Articles | Volume 22, issue 9
https://doi.org/10.5194/hess-22-4771-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-22-4771-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization
Khabat Khosravi
Department of Watershed Management Engineering, Faculty of Natural
Resources, Sari Agricultural Science and Natural Resources University, Sari,
Iran
Young Researchers and Elites Club, North Tehran Branch, Islamic Azad
University, Tehran, Iran
Dieu Tien Bui
CORRESPONDING AUTHOR
GIS group, Department of Business and IT, University of South-Eastern
Norway, Gullbringvegen 36, 3800 Bø i Telemark, Norway
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133 citations as recorded by crossref.
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- A Novel Swarm Intelligence—Harris Hawks Optimization for Spatial Assessment of Landslide Susceptibility D. Bui et al. 10.3390/s19163590
- A tree-based intelligence ensemble approach for spatial prediction of potential groundwater M. Avand et al. 10.1080/17538947.2020.1718785
- Comparisons of Diverse Machine Learning Approaches for Wildfire Susceptibility Mapping K. Gholamnia et al. 10.3390/sym12040604
- 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
- Mapping groundwater potentiality by using hybrid machine learning models under the scenario of climate variability: a national level study of Bangladesh S. Sarkar et al. 10.1007/s10668-024-04687-2
- 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
- Multi-Hazard Exposure Mapping Using Machine Learning for the State of Salzburg, Austria T. Nachappa et al. 10.3390/rs12172757
- Integrating Digital Twins and Artificial Intelligence Multi-Modal Transformers into Water Resource Management: Overview and Advanced Predictive Framework T. Syed et al. 10.3390/ai5040098
- 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
- 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
- Hybrid and Integrative Evolutionary Machine Learning in Hydrology: A Systematic Review and Meta-analysis A. Mahdavi-Meymand et al. 10.1007/s11831-023-10017-y
- 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
- Flood susceptibility mapping using multi-temporal SAR imagery and novel integration of nature-inspired algorithms into support vector regression S. Mehravar et al. 10.1016/j.jhydrol.2023.129100
- 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
- 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
- Is multi-hazard mapping effective in assessing natural hazards and integrated watershed management? H. Pourghasemi et al. 10.1016/j.gsf.2019.10.008
- Groundwater Potential Mapping Using GIS‐Based Hybrid Artificial Intelligence Methods T. Phong et al. 10.1111/gwat.13094
- Radon potential mapping in Jangsu-gun, South Korea using probabilistic and deep learning algorithms F. Rezaie et al. 10.1016/j.envpol.2021.118385
- A comparison of machine learning models for the mapping of groundwater spring potential A. Al-Fugara et al. 10.1007/s12665-020-08944-1
- Spatio-temporal cross-validation to predict pluvial flood events in the Metropolitan City of Venice Z. Marco et al. 10.1016/j.jhydrol.2022.128150
- Artificial intelligence algorithms for prediction and sensitivity analysis of mechanical properties of recycled aggregate concrete: A review T. Nguyen et al. 10.1016/j.jobe.2023.105929
- A new approach based on biology-inspired metaheuristic algorithms in combination with random forest to enhance the flood susceptibility mapping S. Razavi-Termeh et al. 10.1016/j.jenvman.2023.118790
- Monthly suspended sediment load prediction using artificial intelligence: testing of a new random subspace method V. Nhu et al. 10.1080/02626667.2020.1754419
- 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
- Experimental-based groundwater salinization from the carbonate aquifer of eastern Saudi Arabia: Insight into machine learning coupled with meta-heuristic algorithms M. Benaafi et al. 10.1016/j.chemolab.2024.105135
- Past, Present, and Future of Using Neuro-Fuzzy Systems for Hydrological Modeling and Forecasting Y. Ang et al. 10.3390/hydrology10020036
- 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
- Improving groundwater potential mapping using metaheuristic approaches S. Razavi-Termeh et al. 10.1080/02626667.2020.1828589
- A Method Combining Seepage Theory and Model Simulation for the Identification of Potential Groundwater Resources K. Wang & D. Shih 10.1061/(ASCE)HE.1943-5584.0002223
- Hybrid-based approaches for the flood susceptibility prediction of Kermanshah province, Iran S. Paryani et al. 10.1007/s11069-022-05701-4
- Enhancing spatial prediction of groundwater-prone areas through optimization of a boosting algorithm with bio-inspired metaheuristic algorithms S. Razavi-Termeh et al. 10.1007/s13201-024-02301-4
- Mapping Fire Susceptibility in the Brazilian Amazon Forests Using Multitemporal Remote Sensing and Time-Varying Unsupervised Anomaly Detection A. Luz et al. 10.3390/rs14102429
- 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
- Computational Machine Learning Approach for Flood Susceptibility Assessment Integrated with Remote Sensing and GIS Techniques from Jeddah, Saudi Arabia A. Al-Areeq et al. 10.3390/rs14215515
- Comparison of Novel Hybrid and Benchmark Machine Learning Algorithms to Predict Groundwater Potentiality: Case of a Drought-Prone Region of Medjerda Basin, Northern Tunisia F. Trabelsi et al. 10.3390/rs15010152
- Enhancing nitrate and strontium concentration prediction in groundwater by using new data mining algorithm D. Bui et al. 10.1016/j.scitotenv.2020.136836
- Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping P. Nguyen et al. 10.3390/app10072469
- Groundwater potentiality mapping using ensemble machine learning algorithms for sustainable groundwater management S. Sarkar et al. 10.1108/FEBE-09-2021-0044
- 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
- Earth Observation in the EMMENA Region: Scoping Review of Current Applications and Knowledge Gaps M. Eliades et al. 10.3390/rs15174202
- 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
- A Review of Groundwater Management Models with a Focus on IoT-Based Systems B. Aderemi et al. 10.3390/su14010148
- Estimation of the undrained shear strength of sensitive clays using optimized inference intelligence system Q. Tran et al. 10.1007/s00521-022-06891-5
- Modeling the total hardness (TH) of groundwater in aquifers using novel hybrid soft computing optimizer models H. Moayedi et al. 10.1007/s12665-024-11618-x
- Simulation-optimization model for groundwater replenishment from the river: a case study in the Hutuo River alluvial fan, China P. Zhang et al. 10.2166/ws.2022.280
- Application of hybrid model-based machine learning for groundwater potential prediction in the north central of Vietnam H. Nguyen et al. 10.1007/s12145-023-01209-y
- 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
- Optimized Conditioning Factors Using Machine Learning Techniques for Groundwater Potential Mapping B. Kalantar et al. 10.3390/w11091909
- Application of Machine Learning Algorithms for Geogenic Radon Potential Mapping in Danyang-Gun, South Korea F. Rezaie et al. 10.3389/fenvs.2021.753028
- Spatial modeling of radon potential mapping using deep learning algorithms M. Panahi et al. 10.1080/10106049.2021.2022011
- Groundwater spring potential mapping using population-based evolutionary algorithms and data mining methods W. Chen et al. 10.1016/j.scitotenv.2019.05.312
- 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
- Mapping Burned Areas with Multitemporal–Multispectral Data and Probabilistic Unsupervised Learning R. Negri et al. 10.3390/rs14215413
- Advanced machine learning for predicting groundwater decline and drought in the Rabat–Salé–Kénitra region, Morocco A. Elmotawakkil & N. Enneya 10.2166/hydro.2024.328
- Bedload transport rate prediction: Application of novel hybrid data mining techniques K. Khosravi et al. 10.1016/j.jhydrol.2020.124774
- Determination of compound channel apparent shear stress: application of novel data mining models Z. Khozani et al. 10.2166/hydro.2019.037
- Predictive performance assessment of recycled coarse aggregate concrete using artificial intelligence: A review P. Kumari et al. 10.1016/j.clema.2024.100263
- Multi-hazard probability assessment and mapping in Iran H. Pourghasemi et al. 10.1016/j.scitotenv.2019.07.203
- 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
- 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
- Groundwater spring potential prediction using a deep-learning algorithm S. Moughani et al. 10.1007/s11600-023-01053-0
- 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
- Stochastic Modeling of Groundwater Fluoride Contamination: Introducing Lazy Learners K. Khosravi et al. 10.1111/gwat.12963
- 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
- Prediction of embankments dam break peak outflow: a comparison between empirical equations and ensemble-based machine learning algorithms K. Khosravi et al. 10.1007/s11069-023-06060-4
- Solving water scarcity challenges in arid regions: A novel approach employing human-based meta-heuristics and machine learning algorithm for groundwater potential mapping S. Razavi-Termeh et al. 10.1016/j.chemosphere.2024.142859
- Groundwater spring potential zonation using AHP and fuzzy-AHP in Eastern Himalayan region: Papum Pare district, Arunachal Pradesh, India P. Ranjan et al. 10.1007/s11356-023-26769-w
- Delineating flood-prone areas using advanced integration of reduced-error pruning tree with different ensemble classifier algorithms E. Nohani et al. 10.1007/s11600-023-01238-7
- Identification of landslide-prone zones using a GIS-based multi-criteria decision analysis and region-growing algorithm in uncertain conditions S. Beheshtifar 10.1007/s11069-022-05603-5
- Suspended sediment load modeling using advanced hybrid rotation forest based elastic network approach K. Khosravi et al. 10.1016/j.jhydrol.2022.127963
- 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
- Enhancing the predictive accuracy of recycled aggregate concrete’s strength using machine learning and statistical approaches: a review J. Tariq et al. 10.1007/s42107-024-01192-9
- Integration of group method of data handling (GMDH) algorithm and population-based metaheuristic algorithms for spatial prediction of potential groundwater S. Amiri-Doumari et al. 10.1007/s12665-022-10593-5
- A country-wide assessment of Iran's land subsidence susceptibility using satellite-based InSAR and machine learning M. Panahi et al. 10.1080/10106049.2022.2086631
- Daily streamflow prediction using optimally pruned extreme learning machine R. Adnan et al. 10.1016/j.jhydrol.2019.123981
- Spatial prediction of groundwater potentiality using machine learning methods with Grey Wolf and Sparrow Search Algorithms R. Liu et al. 10.1016/j.jhydrol.2022.127977
- 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
- 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
- Enhanced machine learning model to estimate groundwater spring potential based on digital elevation model parameters M. Msaddek et al. 10.1080/10106049.2021.2007292
- New hybrid-based approach for improving the accuracy of coastal aquifer vulnerability assessment maps K. Khosravi et al. 10.1016/j.scitotenv.2021.145416
- River Water Salinity Prediction Using Hybrid Machine Learning Models A. Melesse et al. 10.3390/w12102951
- Soil water erosion susceptibility assessment using deep learning algorithms K. Khosravi et al. 10.1016/j.jhydrol.2023.129229
- 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
- A New Approach Based on Deep Neural Networks and Multisource Geospatial Data for Spatial Prediction of Groundwater Spring Potential V. Nhu et al. 10.1109/ACCESS.2024.3360337
- 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
- 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
- Leveraging machine learning in porous media M. Delpisheh et al. 10.1039/D4TA00251B
- SEVUCAS: A Novel GIS-Based Machine Learning Software for Seismic Vulnerability Assessment S. Lee et al. 10.3390/app9173495
- Groundwater spring potential assessment using new ensemble data mining techniques S. Yousefi et al. 10.1016/j.measurement.2020.107652
- 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
- Spatial mapping of water spring potential using four data mining models A. Al-Shabeeb et al. 10.2166/ws.2023.087
- Optimization of statistical and machine learning hybrid models for groundwater potential mapping P. Yariyan et al. 10.1080/10106049.2020.1870164
- 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
- Potentialities and development of groundwater resources applying machine learning models in the extended section of Manbhum-Singhbhum Plateau, India A. Ghosh & B. Bera 10.1016/j.hydres.2023.11.002
- Optimization of an adaptive neuro-fuzzy inference system for groundwater potential mapping S. Termeh et al. 10.1007/s10040-019-02017-9
- Combination of Metaheuristic Optimization Algorithms and Machine Learning Methods for Groundwater Potential Mapping S. AlAyyash et al. 10.3390/su15032499
- Slope Stability Monitoring Using Novel Remote Sensing Based Fuzzy Logic H. Moayedi et al. 10.3390/s19214636
- A novel per pixel and object-based ensemble approach for flood susceptibility mapping T. Gudiyangada Nachappa & S. Meena 10.1080/19475705.2020.1833990
- Model identification and accuracy for estimation of suspended sediment load K. Khosravi et al. 10.1080/10106049.2022.2142964
- Groundwater-Potential Mapping Using a Self-Learning Bayesian Network Model: A Comparison among Metaheuristic Algorithms S. Karimi-Rizvandi et al. 10.3390/w13050658
- 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
- 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
- Predicting groundwater phosphate levels in coastal multi-aquifers: A geostatistical and data-driven approach M. Mamun et al. 10.1016/j.scitotenv.2024.176024
- 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
- Spatial variability of soil water erosion: Comparing empirical and intelligent techniques A. Golkarian et al. 10.1016/j.gsf.2022.101456
- Spring protection and sustainable management of groundwater resources in a spring field Q. Luo et al. 10.1016/j.jhydrol.2019.124498
- Groundwater prospective mapping using remote sensing and GIS techniques: the case of Meki watershed in Central Rift Valley, Ethiopia D. Doyo et al. 10.1007/s40899-022-00775-1
- Groundwater level dynamics in a subtropical fan delta region and its future prediction using machine learning tools: Sustainable groundwater restoration S. Mahammad et al. 10.1016/j.ejrh.2023.101385
- Short-term River streamflow modeling using Ensemble-based additive learner approach K. Khosravi et al. 10.1016/j.jher.2021.07.003
- Fluvial bedload transport modelling: advanced ensemble tree-based models or optimized deep learning algorithms? K. Khosravi et al. 10.1080/19942060.2024.2346221
- 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
- Groundwater potential delineation using geodetector based convolutional neural network in the Gunabay watershed of Ethiopia A. Tegegne et al. 10.1016/j.envres.2023.117790
- Global review of groundwater potential models in the last decade: Parameters, model techniques, and validation N. Thanh et al. 10.1016/j.jhydrol.2022.128501
- GIS-based multi-criteria decision making and entropy approaches for groundwater potential zones delineation E. Forootan & F. Seyedi 10.1007/s12145-021-00576-8
- Groundwater spring potential mapping: Assessment the contribution of hydrogeological factors R. Zhao et al. 10.1016/j.asr.2024.03.038
- Evaluating the usage of tree-based ensemble methods in groundwater spring potential mapping W. Chen et al. 10.1016/j.jhydrol.2020.124602
- Intelligent flow discharge computation in a rectangular channel with free overfall condition K. Khosravi et al. 10.1007/s00521-022-07112-9
- A hybrid framework based on LSTM for predicting karst spring discharge using historical data W. Zhang et al. 10.1016/j.jhydrol.2024.130946
- 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
- Predicting Landslides Susceptible Zones in the Lesser Himalayas by Ensemble of Per Pixel and Object-Based Models U. Sur et al. 10.3390/rs14081953
- A New Method for Determination of Optimal Borehole Drilling Location Considering Drilling Cost Minimization and Sustainable Groundwater Management A. Khan et al. 10.1021/acsomega.2c06854
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Latest update: 23 Nov 2024