Skip to main content

Advertisement

SpringerLink
Log in

Spatial Prediction of the Groundwater Potential Using Remote Sensing Data and Bivariate Statistical-Based Artificial Intelligence Models

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

Evaluation of the groundwater potential in a given region must be performed in groundwater resource management. The main purpose of this research was to simulate the groundwater potential in Wuqi County, China, based on five artificial intelligence models. The five data mining methods included the decision forest by penalizing attributes (forest PA) model, rudiment of the radial basis function network (RBFN) model, certainty factor (CF) model, logistic regression (LR) model and naïve Bayes (NB) model. According to local geographic environment characteristics, a total of sixteen conditioning factors were selected, the two-variable CF model was used to calculate the weight of factor subclasses, and multicollinearity analysis and the SVM method with different filter types were used to assess the independence and importance, respectively, of the conditioning factors. In addition, to obtain better model prediction results, the relevant modeling parameters of the forest PA and RBFN models were optimized via the tenfold cross-validation method in the modeling process. Finally, corresponding groundwater potential maps (GWPMs) of each model were generated, groundwater areas with a high potential were identified, and the consistency between the groundwater potential maps was compared by calculating the kappa statistical index. The area under the receiver operating characteristic curve (AUROC) was used to verify the accuracy and success rate of each model. The Wilcoxon signed-rank test method was also used to evaluate the significance of several benchmark numerical models. The results indicated that all five models achieved a satisfactory performance, and the models with optimized parameters (forest PA and RBFN models) realized greater predictive capabilities than those of the other unoptimized models. The results of this study could provide a certain guiding significance for rational management of groundwater systems and could contribute to the formulation of measures to ensure the best future use of groundwater energy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  • Adnan MN, Islam MZ (2017) Forest Pa: Constructing a decision forest by penalizing attributes used in previous trees. Expert Syst Appl 89:389–403. https://doi.org/10.1016/j.eswa.2017.08.002

    Article  Google Scholar 

  • Agarwal R, Garg P (2016) Remote sensing and Gis based groundwater potential & recharge zones mapping using multi-criteria decision making technique. Water Resour Manage 30:243–260

    Article  Google Scholar 

  • Al-Abadi AM (2015) Groundwater potential mapping at Northeastern Wasit and Missan Governorates, Iraq using a data-driven weights of evidence technique in framework of Gis. Environ Earth Sci 74:1109–1124

    Article  Google Scholar 

  • Alin A (2010) Multicollinearity. Wiley Interdiscip Rev Comput Stat 2:370–374

    Article  Google Scholar 

  • Althuwaynee OF, Pradhan B, Park H-J, Lee JH (2014) A novel ensemble decision tree-based Chi-Squared Automatic Interaction Detection (Chaid) and multivariate logistic regression models in landslide susceptibility mapping. Landslides 11:1063–1078

    Article  Google Scholar 

  • Altman DG (1990) Practical statistics for medical research. CRC Press

    Book  Google Scholar 

  • Aniya M (1985) Landslide-susceptibility mapping in the Amahata River Basin, Japan. Ann Assoc Am Geogr 75:102–114

    Article  Google Scholar 

  • Arabameri A, Pourghasemi HR (2019) Spatial modeling of gully erosion using linear and quadratic discriminant analyses in Gis and R. Spatial modeling in Gis and R for earth and environmental sciences. Elsevier, pp 299–321

  • Arora R (2015) Multicollinearity: Detection and some solutions. In: Rogers III JC, Lamb JCW (eds), Proceedings of the 1983 Academy of Marketing Science (AMS) Annual Conference. Springer International Publishing, Cham, pp 501–505

  • Basak A, Das J, Rahman ATMS, Pham QB (2021) An integrated approach for delineating and characterizing groundwater depletion hotspots in a Coastal State of India. J Geol Soc India 97:1429–1440. https://doi.org/10.1007/s12594-021-1883-z

    Article  Google Scholar 

  • Beheshtirad HR, Masood P (2015) Assessment of a data-driven evidential belief function model and gis for groundwater potential mapping in the Koohrang Watershed, Iran. GeoIn 30:662–685

    Google Scholar 

  • Bianchini M, Frasconi P, Gori M (1995) Learning without local minima in radial basis function networks. IEEE Trans Neural Networks 6:749–756. https://doi.org/10.1109/72.377979

    Article  Google Scholar 

  • Bischof R, Loe LE, Meisingset EL, Zimmermann B, Van Moorter B, Mysterud A (2012) a migratory northern ungulate in the pursuit of spring: Jumping or surfing the green wave? Am Nat 180:407–424

    Article  Google Scholar 

  • Bui DT, Ngo P-TT, Pham TD, Jaafari A, Minh NQ, Hoa PV, Samui P (2019) A novel hybrid approach based on a swarm intelligence optimized extreme learning machine for flash flood susceptibility mapping. CATENA 179:184–196

    Article  Google Scholar 

  • Bui DT, Pradhan B, Revhaug I, Nguyen DB, Pham HV, Bui QN (2015) A novel hybrid evidential belief function-based fuzzy logic model in spatial prediction of rainfall-induced shallow landslides in the Lang Son City Area (Vietnam). Geomat Nat Haz Risk 6:243–271

    Article  Google Scholar 

  • Burdsall B, Giraud-Carrier C (1998) Ga-Rbf: a self-optimising Rbf network. In: Smith GD, Steele NC, Albrecht RF (eds) Artificial neural nets and genetic algorithms. Springer Vienna, Vienna, pp 346–349

    Chapter  Google Scholar 

  • Centor R, Keightley G (1989) Receiver Operating Characteristics (Roc) curve area analysis using the Roc analyzer. Proceedings Symposium on Computer Applications in Medical Care, pp 222–226

  • Chen W, Hong H, Panahi M, Shahabi H, Wang Y, Shirzadi A, Pirasteh S, Alesheikh AA, Khosravi K, Panahi S (2019a) Spatial prediction of landslide susceptibility using Gis-based data mining techniques of Anfis with Whale Optimization Algorithm (Woa) and Grey Wolf Optimizer (Gwo). Appl Sci 9:3755

    Article  Google Scholar 

  • Chen W, Tsangaratos P, Ilia I, Duan Z, Chen X (2019b) 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 

  • Chen W, Xie X, Peng J, Wang J, Duan Z, Hong H (2017) Gis-based landslide susceptibility modelling: a comparative assessment of kernel logistic regression, Na Ve-Bayes tree, and alternating decision tree models. Geomat Nat Haz Risk 8:950–973

    Article  Google Scholar 

  • Chowdhury A, Jha M, Chowdary V, Mal B (2009) Integrated remote sensing and GIS-based approach for assessing groundwater potential in West Medinipur District, West Bengal, India. Int J Remote Sens 30:231–250

    Article  Google Scholar 

  • Chung C-JF, Fabbri AG (1993) The representation of geoscience information for data integration. Nonrenewable Resour 2:122–139

    Article  Google Scholar 

  • Cook RJ (2005) Kappa and its dependence on marginal rates. Encyclopedia of Biostatistics 4

  • Corsini A, Cervi F, Ronchetti F (2009) Weight of evidence and artificial neural networks for potential groundwater spring mapping: an application to the Mt. Modino Area (Northern Apennines, Italy). Geomorphology 111:79–87

    Article  Google Scholar 

  • Daoud JI (2017) Multicollinearity and regression analysis. Journal of Physics: Conference Series. IOP Publishing, p 012009

  • Díaz-Alcaide S, Martínez-Santos P (2019) Review: Advances in groundwater potential mapping. Hydrogeol J 27:2307–2324. https://doi.org/10.1007/s10040-019-02001-3

    Article  Google Scholar 

  • Dinesh Kumar P, Gopinath G, Seralathan P (2007) Application of remote sensing and Gis for the demarcation of groundwater potential zones of a river basin in Kerala, Southwest Coast of India. Int J Remote Sens 28:5583–5601

    Article  Google Scholar 

  • Evans M (2012) A partial least squares solution to the problem of multicollinearity when predicting the high temperature properties of 1cr–1mo–0.25v steel using parametric models. J Mater Sci 47:2712–2724. https://doi.org/10.1007/s10853-011-6097-0

    Article  Google Scholar 

  • Fashae OA, Tijani MN, Talabi AO, Adedeji OI (2014) Delineation of groundwater potential zones in the crystalline basement terrain of Sw-Nigeria: an integrated Gis and remote sensing approach. Appl Water Sci 4:19–38

    Article  Google Scholar 

  • Fleiss JL, Levin B, Paik MC (2013) Statistical methods for rates and proportions. John Wiley & Sons

  • Frank E, Trigg L, Holmes G, Witten IH (2000) Naive Bayes for regression. Mach Learn 41:5–25

    Article  Google Scholar 

  • Ghosh B (2021) Spatial mapping of groundwater potential using data-driven evidential belief function, knowledge-based analytic hierarchy process and an ensemble approach. Environ Earth Sci 80:625. https://doi.org/10.1007/s12665-021-09921-y

    Article  Google Scholar 

  • Guru B, Seshan K, Bera S (2016) Frequency ratio model for groundwater potential mapping and its sustainable management in cold desert, India. J King Saud Univ Sci

  • Hakim WL, Nur AS, Rezaie F, Panahi M, Lee C-W, Lee S (2022) Convolutional neural network and long short-term memory algorithms for groundwater potential mapping in Anseong, South Korea. J Hydrol Region Stud 39:100990. https://doi.org/10.1016/j.ejrh.2022.100990

    Article  Google Scholar 

  • He Q, Shahabi H, Shirzadi A, Li S, Chen W, Wang N, Chai H, Bian H, Ma J, Chen Y, Wang X, Chapi K, Ahmad BB (2019) Landslide spatial modelling using novel bivariate statistical Based Naïve Bayes, Rbf classifier, and Rbf network machine learning algorithms. ScTEn 663:1–15. https://doi.org/10.1016/j.scitotenv.2019.01.329

    Article  Google Scholar 

  • Heckerman D (1986) Probabilistic Interpretations for Mycin’s Certainty Factors 4:167–196

    Google Scholar 

  • Ibrahim-Bathis K, Ahmed S (2016) Geospatial technology for delineating groundwater potential zones in Doddahalla watershed of Chitradurga District, India. Egypt J Remote Sens Space Sci 19:223–234

    Google Scholar 

  • Jaafari A, Termeh SVR, Bui DT (2019a) Genetic and firefly metaheuristic algorithms for an optimized neuro-fuzzy prediction modeling of wildfire probability. J Environ Manage 243:358–369

    Article  Google Scholar 

  • Jaafari A, Zenner EK, Panahi M, Shahabi H (2019b) Hybrid artificial intelligence models based on a neuro-fuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability. Agric Meteorol 266:198–207

    Article  Google Scholar 

  • Jaiswal RK, Krishnamurthy J, Mukherjee S (2005) Regional study for mapping the natural resources prospect and problem zones using remote sensing and Gis. Geocarto Int 20:21–31

    Article  Google Scholar 

  • Janizadeh S, Avand M, Jaafari A, Phong TV, Bayat M, Ahmadisharaf E, Prakash I, Pham BT, Lee S (2019) Prediction success of machine learning methods for flash flood susceptibility mapping in the Tafresh Watershed, Iran. Sustainability 11:5426

    Article  Google Scholar 

  • Kalantar B, Al-Najjar HAH, Pradhan B, Saeidi V, Halin AA, Ueda N, Naghibi SA (2019) Optimized conditioning factors using machine learning techniques for groundwater potential mapping. Water 11:1909

    Article  Google Scholar 

  • Kamali Maskooni E, Naghibi SA, Hashemi H, Berndtsson R (2020) Application of advanced machine learning algorithms to assess groundwater potential using remote sensing-derived data. Remote Sens 12:2742

    Article  Google Scholar 

  • Khodadadzadeh M, Li J, Plaza A, Bioucas-Dias JM (2014) A subspace-based multinomial logistic regression for hyperspectral image classification. IEEE Geosci Remote Sens Lett 11:2105–2109. https://doi.org/10.1109/LGRS.2014.2320258

    Article  Google Scholar 

  • Kim J-C, Jung H-S, Lee S (2019) Spatial mapping of the groundwater potential of the Geum River Basin using ensemble models based on remote sensing images. Remote Sens 11:2285

    Article  Google Scholar 

  • Kim K-B, Kim M, Woo YW (2007) Recognition of shipping container identifiers using art2-based quantization and a refined Rbf network. In: Beliczynski B, Dzielinski A, Iwanowski M, Ribeiro B (eds) Adaptive and natural computing algorithms. Springer, Berlin Heidelberg, Berlin, Heidelberg, pp 572–581

    Chapter  Google Scholar 

  • Kondo C, Kondo T (2010) Learning algorithm of the revised Rbf network and its application to the media art system. Artif Life Robot 15:258–263. https://doi.org/10.1007/s10015-010-0804-9

    Article  Google Scholar 

  • Kontos Y (2020) Optimal management of polluted aquifers with genetic algorithms, considering possible nitrogen retrieval for reuse as fertilizer. 71/72:63–77

  • 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 

  • Kumar A, Pramanik M, Chaudhary S, Negi MS, Szabo S (2022) Geospatial multi-criteria evaluation to identify groundwater potential in a Himalayan District, Rudraprayag, India. Environ Dev Sustain. https://doi.org/10.1007/s10668-021-02107-3

  • Kumar P, Herath S, Avtar R, Takeuchi K (2016) Mapping of groundwater potential zones in Killinochi Area, Sri Lanka, using Gis and remote sensing techniques. Sustain Water Resour Manage 2:419–430

    Article  Google Scholar 

  • Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 159–174

  • Lee J-H, Sameen MI, Pradhan B, Park H-J (2018a) Modeling landslide susceptibility in data-scarce environments using optimized data mining and statistical methods. Geomorphology 303:284–298

    Article  Google Scholar 

  • Lee S, Hong S-M, Jung H-S (2018b) Gis-based groundwater potential mapping using artificial neural network and support vector machine models: the case of Boryeong City in Korea. Geocarto Int 33:847–861. https://doi.org/10.1080/10106049.2017.1303091

    Article  Google Scholar 

  • Lee S, Kim YS, Oh HJ (2012) Application of a weights-of-evidence method and Gis to regional groundwater productivity potential mapping. J Environ Manage 96:91–105

    Article  Google Scholar 

  • Lee S, Lee C-W, Kim J-C (2019) Groundwater productivity potential mapping using logistic regression and boosted tree models: the case of Okcheon City in Korea. Springer International Publishing, Cham, pp 305–307

    Google Scholar 

  • Lee S, Oh H-J (2012) Ensemble-based landslide susceptibility maps in Jinbu Area, Korea. Terrigenous Mass Movements. Springer, pp 193–220

  • Lei X, Chen W, Pham BT (2020) Performance evaluation of Gis-based artificial intelligence approaches for landslide susceptibility modeling and spatial patterns analysis. ISPRS Int J Geo Inf 9:443

    Article  Google Scholar 

  • Li H, Huang X, Huang B, Ping L (2010) Prediction of urban land use evolution using temporal remote sensing data analysis and a spatial logistic model. 2010 30th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010, July 25, 2010 - July 30, 2010. Institute of Electrical and Electronics Engineers Inc., Honolulu, HI, United states, pp 2751–2753

    Google Scholar 

  • Li J, Bioucas-Dias JM, Plaza A (2013) Semisupervised hyperspectral image classification using soft sparse multinomial logistic regression. IEEE Geosci Remote Sens Lett 10:318–322. https://doi.org/10.1109/LGRS.2012.2205216

    Article  Google Scholar 

  • Li MM, Verma B (2016) Nonlinear curve fitting to stopping power data using Rbf neural networks. Expert Syst Appl 45:161–171. https://doi.org/10.1016/j.eswa.2015.09.033

    Article  Google Scholar 

  • Lin G-F, Chang M-J, Huang Y-C, Ho J-Y (2017) Assessment of susceptibility to rainfall-induced landslides using improved self-organizing linear output map, support vector machine, and logistic regression. Eng Geol 224:62–74. https://doi.org/10.1016/j.enggeo.2017.05.009

    Article  Google Scholar 

  • Luce RD (2005) Individual choice behavior: a theoretical analysis

  • Lv G, Wang X (2006) Power quality identification based on S-Transform and Rbf neural network. In: Huang D-S, Li K, Irwin GW (eds) Intelligent computing. Springer, Berlin Heidelberg, Berlin, Heidelberg, pp 434–439

    Chapter  Google Scholar 

  • Madani A (2015) Groundwater potential mapping using remote sensing techniques and weights of evidence Gis Model: a case study from Wadi Yalamlam Basin, Makkah Province, Western Saudi Arabia. Environ Earth Sci 74:1–14

    Article  Google Scholar 

  • Manap MA, Nampak H, Pradhan B, Lee S, Sulaiman WNA, Ramli MF (2014) Application of probabilistic-based frequency ratio model in groundwater potential mapping using remote sensing data and Gis. Arab J Geosci 7:711–724

    Article  Google Scholar 

  • Manap MA, Nampak H, Pradhan B, Lee S, Wan NAS, Ramli MF (2012) Application of probabilistic-based frequency ratio model in groundwater potential mapping using remote sensing data and Gis. Arab J Geosci 7:1–14

    Google Scholar 

  • Manap MA, Sulaiman WNA, Ramli MF, Pradhan B, Surip N (2013) A knowledge-driven Gis modeling technique for groundwater potential mapping at the upper Langat Basin, Malaysia. Arab J Geosci 6:1621–1637. https://doi.org/10.1007/s12517-011-0469-2

    Article  Google Scholar 

  • Mansfield ER, Helms BP (1982) Detecting Multicollinearity. Am Stat 36:158–160

    Google Scholar 

  • McFadden D (1974) Conditional logit analysis of qualitative choice behavior, pp 105–142

  • McHugh ML (2012) Interrater reliability: the Kappa Statistic. Biochemia Medica 22:276–282

    Article  Google Scholar 

  • Miraki S, Zanganeh SH, Chapi K, Singh VP, Shirzadi A, Shahabi H, Pham BT (2019) Mapping groundwater potential using a novel hybrid intelligence approach. Water Resour Manage 33:281–302

    Article  Google Scholar 

  • Misi A, Gumindoga W, Hoko Z (2018) An Assessment of groundwater potential and vulnerability in the upper Manyame sub-catchment of Zimbabwe. Phys Chem Earth Parts a/b/c 105:72–83

    Article  Google Scholar 

  • Moayedi H, Tien Bui D, Gor M, Pradhan B, Jaafari A (2019) 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 (2015) Regional prediction of groundwater potential mapping in a multifaceted geology terrain using Gis-based Dempster-Shafer model. Arab J Geosci 8:3235–3258

    Article  Google Scholar 

  • Moghaddam DD, Rezaei M, Pourghasemi H, Pourtaghie Z, Pradhan B (2015) Groundwater spring potential mapping using bivariate statistical model and Gis in the Taleghan watershed, Iran. Arab J Geosci 8:913–929

    Article  Google Scholar 

  • Moody J, Darken CJ (1989) Fast learning in networks of locally-tuned processing units. Neural Comput 1:281–294. https://doi.org/10.1162/neco.1989.1.2.281

    Article  Google Scholar 

  • Moore ID, Grayson R, Ladson A (1991) Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrol Process 5:3–30

    Article  Google Scholar 

  • Mortezaeipooya S-S, Ashofteh P-S, Golfam P (2022) Selecting the best approach to modeling the performance of water supply system using the combination of rough set theory with multi-criteria decision making. Water Resour Manage 36:3129–3152. https://doi.org/10.1007/s11269-022-03193-2

    Article  Google Scholar 

  • Naghibi SA, Ahmadi K, Daneshi A (2017) Application of support vector machine, random forest, and genetic algorithm optimized random forest models in groundwater potential mapping. Water Resour Manage 31:2761–2775. https://doi.org/10.1007/s11269-017-1660-3

    Article  Google Scholar 

  • Naghibi SA, Pourghasemi HR (2015) A comparative assessment between three machine learning models and their performance comparison by bivariate and multivariate statistical methods in groundwater potential mapping. Water Resour Manage 29:5217–5236. https://doi.org/10.1007/s11269-015-1114-8

    Article  Google Scholar 

  • Naghibi SA, Pourghasemi HR, Abbaspour K (2018) A comparison between ten advanced and soft computing models for groundwater qanat potential assessment in Iran Using R and Gis. ThApC 131:967–984. https://doi.org/10.1007/s00704-016-2022-4

    Article  Google Scholar 

  • Naghibi SA, Pourghasemi HR, Dixon B (2016) Gis-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran. Environ Monit Assess 188:1–27

    Article  Google Scholar 

  • Naghibi SA, Pourghasemi HR, Pourtaghi ZS, Rezaei A (2014) Groundwater Qanat potential mapping using frequency ratio and shannon’s entropy models in the Moghan Watershed, Iran. Earth Sci Inform 8:171–186

    Article  Google Scholar 

  • Naghibi SA, Pourghasemi HR, Pourtaghi ZS, Rezaei A (2015) Groundwater Qanat potential mapping using frequency ratio and Shannon’s entropy models in the Moghan Watershed, Iran. Earth Sci Inform 8:171–186

    Article  Google Scholar 

  • Nampak H, Pradhan B, Manap MA (2014a) Application of Gis based data driven evidential belief function model to predict groundwater potential zonation. J Hydrol 513:283–300

    Article  Google Scholar 

  • Nampak H, Pradhan B, Manap MA (2014b) Application of Gis based data driven evidential belief function model to predict groundwater potential zonation. J Hyd 513:283–300

    Article  Google Scholar 

  • Nejad SG, Falah F, Daneshfar M, Haghizadeh A, Rahmati O (2015) Delineation of groundwater potential zones using remote sensing and Gis-based data-driven models. Geocarto International

  • Nemati A, Najafabadi SHG, Joodaki G, Nadoushani SSM (2020) Spatiotemporal drought characterization using gravity recovery and climate experiment (Grace) in the Central Plateau Catchment of Iran. Environ Process 7:135–157. https://doi.org/10.1007/s40710-019-00413-7

    Article  Google Scholar 

  • Nguyen PT, Ha DH, Avand M, Jaafari A, Nguyen HD, Al-Ansari N, Phong TV, Sharma R, Kumar R, Le HV (2020a) Soft computing ensemble models based on logistic regression for groundwater potential mapping. Appl Sci 10:2469

    Article  Google Scholar 

  • Nguyen PT, Ha DH, Jaafari A, Nguyen HD, Van Phong T, Al-Ansari N, Prakash I, Le HV, Pham BT (2020b) Groundwater potential mapping combining artificial neural network and real Adaboost ensemble technique: the Daknong Province case-study, Vietnam. Int J Environ Res Pub Health 17:2473

    Article  Google Scholar 

  • Nguyen PT, Ha DH, Nguyen HD, Van Phong T, Trinh PT, Al-Ansari N, Le HV, Pham BT, Ho LS, Prakash I (2020c) Improvement of Credal decision trees using ensemble frameworks for groundwater potential modeling. Sustainability 12:2622

    Article  Google Scholar 

  • Nhu V-H, Rahmati O, Falah F, Shojaei S, Al-Ansari N, Shahabi H, Shirzadi A, Gorski K, Nguyen H, Ahmad BB (2020a) Mapping of groundwater spring potential in Karst aquifer system using novel ensemble bivariate and multivariate models. Water 12:985

    Article  Google Scholar 

  • Nhu V-H, Shirzadi A, Shahabi H, Singh SK, Al-Ansari N, Clague JJ, Jaafari A, Chen W, Miraki S, Dou J, Luu C, Górski K, Thai Pham B, Nguyen HD, Ahmad BB (2020b) Shallow landslide susceptibility mapping: a comparison between logistic model tree, logistic regression, Naïve Bayes tree, artificial neural network, and support vector machine algorithms. Int J Env Res Public Health 17:2749

    Article  Google Scholar 

  • O’Brien RM (2007) A caution regarding rules of thumb for variance inflation factors. Qual Quant 41:673–690

    Article  Google Scholar 

  • Oikonomidis D, Dimogianni S, Kazakis N, Voudouris K (2015) A Gis/remote sensing-based methodology for groundwater potentiality assessment in Tirnavos Area, Greece. J Hydrol 525:197–208

    Article  Google Scholar 

  • Ozdemir A (2011) Gis-based groundwater spring potential mapping in the Sultan Mountains (Konya, Turkey) using frequency ratio, weights of evidence and logistic regression methods and their comparison. J Hydrol 411:290–308

    Article  Google Scholar 

  • Ozdemir A, Altural T (2013) A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, Sw Turkey. J Asian Earth Sci 64:180–197

    Article  Google Scholar 

  • Pourghasemi HR, Beheshtirad M (2015) Assessment of a data-driven evidential belief function model and gis for groundwater potential mapping in the Koohrang Watershed, Iran. Geocarto Int 30:662–685

    Article  Google Scholar 

  • Pourghasemi HR, Gayen A, Park S, Lee C-W, Lee S (2018) Assessment of landslide-prone areas and their zonation using logistic regression, logitboost, and na vebayes machine-learning algorithms. Sustainability 10:3697

    Article  Google Scholar 

  • Pourtaghi ZS, Pourghasemi HR (2014) Gis-based groundwater spring potential assessment and mapping in the Birjand Township, Southern Khorasan Province, Iran. Hydrogeol J 22:643–662

    Article  Google Scholar 

  • Prasad P, Loveson VJ, Kotha M, Yadav R (2020) Application of machine learning techniques in groundwater potential mapping along the West Coast of India. Giscience Remote Sens 57:735–752. https://doi.org/10.1080/15481603.2020.1794104

    Article  Google Scholar 

  • Rahmati O, Melesse AM (2016) Application of Dempster-Shafer theory, spatial analysis and remote sensing for groundwater potentiality and nitrate pollution analysis in the Semi-Arid Region of Khuzestan, Iran. Sci Total Environ 568:1110–1123

    Article  Google Scholar 

  • Rather AF, Ahmed R, Wani GF, Ahmad ST, Dar T, Javaid S, Ahmed P (2022) Mapping of groundwater potential zones in Pohru Watershed of Jhelum Basin-Western Himalaya, India using integrated approach of remote sensing, Gis and Ahp. Earth Sci Inform. https://doi.org/10.1007/s12145-022-00824-5

  • Razandi Y, Pourghasemi HR, Neisani NS, Rahmati O (2015) Application of analytical hierarchy process, frequency ratio, and certainty factor models for groundwater potential mapping using Gis. Earth Sci Inf 8:867–883. https://doi.org/10.1007/s12145-015-0220-8

    Article  Google Scholar 

  • Rennie JD, Shih L, Teevan J, Karger DR (2003) Tackling the Poor Assumptions of Naive Bayes Text Classifiers. Proceedings of the 20th international conference on machine learning (ICML-03), pp 616–623

  • Rish I (2001) An empirical study of the Naive Bayes classifier. IJCAI 2001 workshop on empirical methods in artificial intelligence, pp 41–46

  • Rodriguez-Galiano V, Mendes MP, Garcia-Soldado MJ, Chica-Olmo M, Ribeiro L (2014) Predictive modeling of groundwater nitrate pollution using random forest and multisource variables related to intrinsic and specific vulnerability: a case study in an agricultural setting (Southern Spain). Sci Total Environ 476–477C:189–206

  • Ruidas D, Pal SC, Towfiqul Islam ARM, Saha A (2022) Hydrogeochemical evaluation of groundwater aquifers and associated health hazard risk mapping using ensemble data driven model in a water scares Plateau Region of Eastern India. Exposure Health. https://doi.org/10.1007/s12403-022-00480-6

    Article  Google Scholar 

  • Ruljigaljig T, Cheng YS, Lin HI et al (2014) Evaluating the perennial stream using logistic regression in central Taiwan. AGU Fall Meeting Abstracts 2014:H11G-0942

  • Sörensen R, Zinko U, Seibert J (2006) On the calculation of the topographic wetness index: Evaluation of different methods based on field observations. Hydrol Earth Syst Sci 10:101–112

    Article  Google Scholar 

  • Saridakis M, Spiliotis M, Hrissanthou V (2021) Assessment of Bedload Transport in Sand-Gravel Bed Rivers by Using Nonlinear Fuzzy Regression 69(70):15–22

    Google Scholar 

  • Saritas MM, Yasar A (2019) Performance analysis of Ann and Naive Bayes classification algorithm for data classification. Int J Intell Syst App Eng 7:88–91

    Article  Google Scholar 

  • Satapathy SK, Dehuri S, Jagadev AK (2017) Abc optimized Rbf network for classification of Eeg signal for epileptic seizure identification. Egypt Inform J 18:55–66. https://doi.org/10.1016/j.eij.2016.05.001

    Article  Google Scholar 

  • Shortliffe EH, Buchanan BG (1975) A model of inexact reasoning in medicine. Math Biosci 23:351–379. https://doi.org/10.1016/0025-5564(75)90047-4

    Article  Google Scholar 

  • Shrout PE (1998) Measurement reliability and agreement in psychiatry. Stat Methods Med Res 7:301–317

    Article  Google Scholar 

  • Sim J, Wright CC (2005) The Kappa statistic in reliability studies: Use, interpretation, and sample size requirements. Phys Ther 85:257–268

    Article  Google Scholar 

  • Srivastava PK, Bhattacharya AK (2006) Groundwater assessment through an integrated approach using remote sensing, Gis and resistivity techniques: a case study from a hard rock terrain. Int J Remote Sens 27:4599–4620

    Article  Google Scholar 

  • Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240:1285–1293

    Article  Google Scholar 

  • Tahmassebipoor N, Rahmati O, Noormohamadi F, Lee S (2016) Spatial analysis of groundwater potential using weights-of-evidence and evidential belief function models and remote sensing. Arab J Geosci 9:1–18

    Article  Google Scholar 

  • Termeh SVR, Khosravi K, Sartaj M, Keesstra SD, Tsai FTC, Dijksma R, Pham BT (2019) Optimization of an adaptive neuro-fuzzy inference system for groundwater potential mapping. HydJ 27:2511–2534. https://doi.org/10.1007/s10040-019-02017-9

    Article  Google Scholar 

  • Tien Bui D, Moayedi H, Gor M, Jaafari A, Foong LK (2019a) Predicting slope stability failure through machine learning paradigms. ISPRS Int J Geo Inf 8:395

    Article  Google Scholar 

  • Tien Bui D, Shirzadi A, Chapi K, Shahabi H, Pradhan B, Pham BT, Singh VP, Chen W, Khosravi K, Bin Ahmad B (2019b) A hybrid computational intelligence approach to groundwater spring potential mapping. Water 11:2013

    Article  Google Scholar 

  • Torres M, Hervas C, Garcia C (2009) Multinomial logistic regression and product unit neural network models: Application of a new hybrid methodology for solving a classification problem in the livestock sector. Expert Syst Appl 36:12225–12235. https://doi.org/10.1016/j.eswa.2009.04.070

    Article  Google Scholar 

  • Tran P, Ly H-B, Trinh P, Prakash I, Pham B (2020) Landslide susceptibility mapping using forest by penalizing attributes (Fpa) algorithm based machine learning approach. Vietnam J Earth Sci. https://doi.org/10.15625/0866-7187/42/3/15047

    Article  Google Scholar 

  • Vafaeinejad A, Mahmoudi Jam S (2021) Using particle swarm optimization algorithm and geospatial information system for potential evaluating of groundwater (Case Study: Mehran, Iran). Arab J Geosci 14:1139. https://doi.org/10.1007/s12517-021-07475-8

    Article  Google Scholar 

  • Walter S (2002) Properties of the summary receiver operating characteristic (Sroc) curve for diagnostic test data. Stat Med 21:1237–1256

    Article  Google Scholar 

  • Wan T, Jun H, Zhang H, Pan W, Hua H (2015) Kappa coefficient: a popular measure of rater agreement. Shanghai Arch Psychiatry 27:62

    Google Scholar 

  • Webb G (2016) Naïve Bayes, pp 1–2

  • Yijun H, Wu N (2010) Application of Rbf network in system identification for flight control systems. 2010 International Forum on Information Technology and Applications, IFITA 2010, July 16, 2010 - July 18, 2010. IEEE Computer Society, Kunming, China, pp 67–69

    Google Scholar 

  • Yu H, Jiang S, Land KC (2015) Multicollinearity in hierarchical linear models. Soc Sci Res 53:118–136

    Article  Google Scholar 

  • Zabihi M, Pourghasemi HR, Pourtaghi ZS, Behzadfar M (2016) Gis-based multivariate adaptive regression spline and random forest models for groundwater potential mapping in Iran. Environ Earth Sci 75:665

    Article  Google Scholar 

  • Zhai X, Xia J, Zhang Y (2014) Water quality variation in the highly disturbed Huai River Basin, China from 1994 to 2005 by multi-statistical analyses. Sci Total Environ 496:594–606

    Article  Google Scholar 

  • Zhang R, Huang G-B, Sundararajan N, Saratchandran P (2007) Improved Gap-Rbf network for classification problems. Neurocomputing 70:3011–3018. https://doi.org/10.1016/j.neucom.2006.07.016

    Article  Google Scholar 

  • Zhao X, Chen W (2020) Optimization of computational intelligence models for landslide susceptibility evaluation. Remote Sens 12:2180

    Article  Google Scholar 

  • Zhou C, Yin K, Cao Y, Ahmed B, Li Y, Catani F, Pourghasemi HR (2018) Landslide susceptibility modeling applying machine learning methods: a case study from Longju in the Three Gorges Reservoir Area, China. Comput Geosci 112:23–37

    Article  Google Scholar 

Download references

Acknowledgements

This study was supported by the National Natural Science Foundation of China (Grant No. 41807192) and Natural Science Basic Research Program of Shaanxi (Program No. 2019JLM-7). The authors wish to express their sincere thanks to Prof. Zhao Duan and Prof. Xinjian Chen for the useful information provided.

Author information

Authors and Affiliations

  1. College of Geology and Environment, Xi’an University of Science and Technology, Xi’an, 710054, China

    Yong Ye, Wei Chen & Guirong Wang

  2. Key Laboratory of Coal Resources Exploration and Comprehensive Utilization, Ministry of Natural Resources, Xi’an, 710021, China

    Wei Chen

  3. Shaanxi Coal and Chemical Technology Institute Co. Ltd., 710065, Xi’an, China

    Weifeng Xue

Authors
  1. Yong Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guirong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Weifeng Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wei Chen.

Ethics declarations

Conflicts of Interest

No potential conflicts of interest were reported by the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ye, Y., Chen, W., Wang, G. et al. Spatial Prediction of the Groundwater Potential Using Remote Sensing Data and Bivariate Statistical-Based Artificial Intelligence Models. Water Resour Manage 36, 5461–5494 (2022). https://doi.org/10.1007/s11269-022-03307-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-022-03307-w

Keywords

Search

Navigation