol.2019.124498
58. Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping P. Nguyen et al. 10.3390/app10072469
59. Groundwater potentiality mapping using ensemble machine learning algorithms for sustainable groundwater management S. Sarkar et al. 10.1108/FEBE-09-2021-0044
60. Spatial prediction of groundwater potentiality using ANFIS ensembled with teaching-learning-based and biogeography-based optimization W. Chen et al. 10.1016/j.jhydrol.2019.03.013
61. Groundwater Potential Mapping Using an Integrated Ensemble of Three Bivariate Statistical Models with Random Forest and Logistic Model Tree Models S. Razavi-Termeh et al. 10.3390/w11081596
62. Short-term River streamflow modeling using Ensemble-based additive learner approach K. Khosravi et al. 10.1016/j.jher.2021.07.003
63. A Review of Groundwater Management Models with a Focus on IoT-Based Systems B. Aderemi et al. 10.3390/su14010148
64. Estimation of the undrained shear strength of sensitive clays using optimized inference intelligence system Q. Tran et al. 10.1007/s00521-022-06891-5
65. Groundwater salinity prediction using adaptive neuro-fuzzy inference system methods: a case study in Azarshahr, Ajabshir and Maragheh plains, Iran H. Nazari et al. 10.1007/s12665-021-09455-3
66. GIS-based multi-criteria decision making and entropy approaches for groundwater potential zones delineation E. Forootan & F. Seyedi 10.1007/s12145-021-00576-8
67. Evaluating the usage of tree-based ensemble methods in groundwater spring potential mapping W. Chen et al. 10.1016/j.jhydrol.2020.124602
68. Intelligent flow discharge computation in a rectangular channel with free overfall condition K. Khosravi et al. 10.1007/s00521-022-07112-9
69. Convolutional neural network approach for spatial prediction of flood hazard at national scale of Iran K. Khosravi et al. 10.1016/j.jhydrol.2020.125552
70. Assessment of influencing factor method for delineation of groundwater potential zones with geospatial techniques. Case study of Bostanlik district, Uzbekistan B. Alikhanov et al. 10.1016/j.gsd.2021.100548
71. Optimized Conditioning Factors Using Machine Learning Techniques for Groundwater Potential Mapping . Kalantar et al. 10.3390/w11091909
72. Application of Machine Learning Algorithms for Geogenic Radon Potential Mapping in Danyang-Gun, South Korea F. Rezaie et al. 10.3389/fenvs.2021.753028
73. Spatial modeling of radon potential mapping using deep learning algorithms M. Panahi et al. 10.1080/10106049.2021.2022011
74. Groundwater spring potential mapping using population-based evolutionary algorithms and data mining methods W. Chen et al. 10.1016/j.scitotenv.2019.05.312
75. Harris Hawks Optimization: A Novel Swarm Intelligence Technique for Spatial Assessment of Landslide Susceptibility . Bui et al. 10.3390/s19163590
76. Slope Stability Monitoring Using Novel Remote Sensing Based Fuzzy Logic H. Moayedi et al. 10.3390/s19214636
77. K-Fold and State-of-the-Art Metaheuristic Machine Learning Approaches for Groundwater Potential Modelling A. Arabameri et al. 10.1007/s11269-021-02815-5