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K-Fold and State-of-the-Art Metaheuristic Machine Learning Approaches for Groundwater Potential Modelling

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Abstract

Groundwater being an essential resource is not easily available in some parts of the world. The present study, aimed at procuring better prediction maps for groundwater potential zones, is based on a novel approach combining the use of k-fold cross-validation method and the implementation of four scenarios, each comprising of six machine learning models, ANFIS (Adaptive Neuro Fuzzy Inference System) and five other ensembles of it, ANFIS-Firefly, ANFIS-Bees, ANFIS-GA, ANFIS-DE and ANFIS-ACO. Ada Boost Model has played a vital role in determining the collinearity among the fourteen conditioning factors, which are, Lithology, Slope, TST, TRI, LULC, HAND, Curvature, Distance to Stream, Distance to Fault, Rainfall, Fault Density, Drainage Density, Elevation and Aspect. The AUCROC (Area Under Curve – Receiver Operating Characteristics) approach was employed as a model evaluation metric along with Accuracy, Sensitivity and Specificity. Among the models, ANFIS-DE showed the most promising results, acquiring the highest average values among the four scenarios for AUC (0.934), Accuracy (0.987), Sensitivity (0.985) and Specificity (0.985). Promising results of this study gives the necessary incentive for further applying this approach for groundwater zonation of other areas of the world as well as other areas of hydrogeological studies.

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References

  • Amiri B, Hossain L, Crawford JW, Wigand RT (2013) Community detection in complex networks: multi-objective enhanced firefly algorithm. Knowl-Based Syst 46:1–11. https://doi.org/10.1016/j.knosys.2013.01.004

    Article  Google Scholar 

  • Arabameri A, Lee S, Tiefenbacher JP, Ngo PTT (2020) Novel ensemble of MCDM-artificial intelligence techniques for groundwater-potential mapping in arid and semi-arid regions (Iran). Remote Sens 12:1–28. https://doi.org/10.3390/rs12030490

    Article  Google Scholar 

  • Arabameri A, Pradhan B, Rezaei K, Yamani M, Pourghasemi HR, Lombardo L (2018) Spatial modelling of gully erosion using evidential belief function, logistic regression, and a new ensemble of evidential belief function–logistic regression algorithm. Land Degrad Dev 29:4035–4049. https://doi.org/10.1002/ldr.3151

    Article  Google Scholar 

  • Arora A, Arabameri A, Pandey M, Siddiqui MA, Shukla UK, Bui DT, Mishra VN, Bhardwaj A (2021) Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the middle ganga plain, India. Sci Total Environ 750:141565. https://doi.org/10.1016/j.scitotenv.2020.141565

    Article  Google Scholar 

  • Arora A, Pandey M, Siddiqui MA, Hong H, Mishra VN (2019) Spatial flood susceptibility prediction in middle ganga plain: comparison of frequency ratio and Shannon’s entropy models. Geocarto International 0:1–32. https://doi.org/10.1080/10106049.2019.1687594

    Article  Google Scholar 

  • Berberian M, King GCP (1981) Towards a paleogeography and tectonic evolution of Iran. Can J Earth Sci 18:210–265. https://doi.org/10.1139/e81-019

    Article  Google Scholar 

  • Booker LB, Goldberg DE, Holland JH (1989) Classifier systems and genetic algorithms. Artif Intell 40:235–282. https://doi.org/10.1016/0004-3702(89)90050-7

    Article  Google Scholar 

  • Bui DT, Moayedi H, Kalantar B et al (2019) A novel swarm intelligence—Harris hawks optimization for spatial assessment of landslide susceptibility. Sensors 19:3590. https://doi.org/10.3390/s19163590

    Article  Google Scholar 

  • Cao B, Zhao J, Lv Z, Gu Y, Yang P, Halgamuge SK (2020) Multiobjective evolution of fuzzy rough neural network via distributed parallelism for stock prediction. IEEE Trans Fuzzy Syst 28:939–952

    Article  Google Scholar 

  • Chao L, Zhang K, Li Z, Zhu Y, Wang J, Yu Z (2018) Geographically weighted regression based methods for merging satellite and gauge precipitation. J Hydrol 558:275–289

    Article  Google Scholar 

  • Chen H, Qiao H, Xu L, Feng Q, Cai K (2019a) A fuzzy optimization strategy for the implementation of RBF LSSVR model in Vis–NIR analysis of pomelo maturity. IEEE Trans Ind Inf 15:5971–5979

    Article  Google Scholar 

  • Chen J, Wu H, Qian H, Li X (2018a) Challenges and prospects of sustainable groundwater management in an agricultural plain along the silk road Economic Belt, north-West China. Int J Water Res Dev 34:354–368. https://doi.org/10.1080/07900627.2016.1238348

    Article  Google Scholar 

  • Chen W, Li H, Hou E, Wang S, Wang G, Panahi M, Li T, Peng T, Guo C, Niu C, Xiao L, Wang J, Xie X, Ahmad BB (2018b) GIS-based groundwater potential analysis using novel ensemble weights-of-evidence with logistic regression and functional tree models. Sci Total Environ 634:853–867. https://doi.org/10.1016/j.scitotenv.2018.04.055

    Article  Google Scholar 

  • Chen W, Panahi M, Khosravi K, Pourghasemi HR, Rezaie F, Parvinnezhad D (2019b) Spatial prediction of groundwater potentiality using ANFIS ensembled with teaching-learning-based and biogeography-based optimization. J Hydrol 572:435–448. https://doi.org/10.1016/j.jhydrol.2019.03.013

    Article  Google Scholar 

  • Chen W, Panahi M, Pourghasemi HR (2017) Performance evaluation of GIS-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) for landslide spatial modelling. Catena 157:310–324. https://doi.org/10.1016/j.catena.2017.05.034

    Article  Google Scholar 

  • Chen W, Tsangaratos P, Ilia I, Duan Z, Chen X (2019c) Groundwater spring potential mapping using population-based evolutionary algorithms and data mining methods. Sci Total Environ 684:31–49. https://doi.org/10.1016/j.scitotenv.2019.05.312

    Article  Google Scholar 

  • Cheng X, He L, Lu H, Chen Y, Ren L (2016) Optimal water resources management and system benefit for the Marcellus shale-gas reservoir in Pennsylvania and West Virginia. J Hydrol 540:412–422

    Article  Google Scholar 

  • Das B, Pal SC (2020) Assessment of groundwater vulnerability to over-exploitation using MCDA, AHP, fuzzy logic and novel ensemble models: a case study of Goghat-I and II blocks of West Bengal, India. Environ Earth Sci 79:104. https://doi.org/10.1007/s12665-020-8843-6

    Article  Google Scholar 

  • Das B, Pal SC (2019) Combination of GIS and fuzzy-AHP for delineating groundwater recharge potential zones in the critical Goghat-II block of West Bengal, India. HydroResearch 2:21–30

    Article  Google Scholar 

  • Das B, Pal SC, Malik S, Chakrabortty R (2019) Living with floods through geospatial approach: a case study of Arambag C.D. Block of Hugli District, West Bengal, India. SN Appl Sci 1:329. https://doi.org/10.1007/s42452-019-0345-3

  • Ding L, Li S, Gao H, et al (2019) Adaptive neural network-based finite-time online optimal tracking control of the nonlinear system with dead zone. IEEE transactions on cybernetics

  • Dorigo M, Blum C (2005) Ant colony optimization theory: a survey. Theor Comput Sci 344:243–278. https://doi.org/10.1016/j.tcs.2005.05.020

    Article  Google Scholar 

  • Esmaeelzadeh SR, Adib A, Alahdin S (2014) Long-term streamflow forecasts by adaptive Neuro-fuzzy inference system using satellite images and K-fold cross-validation (case study: Dez, Iran). KSCE J Civ Eng 19:2298–2306. https://doi.org/10.1007/s12205-014-0105-2

    Article  Google Scholar 

  • Feng W, Lu H, Yao T, Yu Q (2020) Drought characteristics and its elevation dependence in the Qinghai–Tibet plateau during the last half-century. Sci Rep 10:1–11

    Article  Google Scholar 

  • Freund Y, Schapire RE (1999) A brief introduction to boosting. IJCAI Int Joint Conf Artificial Int 2:1401–1406

    Google Scholar 

  • Fu X, Yang Y (2020) Modeling and analysis of cascading node-link failures in multi-sink wireless sensor networks. Reliability Eng Syst Safety 197:106815

    Article  Google Scholar 

  • Ghorbanzadeh O, Blaschke T, Aryal J, Gholaminia K (2018) A new GIS-based technique using an adaptive neuro-fuzzy inference system for land subsidence susceptibility mapping. J Spat Sci 00:1–17. https://doi.org/10.1080/14498596.2018.1505564

    Article  Google Scholar 

  • Giordano M (2009) Global groundwater? Issues and solutions. Annu Rev Environ Resour 34:153–178. https://doi.org/10.1146/annurev.environ.030308.100251

    Article  Google Scholar 

  • Han C, Zhang B, Chen H, Wei Z, Liu Y (2019) Spatially distributed crop model based on remote sensing. Agric Water Manag 218:165–173

    Article  Google Scholar 

  • He L, Shao F, Ren L (2020) Sustainability appraisal of desired contaminated groundwater remediation strategies: an information-entropy-based stochastic multi-criteria preference model. Environment, development and sustainability 1–21

  • Holland JH (1975) An efficient genetic algorithm for the traveling salesman problem. Eur J Oper Res 145:606–617

    Google Scholar 

  • Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cyber 23:665–685. https://doi.org/10.1109/21.256541

    Article  Google Scholar 

  • Jiang P, Chen J (2016) Displacement prediction of landslide based on generalized regression neural networks with K-fold cross-validation. Neurocomputing 198:40–47. https://doi.org/10.1016/j.neucom.2015.08.118

    Article  Google Scholar 

  • Khosravi K, Panahi M, Tien Bui D (2018a) A comprehensive study of new hybrid models for adaptive Neuro-fuzzy inference system (ANFIS) with invasive weed optimization (IWO), differential evolution (DE), firefly (FA), particle swarm optimization (PSO) and bees (BA) algorithms for spatial predictio

  • Khosravi K, Panahi M, Tien Bui D (2018b) Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization. Hydrol Earth Syst Sci 22:4771–4792. https://doi.org/10.5194/hess-22-4771-2018

    Article  Google Scholar 

  • Kordestani MD, Naghibi SA, Hashemi H, Ahmadi K, Kalantar B, Pradhan B (2019) Groundwater potential mapping using a novel data-mining ensemble model. Hydrogeol J 27:211–224. https://doi.org/10.1007/s10040-018-1848-5

    Article  Google Scholar 

  • Li T, Xu M, Zhu C, Yang R, Wang Z, Guan Z (2019) A deep learning approach for multi-frame in-loop filter of HEVC. IEEE Trans Image Process 28:5663–5678

    Article  Google Scholar 

  • Ling H, Qian C, Kang W, Liang C, Chen H (2019) Combination of support vector machine and K-fold cross validation to predict compressive strength of concrete in marine environment. Constr Build Mater 206:355–363. https://doi.org/10.1016/j.conbuildmat.2019.02.071

    Article  Google Scholar 

  • Liu J, Liu Y, Wang X (2020a) An environmental assessment model of construction and demolition waste based on system dynamics: a case study in Guangzhou. Environ Sci Pollut Res 27:37237–37259

    Article  Google Scholar 

  • Liu S, Chan FT, Ran W (2016) Decision making for the selection of cloud vendor: an improved approach under group decision-making with integrated weights and objective/subjective attributes. Expert Syst Appl 55:37–47

    Article  Google Scholar 

  • Liu S, Yu W, Chan FT, Niu B (2020b) A variable weight-based hybrid approach for multi-attribute group decision making under interval-valued intuitionistic fuzzy sets. Int J Intell Syst

  • Lv Z, Li X, Lv H, Xiu W (2019) BIM big data storage in WebVRGIS. IEEE Trans Ind Inf 16:2566–2573

    Article  Google Scholar 

  • Lv Z, Qiao L (2020) Deep belief network and linear perceptron based cognitive computing for collaborative robots. Appl Soft Comput 92:106300

    Article  Google Scholar 

  • Ma H-J, Yang G-H (2015) Adaptive fault tolerant control of cooperative heterogeneous systems with actuator faults and unreliable interconnections. IEEE Trans Autom Control 61:3240–3255

    Article  Google Scholar 

  • Mamdani EH, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man Mach Stud 7:1–13. https://doi.org/10.1016/S0020-7373(75)80002-2

    Article  Google Scholar 

  • Moayedi H, Mehrabi M, Kalantar B, Abdullahi Mu’azu M, A. Rashid AS, Foong LK, Nguyen H (2019a) Novel hybrids of adaptive neuro-fuzzy inference system (ANFIS) with several metaheuristic algorithms for spatial susceptibility assessment of seismic-induced landslide. Geomatics, Natural Hazards and Risk 10:1879–1911. https://doi.org/10.1080/19475705.2019.1650126

    Article  Google Scholar 

  • Moayedi H, Osouli A, Bui DT, Kok Foong L, Nguyen H, Kalantar B (2019b) Two novel neural-evolutionary predictive techniques of dragonfly algorithm (DA) and biogeography-based optimization (BBO) for landslide susceptibility analysis. Geomatics, Natural Hazards and Risk 10:2429–2453

    Article  Google Scholar 

  • Moayedi H, Tien Bui D, Gör M, Pradhan B, Jaafari A (2019c) The feasibility of three prediction techniques of the artificial neural network, adaptive neuro-fuzzy inference system, and hybrid particle swarm optimization for assessing the safety factor of cohesive slopes. ISPRS Int J Geo Inf 8:391

    Article  Google Scholar 

  • Mogaji KA, Lim HS, Abdullah K (2014) Modeling groundwater vulnerability prediction using geographic information system (GIS)-based ordered weighted average (OWA) method and DRASTIC model theory hybrid approach. Arab J Geosci 7:5409–5429. https://doi.org/10.1007/s12517-013-1163-3

    Article  Google Scholar 

  • Moghaddam DD, Rahmati O, Panahi M, Tiefenbacher J, Darabi H, Haghizadeh A, Haghighi AT, Nalivan OA, Tien Bui D (2020) The effect of sample size on different machine learning models for groundwater potential mapping in mountain bedrock aquifers. Catena 187:104421. https://doi.org/10.1016/j.catena.2019.104421

    Article  Google Scholar 

  • Moghaddam DD, Rezaei M, Pourghasemi HR, Pourtaghie ZS, Pradhan B (2013) Groundwater spring potential mapping using bivariate statistical model and GIS in the Taleghan watershed, Iran. Arab J Geosci 8:913–929. https://doi.org/10.1007/s12517-013-1161-5

    Article  Google Scholar 

  • Nguyen H, Mehrabi M, Kalantar B, Moayedi H, Abdullahi M’M (2019) Potential of hybrid evolutionary approaches for assessment of geo-hazard landslide susceptibility mapping. Geomatics, Natural Hazards and Risk 10:1667–1693. https://doi.org/10.1080/19475705.2019.1607782

    Article  Google Scholar 

  • Nobre AD, Cuartas LA, Hodnett M, Rennó CD, Rodrigues G, Silveira A, Waterloo M, Saleska S (2011) Height above the nearest drainage - a hydrologically relevant new terrain model. J Hydrol 404:13–29. https://doi.org/10.1016/j.jhydrol.2011.03.051

    Article  Google Scholar 

  • Pal S, Ghosh, Chowdhuri I (2020) Assessment of groundwater potentiality using geospatial techniques in Purba Bardhaman district, West Bengal. Applied Water Science 10: https://doi.org/10.1007/s13201-020-01302-3

  • Pham BT, Jaafari A, Prakash I, Singh SK, Quoc NK, Bui DT (2019) Hybrid computational intelligence models for groundwater potential mapping. Catena 182:104101. https://doi.org/10.1016/j.catena.2019.104101

    Article  Google Scholar 

  • Pham DT, Castellani M, Sholedolu M, Ghanbarzadeh A (2008) The bees algorithm. 250–255

  • Qian J, Feng S, Tao T, Hu Y, Li Y, Chen Q, Zuo C (2020) Deep-learning-enabled geometric constraints and phase unwrapping for single-shot absolute 3D shape measurement. APL Photonics 5:046105

    Article  Google Scholar 

  • Rahmati O, Naghibi SA, Shahabi H, Bui DT, Pradhan B, Azareh A, Rafiei-Sardooi E, Samani AN, Melesse AM (2018) Groundwater spring potential modelling: comprising the capability and robustness of three different modeling approaches. J Hydrol 565:248–261. https://doi.org/10.1016/j.jhydrol.2018.08.027

    Article  Google Scholar 

  • Rahmati O, Nazari Samani A, Mahdavi M, Pourghasemi HR, Zeinivand H (2015) Groundwater potential mapping at Kurdistan region of Iran using analytic hierarchy process and GIS. Arab J Geosci 8:7059–7071. https://doi.org/10.1007/s12517-014-1668-4

    Article  Google Scholar 

  • Riley SJ, DeGloria SD, Elliot R (1999) Terrain_Ruggedness_Index.Pdf. Intermountain J Sci 5:23–27

    Google Scholar 

  • Saeidian B, Mesgari MS, Ghodousi M (2016) Evaluation and comparison of genetic algorithm and bees algorithm for location-allocation of earthquake relief centers. Int J Disaster Risk Reduction 15:94–107. https://doi.org/10.1016/j.ijdrr.2016.01.002

    Article  Google Scholar 

  • Saha A, Pal SC, Arabameri A, Blaschke T, Panahi S, Chowdhuri I, Chakrabortty R, Costache R, Arora A (2021) Flood susceptibility assessment using novel Ensemble of Hyperpipes and Support Vector Regression Algorithms. Water 13:241. https://doi.org/10.3390/w13020241

    Article  Google Scholar 

  • Seyoum WM, Kwon D, Milewski AM (2019) Downscaling GRACE TWSA data into high-resolution groundwater level anomaly using machine learning-based models in a glacial aquifer system Remote Sensing 11:. https://doi.org/10.3390/rs11070824

  • Shekhar S, Pandey AC (2015) Delineation of groundwater potential zone in hard rock terrain of India using remote sensing, geographical information system (GIS) and analytic hierarchy process (AHP) techniques. Geocarto Int 30:402–421. https://doi.org/10.1080/10106049.2014.894584

    Article  Google Scholar 

  • Storn R, Price K (1997) Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11:341–359. https://doi.org/10.1023/A:1008202821328

    Article  Google Scholar 

  • Sugeno M, Kang GT (1988) Structure identification of fuzzy model. Fuzzy Sets Syst 28:15–33. https://doi.org/10.1016/0165-0114(88)90113-3

    Article  Google Scholar 

  • Sun G, Li C, Deng L (2021) An adaptive regeneration framework based on search space adjustment for differential evolution. Neural computing and applications 1–17

  • Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. Readings Fuzzy Sets Intell Syst:387–403. https://doi.org/10.1016/b978-1-4832-1450-4.50045-6

  • Tian P, Lu H, Feng W, Guan Y, Xue Y (2020) Large decrease in streamflow and sediment load of Qinghai–Tibetan plateau driven by future climate change: a case study in Lhasa River basin. CATENA 187:104340

    Article  Google Scholar 

  • Wada Y, Van Beek LPH, Van Kempen CM et al (2010) Global depletion of groundwater resources. Geophys Res Lett 37:1–5. https://doi.org/10.1029/2010GL044571

    Article  Google Scholar 

  • Waikar ML, Nilawar AP (2014) Identification of Groundwater Potential Zone using Remote Sensing and GIS Technique 3:12163–12174

  • Wang J, Zhu P, He B, et al (2020a) An adaptive neural sliding mode control with ESO for uncertain nonlinear systems. International journal of control, automation and systems 1–11

  • Wang S, Zhang K, van Beek LP et al (2020b) Physically-based landslide prediction over a large region: scaling low-resolution hydrological model results for high-resolution slope stability assessment. Environ Model Softw 124:104607

    Article  Google Scholar 

  • Wong TT, Yang NY (2017) Dependency analysis of accuracy estimates in k-fold cross validation. IEEE Trans Knowl Data Eng 29:2417–2427. https://doi.org/10.1109/TKDE.2017.2740926

    Article  Google Scholar 

  • Wu T, Cao J, Xiong L, Zhang H (2019) New stabilization results for semi-Markov chaotic systems with fuzzy sampled-data control. Complexity 2019:1–15

    Google Scholar 

  • Yan J, Pu W, Zhou S, Liu H, Bao Z (2020) Collaborative detection and power allocation framework for target tracking in multiple radar system. Inf Fusion 55:173–183

    Article  Google Scholar 

  • Yang W, Zhao Y, Wang D, Wu H, Lin A, He L (2020) Using principal components analysis and IDW interpolation to determine spatial and temporal changes of surface water quality of Xin’anjiang river in Huangshan, China. Int J Environ Res Public Health 17:2942. https://doi.org/10.3390/ijerph17082942

    Article  Google Scholar 

  • Yang XS (2010) Firefly algorithm, stochastic test functions and design optimization. Int J Bio-Inspired Comput 2:78–84. https://doi.org/10.1504/IJBIC.2010.032124

    Article  Google Scholar 

  • Yeh HF, Lee CH, Hsu KC, Chang PH (2009) GIS for the assessment of the groundwater recharge potential zone. Environ Geol 58:185–195. https://doi.org/10.1007/s00254-008-1504-9

    Article  Google Scholar 

  • Zadeh LA, Klir GJ, Yuan B (1996) Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers. World Scientific

  • Zhang K, Ruben GB, Li X, Li Z, Yu Z, Xia J, Dong Z (2020) A comprehensive assessment framework for quantifying climatic and anthropogenic contributions to streamflow changes: a case study in a typical semi-arid North China basin. Environ Model Softw 128:104704

    Article  Google Scholar 

  • Zhang K, Wang Q, Chao L, Ye J, Li Z, Yu Z, Yang T, Ju Q (2019) Ground observation-based analysis of soil moisture spatiotemporal variability across a humid to semi-humid transitional zone in China. J Hydrol 574:903–914

    Article  Google Scholar 

  • Zhang X, Wang Y, Chen X et al (2018) Decentralized adaptive neural approximated inverse control for a class of large-scale nonlinear hysteretic systems with time delays. IEEE Trans Syst Man Cyber Syst 49:2424–2437

    Article  Google Scholar 

  • Zhu J, Wang X, Chen M, Wu P, Kim MJ (2019) Integration of BIM and GIS: IFC geometry transformation to shapefile using enhanced open-source approach. Autom Constr 106:102859. https://doi.org/10.1016/j.autcon.2019.102859

    Article  Google Scholar 

  • Zhu J, Wu P, Chen M, Kim MJ, Wang X, Fang T (2020) Automatically processing IFC clipping representation for BIM and GIS integration at the process level. Appl Sci 10:2009

    Article  Google Scholar 

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The models and code that support the findings of this study are available from the corresponding author upon reasonable request.

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Authors and Affiliations

  1. Department of Geomorphology, Tarbiat Modares University, Tehran, 14117-13116, Iran

    Alireza Arabameri

  2. University Center for Research & Development (UCRD), Chandigarh University, Mohali, Punjab, 140413, India

    Aman Arora & Satarupa Mitra

  3. Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, 110025, India

    Aman Arora

  4. Department of Geography, The University of Burdwan, Bardhaman, West Bengal, India

    Subodh Chandra Pal & Asish Saha

  5. Department of Watershed Management, Gorgan University of Agricultural Sciences and Natural Resources (GUASNR), Gorgan, 3184761174, Iran

    Omid Asadi Nalivan

  6. Department of Computer Engineering, Faculty of Valiasr, Technical and Vocational University (TVU), Tehran Branch, Tehran, Iran

    Somayeh Panahi

  7. Informetrics Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Hossein Moayedi

  8. Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Hossein Moayedi

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  1. Alireza Arabameri
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Contributions

Material preparation, data collection and analysis were performed by [Alireza Arabameri], and [Omid Asadi Nalivan], The first draft of the manuscript was written by [Aman Arora], [Satarupa Mitra], [Alireza Arabameri], [Subodh Chandra Pal], and [Asish Saha]. [Somayeh Panahi], and [Hossein Moayedi] commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Alireza Arabameri.

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Arabameri, A., Arora, A., Pal, S.C. et al. K-Fold and State-of-the-Art Metaheuristic Machine Learning Approaches for Groundwater Potential Modelling. Water Resour Manage 35, 1837–1869 (2021). https://doi.org/10.1007/s11269-021-02815-5

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