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Research Articles

Developing groundwater potentiality models by coupling ensemble machine learning algorithms and statistical techniques for sustainable groundwater management

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Pages 7927-7953 | Received 21 Jun 2021, Accepted 24 Sep 2021, Published online: 19 Oct 2021

References

  • Abdulkadir TS, Muhammad RUM, Wan Yusof K, Ahmad MH, Aremu SA, Gohari A, Abdurrasheed AS. 2019. Quantitative analysis of soil erosion causative factors for susceptibility assessment in a complex watershed. Cogent Eng. 6(1):1594506.
  • Achu AL, Reghunath R, Thomas J. 2020. Mapping of groundwater recharge potential zones and identification of suitable site-specific recharge mechanisms in a tropical river basin. Earth Syst Environ. 4(1):131–145.
  • Ahmad MUD, Kirby M, Islam MS, Hossain MJ, Islam MM. 2014. Groundwater use for irrigation and its productivity: status and opportunities for crop intensification for food security in Bangladesh. Water Resour Manag. 28(5):1415–1429.
  • Akhter S, Eibek KU, Islam S, Islam ARMT, Chu R, Shuanghe S. 2019. Predicting spatiotemporal changes of channel morphology in the reach of Teesta River, Bangladesh using GIS and ARIMA modeling. Quat Int. 513:80–94.
  • Aldous D, Pitman J. 2000. Inhomogeneous continuum random trees and the entrance boundary of the additive coalescent. Probab Theory Relat Fields. 118(4):455–482.
  • Al-Fugara AK, Ahmadlou M, Al-Shabeeb AR, AlAyyash S, Al-Amoush H, Al-Adamat R. 2020. Spatial mapping of groundwater springs potentiality using grid search-based and genetic algorithm-based support vector regression. Geocarto Int. :1–20.
  • Al-Quraishi KK, He Q, Kauppila W, Wang M, Yang Y. 2020. Mechanical testing of two-dimensional materials: a brief review. Int J Smart Nano Mater. 11(3):207–246.
  • Anim-Gyampo M, Anornu GK, Agodzo SK, Appiah-Adjei EK. 2019. Groundwater risk assessment of shallow aquifers within the Atankwidi Basin of Northeastern Ghana. Earth Syst Environ. 3(1):59–72.
  • 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 Sensing. 12(3):490.
  • Arabameri A, Pourghasemi HR. 2019. Spatial modeling of gully erosion using linear and quadratic discriminant analyses in GIS and R. In Spatial modeling in GIS and R for earth and environmental sciences. Elsevier; p. 299–321.
  • Arulbalaji P, Padmalal D, Sreelash K. 2019. GIS and AHP techniques based delineation of groundwater potential zones: a case study from southern Western Ghats, India. Sci Rep. 9(1):1–17.
  • Ayalew L, Yamagishi H. 2005. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology. 65(1-2):15–31.
  • Batterman S. 1992. The use of singular value variance-decomposition proportions in FT-IR analysis of gases and vapors. Appl Spectrosc. 46(5):800–806.
  • BBS (Bangladesh Bureau of Statistics). 2017. Statistical Yearbook of Bangladesh. Statistical Division, Ministry of Planning, Govt. People's Republic of Bangladesh.
  • Benesty J, Chen J, Huang Y, Cohen I. 2009. Pearson correlation coefficient. In Noise reduction in speech processing. Berlin, Heidelberg: Springer; p. 1–4.
  • Bhattacharya S, Das S, Das S, Kalashetty M, Warghat SR. 2021. An integrated approach for mapping groundwater potential applying geospatial and MIF techniques in the semiarid region. Environ Dev Sustain. 23(1):495–510.
  • Bierkens MF, Reinhard S, de Bruijn JA, Veninga W, Wada Y. 2019. The shadow price of irrigation water in major groundwater‐depleting countries. Water Resour Res. 55(5):4266–4287.
  • Boori MS, Choudhary K, Kupriyanov A. 2019. Identification and Mapping of Groundwater Potential Zone through Remote Sensing and GIS Technology in Kalmykia, Russia. Int J Geoinform. 15(1):23–36.
  • Breiman L. 1996. Bagging predictors. Environ Dev Sustain. 24(2):123–140.
  • Bui DT, Khosravi K, Karimi M, Busico G, Khozani ZS, Nguyen H, Mastrocicco M, Tedesco D, Cuoco E, Kazakis N. 2020. Enhancing nitrate and strontium concentration prediction in groundwater by using new data mining algorithm. Sci Total Environ. 715:136836.
  • Bui QT, Nguyen QH, Nguyen XL, Pham VD, Nguyen HD, Pham VM. 2020. Verification of novel integrations of swarm intelligence algorithms into deep learning neural network for flood susceptibility mapping. J Hydrol. 581:124379.
  • Chen Y, Chen W, Chandra Pal S, Saha A, Chowdhuri I, Adeli B, Janizadeh S, Dineva AA, Wang X, Mosavi A. 2021. Evaluation efficiency of hybrid deep learning algorithms with neural network decision tree and boosting methods for predicting groundwater potential. Geocarto Int. :1–21.
  • Chen W, Li W, Chai H, Hou E, Li X, Ding X. 2016. GIS-based landslide susceptibility mapping using analytical hierarchy process (AHP) and certainty factor (CF) models for the Baozhong region of Baoji City, China. Environ Earth Sci. 75(1):1–14.
  • Chen W, Li Y, Tsangaratos P, Shahabi H, Ilia I, Xue W, Bian H. 2020. Groundwater spring potential mapping using artificial intelligence approach based on kernel logistic regression, random forest, and alternating decision tree models. Appl Sci. 10(2):425.
  • Chen W, Pourghasemi HR, Panahi M, Kornejady A, Wang J, Xie X, Cao S. 2017. Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques. Geomorphology. 297:69–85.
  • Chen W, Pourghasemi HR, Zhao Z. 2017. A GIS-based comparative study of Dempster-Shafer, logistic regression and artificial neural network models for landslide susceptibility mapping. Geocarto Int. 32(4):367–385.
  • Chen W, Shirzadi A, Shahabi H, Ahmad BB, Zhang S, Hong H, Zhang N. 2017. A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China. Geomatics Nat Hazards Risk. 8(2):1955–1977.
  • Chen W, Xie X, Wang J, Pradhan B, Hong H, Bui DT, Duan Z, Ma J. 2017. A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. Catena. 151:147–160.
  • Chen Y, Xie W, Xu X. 2019. Changes of population, built-up land, and cropland exposure to natural hazards in China from 1995 to 2015. Int J Disaster Risk Sci. 10(4):557–572.
  • Chen W, Zhang S, Li R, Shahabi H. 2018. Performance evaluation of the GIS-based data mining techniques of best-first decision tree, random forest, and naïve Bayes tree for landslide susceptibility modeling. Sci Total Environ. 644:1006–1018.
  • Chen W, Zhao X, Tsangaratos P, Shahabi H, Ilia I, Xue W, Wang X, Ahmad BB. 2020. Evaluating the usage of tree-based ensemble methods in groundwater spring potential mapping. J Hydrol. 583:124602.
  • Choi YC, Murtala S, Jeong BC, Choi KS. 2021. Relief Extraction From a Rough Stele Surface Using SVM-Based Relief Segment Selection. IEEE Access. 9:4973–4982.
  • Choubin B, Moradi E, Golshan M, Adamowski J, Sajedi-Hosseini F, Mosavi A. 2019. An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines. Sci Total Environ. 651(Pt 2):2087–2096.
  • 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.
  • 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(5):1–16.
  • Dehnavi A, Aghdam IN, Pradhan B, Varzandeh MHM. 2015. A new hybrid model using step-wise weight assessment ratio analysis (SWARA) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran. Catena. 135:122–148.
  • Dietterich TG. 2000. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Mach Learn. 40(2):139–157.
  • Dou J, Oguchi T, Hayakawa YS, Uchiyama S, Saito H, Paudel U. 2014. GIS-based landslide susceptibility mapping using a certainty factor model and its validation in the Chuetsu Area, Central Japan. In Landslide science for a safer geoenvironment. Cham: Springer; p. 419–424.
  • Duque EL, Aquino PT. 2020. Anthropometric Analysis in Automotive Manual Transmission Gearshift Quality Perception. In CTI SYMPOSIUM 2018. Berlin, Heidelberg: Springer; p. 97–109
  • Fan J, Wang X, Wu L, Zhou H, Zhang F, Yu X, Lu X, Xiang Y. 2018. Comparison of Support Vector Machine and Extreme Gradient Boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: A case study in China. Energy Convers Manag. 164:102–111.
  • Forkuor G, Hounkpatin OK, Welp G, Thiel M. 2017. High resolution mapping of soil properties using remote sensing variables in south-western Burkina Faso: a comparison of machine learning and multiple linear regression models. PloS One. 12(1):e0170478.
  • Ghorbani Nejad S, Falah F, Daneshfar M, Haghizadeh A, Rahmati O. 2016. Delineation of groundwater potential zones using remote sensing and GIS-based data-driven models. Geocarto Int. 32(2):1–187.
  • Hanley JA, McNeil BJ. 1982. Maximum attainable discrimination and the utilization of radiologic examinations. J Chronic Dis. 35(8):601–611.
  • Ho TK. 1998. The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell. 20(8):832–844.
  • Hong H, Chen W, Xu C, Youssef AM, Pradhan B, Tien Bui D. 2016. Rainfall-induced landslide susceptibility assessment at the Chongren area (China) using frequency ratio, certainty factor, and index of entropy. Geocarto Int. 32(2):1–154.
  • Hong H, Liu J, Bui DT, Pradhan B, Acharya TD, Pham BT, Zhu AX, Chen W, Ahmad BB. 2018. Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China). Catena. 163:399–413.
  • Hong H, Liu J, Zhu AX. 2019. Landslide susceptibility evaluating using artificial intelligence method in the Youfang district (China). Environ Earth Sci. 78(15):1–20.
  • Hong H, Pradhan B, Jebur MN, Bui DT, Xu C, Akgun A. 2016. Spatial prediction of landslide hazard at the Luxi area (China) using support vector machines. Environ Earth Sci. 75(1):1–14.
  • Hong H, Pradhan B, Xu C, Bui DT. 2015. Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines. Catena. 133:266–281.
  • Islam ARMT, Talukdar S, Mahato S, Kundu S, Eibek KU, Pham QB, Kuriqi A, Linh NTT. 2021. Flood susceptibility modelling using advanced ensemble machine learning models. Geosci Front. 12(3):101075.
  • Jaafari A, Najafi A, Pourghasemi HR, Rezaeian J, Sattarian A. 2014. GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran. Int J Environ Sci Technol. 11(4):909–926.
  • Javidan N, Kavian A, Pourghasemi HR, Conoscenti C, Jafarian Z. 2020. Data mining technique (maximum entropy model) for mapping gully erosion susceptibility in the gorganrood watershed, Iran. In Gully erosion studies from India and surrounding regions. Cham: Springer; p. 427–448.
  • Johnson TM, Roback RC, McLing T L, Bullen T D, DePaolo DJ, Doughty C, Hunt RJ, Smith RW, Cecil LD, Murrell MT. 2000. Groundwater “fast paths” in the Snake River Plain aquifer: Radiogenic isotope ratios as natural groundwater tracers. Geology. 28(10):871–874.
  • Kanungo DP, Arora MK, Sarkar S, Gupta RP. 2006. A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Eng Geol. 85(3-4):347–366.
  • Kaur L, Rishi MS, Singh G, Nath Thakur S. 2020. Groundwater potential assessment of an alluvial aquifer in Yamuna sub-basin (Panipat region) using remote sensing and GIS techniques in conjunction with analytical hierarchy process (AHP) and catastrophe theory (CT). Ecol Indic. 110:105850.
  • Kotsiantis S. 2011. Combining bagging, boosting, rotation forest and random subspace methods. Artif Intell Rev. 35(3):223–240.
  • La Valle SM. 2017. Rapidly-exploring random trees a new tool for path planning, Oct. 1998, [online] Available: BBS (Bangladesh Bureau of Statistics). Statistical Yearbook of Bangladesh. Statistical Division, Ministry of Planning, Govt. People's Republic of Bangladesh.
  • Lee S, Dan NT. 2005. Probabilistic landslide susceptibility mapping in the Lai Chau province of Vietnam: focus on the relationship between tectonic fractures and landslides. Environ Geol. 48(6):778–787.
  • Li Y, Chen W. 2019. Landslide susceptibility evaluation using hybrid integration of evidential belief function and machine learning techniques. Water. 12(1):113.
  • Li M, Zhang L, Liu G. 2019. Estimation of thermal properties of soil and backfilling material from thermal response tests (TRTs) for exploiting shallow geothermal energy: sensitivity, identifiability, and uncertainty. Renew Energy. 132:1263–1270.
  • Lu J, Wu J, Zhang C, Zhang Y. 2020. Possible effect of submarine groundwater discharge on the pollution of coastal water: Occurrence, source, and risks of endocrine disrupting chemicals in coastal groundwater and adjacent seawater influenced by reclaimed water irrigation. Chemosphere. 250:126323.
  • Luo D, Wen X, Xu J, Zhang H, Vongphet S. 2021. Delineation of groundwater potential zones using modified weight standardization method and GIS in arid environments: case study of Ejina Oasis, Inner Mongolia, China. Arab J Geosci. 14(8):1–14.
  • Magnabosco R, Galvão P, de Carvalho AM. 2020. An approach to map karst groundwater potentiality in an urban area, Sete Lagoas, Brazil. Hydrol Sci J. 65(14):2482–2498.
  • Mahato S, Pal S. 2019. Groundwater potential mapping in a rural river basin by union (OR) and intersection (AND) of four multi-criteria decision-making models. Nat Resour Res. 28(2):523–545.
  • Mallick J, Alqadhi S, Talukdar S, AlSubih M, Ahmed M, Khan RA, Kahla NB, Abutayeh SM. 2021. Risk assessment of resources exposed to rainfall induced landslide with the development of GIS and RS based ensemble metaheuristic machine learning algorithms. Sustainability. 13(2):457.
  • Mallick J, Talukdar S, Alsubih M, Ahmed M, Islam ARMT, Shahfahad, Thanh NV. 2021. Proposing receiver operating characteristic-based sensitivity analysis with introducing swarm optimized ensemble learning algorithms for groundwater potentiality modelling in Asir region, Saudi Arabia. Geocarto Int. :1–28.
  • Mallick J, Talukdar S, Alsubih M, Almesfer MK, Shahfahad H, TH, Rahman A. 2021. 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. Geocarto Int. :1–35.
  • Mallick J, Talukdar S, Kahla NB, Ahmed M, Alsubih M, Almesfer MK, Islam ARMT. 2021. A Novel Hybrid Model for Developing Groundwater Potentiality Model Using High Resolution Digital Elevation Model (DEM) Derived Factors. Water. 13(19):2632.
  • Mandal KK, Ranjan A, Dharanirajan K. 2021. Delineation of groundwater potential zones (GWPZ) of Port Blair, Andaman Islands, India using the Multi Influencing Factors (MIF) method and geospatial techniques. Remote Sens Appl: Soc Environ. 24:100631.
  • Maskooni EK, Naseri-Rad M, Berndtsson R, Nakagawa K. 2020. Use of heavy metal content and modified water quality index to assess groundwater quality in a semiarid area. Water. 12(4):1115.
  • Mind'je R, Li L, Nsengiyumva JB, Mupenzi C, Nyesheja EM, Kayumba PM, Gasirabo A, Hakorimana E. 2019. Landslide susceptibility and influencing factors analysis in Rwanda. Environ Dev Sustain. 22(8):7985–8012.
  • 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 Manag. 33(1):281–302.
  • Mirchooli F, Motevalli A, Pourghasemi HR, Mohammadi M, Bhattacharya P, Maghsood FF, Tiefenbacher JP. 2019. How do data-mining models consider arsenic contamination in sediments and variables importance? Environ Monit Assess. 191(12):1–19.
  • Moayedi H, Tien Bui D, Gör 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. IJGI. 8(9):391.
  • Moghaddam HK, Moghaddam HK, Kivi ZR, Bahreinimotlagh M, Alizadeh MJ. 2019. Developing comparative mathematic models, BN and ANN for forecasting of groundwater levels. Groundwater Sustain Dev. 9:100237.
  • Mohamed WNHW, Salleh MNM, Omar AH. 2012. A comparative study of reduced error pruning method in decision tree algorithms. In 2012 IEEE International conference on control system, computing and engineering (pp. 392–397). IEEE.
  • Mojid MA, Parvez MF, Mainuddin M, Hodgson G. 2019. Water table trend—a sustainability status of groundwater development in North-West Bangladesh. Water. 11(6):1182.
  • Mosavi A, Hosseini FS, Choubin B, Goodarzi M, Dineva AA, Sardooi ER. 2021. Ensemble boosting and bagging based machine learning models for groundwater potential prediction. Water Resour Manage. 35(1):23–37.
  • Mosavi A, Hosseini FS, Choubin B, Taromideh F, Ghodsi M, Nazari B, Dineva AA. 2021. Susceptibility mapping of groundwater salinity using machine learning models. Environ Sci Pollut Res Int. 28(9):10804–10817.
  • Mukherjee I, Singh UK. 2020. Delineation of groundwater potential zones in a drought-prone semi-arid region of east India using GIS and analytical hierarchical process techniques. Catena. 194:104681.
  • 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 Manag. 29(14):5217–5236.
  • Nampak H, Pradhan B, Abd Manap M. 2014. Application of GIS based data driven evidential belief function model to predict groundwater potential zonation. J Hydrol. 513:283–300.
  • Nguyen PT, Ha DH, Avand M, Jaafari A, Nguyen HD, Al-Ansari N, Van Phong T, Sharma R, Kumar R, Le HV, et al. 2020. Soft computing ensemble models based on logistic regression for groundwater potential mapping. Appl Sci. 10(7):2469.
  • Nguyen PT, Ha DH, Jaafari A, Nguyen HD, Van Phong T, Al-Ansari N, Prakash I, Le HV, Pham BT. 2020. Groundwater potential mapping combining artificial neural network and real AdaBoost ensemble technique: the DakNong province case-study, Vietnam. IJERPH. 17(7):2473.
  • Nguyen PT, Ha DH, Nguyen HD, Van Phong T, Trinh PT, Al-Ansari N, Le HV, Pham BT, Ho LS, Prakash I, 2020. Improvement of credal decision trees using ensemble frameworks for groundwater potential modeling. Sustainability. 12(7):2622.
  • Nhu VH, Rahmati O, Falah F, Shojaei S, Al-Ansari N, Shahabi H, Shirzadi A, Górski K, Nguyen H, Ahmad BB. 2020. Mapping of groundwater spring potential in karst aquifer system using novel ensemble bivariate and multivariate models. Water. 12(4):985.
  • Pal S, Kundu S, Mahato S. 2020. Groundwater potential zones for sustainable management plans in a river basin of India and Bangladesh. J Cleaner Prod. 257:120311.
  • Pal S, Sarda R. 2021. Modelling water richness in riparian flood plain wetland using bivariate statistics and machine learning algorithms and figuring out the role of damming. Geocarto Int. :1–24.
  • Pal S, Talukdar S. 2018. Application of frequency ratio and logistic regression models for assessing physical wetland vulnerability in Punarbhaba river basin of Indo-Bangladesh. Hum Ecol Risk Assess Int J. 24(5):1291–1311.
  • Panahi M, Sadhasivam N, Pourghasemi HR, Rezaie F, Lee S. 2020. Spatial prediction of groundwater potential mapping based on convolutional neural network (CNN) and support vector regression (SVR). J Hydrol. 588:125033.
  • Peng L, Niu R, Huang B, Wu X, Zhao Y, Ye R. 2014. Landslide susceptibility mapping based on rough set theory and support vector machines: A case of the Three Gorges area, China. Geomorphology. 204:287–301.
  • Pham BT, Jaafari A, Van Phong T, Yen HPH, Tuyen TT, Van Luong V, Nguyen HD, Van Le H, Foong LK. 2021. Improved flood susceptibility mapping using a best first decision tree integrated with ensemble learning techniques. Geosci Front. 12(3):101105.
  • Polo JM, Liu S, Figueroa ME, Kulalert W, Eminli S, Tan KY, Apostolou E, Stadtfeld M, Li Y, Shioda T, et al. 2010. Cell type of origin influences the molecular and functional properties of mouse induced pluripotent stem cells. Nat Biotechnol. 28(8):848–855.
  • 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(6):662–685.
  • Pourghasemi HR, Rahmati O. 2018. Prediction of the landslide susceptibility: which algorithm, which precision? Catena. 162:177–192.
  • Pourghasemi HR, Sadhasivam N, Yousefi S, Tavangar S, Nazarlou HG, Santosh M. 2020. Using machine learning algorithms to map the groundwater recharge potential zones. J Environ Manag. 265:110525.
  • Pradhan AMS, Kim YT, Shrestha S, Huynh TC, Nguyen BP. 2021. Application of deep neural network to capture groundwater potential zone in mountainous terrain, Nepal Himalaya. Environ Sci Pollut Res Int. 28(15):18501–18517.
  • Pradhan S, Kumar S, Kumar Y, Sharma HC. 2019. Assessment of groundwater utilization status and prediction of water table depth using different heuristic models in an Indian interbasin. Soft Comput. 23(20):10261–10285.
  • Pradhan B, Lee S. 2010. Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environ Earth Sci. 60(5):1037–1054.
  • 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(6):735–752.
  • Qadir J, Bhat MS, Alam A, Rashid I. 2020. Mapping groundwater potential zones using remote sensing and GIS approach in Jammu Himalaya, Jammu and Kashmir. GeoJournal. 85(2):487–504.
  • Quinlan JR. 1992. November. Learning with continuous classes. In 5th Australian joint conference on artificial intelligence (Vol. 92, pp. 343–348).
  • Quinlan J. 1993. C4. 5: Program for machine learning morgan kaufmann. San Mateo, CA, USA.
  • Quinlan JR. 1996. Bagging, boosting, and C4. 5. In AAAI/IAAI, Vol. 1 (pp. 725–730).
  • Rabbi F, Haque MN, Kadir ME, Siddik MS, Kabir A. 2020. An Ensemble Approach to Detect Code Comment Inconsistencies using Topic Modeling. In SEKE (pp. 392–395).
  • Rahman AHMM, Akter S, Rani R, Islam AKMR. 2015. Taxonomic study of leafy vegetables at Santahar Pouroshova of District Bogra, Bangladesh with emphasis on medicinal plants. Int J Adv Res. 3(5):1019–1036.
  • Rizeei HM, Pradhan B, Saharkhiz MA, Lee S. 2019. Groundwater aquifer potential modeling using an ensemble multi-adoptive boosting logistic regression technique. J Hydrol. 579:124172.
  • Roy J, Saha S, Arabameri A, Blaschke T, Bui DT. 2019. A novel ensemble approach for landslide susceptibility mapping (LSM) in Darjeeling and Kalimpong districts, West Bengal, India. Remote Sensing. 11(23):2866.
  • Sachdeva S, Kumar B. 2021. Comparison of gradient boosted decision trees and random forest for groundwater potential mapping in Dholpur (Rajasthan), India. Stoch Environ Res Risk Assess. 35(2):287–306.
  • Saha TK, Pal S, Talukdar S, Debanshi S, Khatun R, Singha P, Mandal I. 2021. How far spatial resolution affects the ensemble machine learning based flood susceptibility prediction in data sparse region. J Environ Manag. 297:113344.
  • Sajedi-Hosseini F, Malekian A, Choubin B, Rahmati O, Cipullo S, Coulon F, Pradhan B. 2018. A novel machine learning-based approach for the risk assessment of nitrate groundwater contamination. Sci Total Environ. 644:954–962.
  • Salam R, Ghose B, Shill BK, Islam MA, Islam ARMT, Sattar MA, Alam GM. Ahmed B., 2021. Perceived and actual risks of drought: household and expert views from the lower Teesta River Basin of northern Bangladesh. Nat Haz. 108:2569–2587.
  • Sameen MI, Pradhan B, Lee S. 2020. Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment. Catena. 186:104249.
  • Schumann GP, Vernieuwe H, De Baets B, Verhoest NEC. 2014. ROC‐based calibration of flood inundation models. Hydrol Process. 28(22):5495–5502.
  • Sihag P, Karimi SM, Angelaki A. 2019. Random forest, M5P and regression analysis to estimate the field unsaturated hydraulic conductivity. Appl Water Sci. 9(5):1–9.
  • Tehrany MS, Pradhan B, Mansor S, Ahmad N. 2015. Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. Catena. 125:91–101.
  • Tien Bui D, Ho TC, Revhaug I, Pradhan B, Nguyen DB. 2014. Landslide susceptibility mapping along the national road 32 of Vietnam using GIS-based J48 decision tree classifier and its ensembles. In Cartography from pole to pole. Berlin, Heidelberg: Springer; p. 303–317.
  • Tien Bui D, Khosravi K, Li S, Shahabi H, Panahi M, Singh VP, Chapi K, Shirzadi A, Panahi S, Chen W, et al. 2018. New hybrids of anfis with several optimization algorithms for flood susceptibility modeling. Water. 10(9):1210.
  • Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB. 2012. Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS. Comput Geosci. 45:199–211.
  • Tien Bui D, Shahabi H, Shirzadi A, Chapi K, Alizadeh M, Chen W, Mohammadi A, Ahmad BB, Panahi M, Hong H, et al. 2018. Landslide detection and susceptibility mapping by airsar data using support vector machine and index of entropy models in cameron highlands, malaysia. Remote Sens. 10(10):1527.
  • Tien Bui D, Shirzadi A, Chapi K, Shahabi H, Pradhan B, Pham BT, Singh VP, Chen W, Khosravi K, Bin Ahmad B, Lee S. 2019. A hybrid computational intelligence approach to groundwater spring potential mapping. Water. 11(10):2013.
  • Trigila A, Iadanza C, Esposito C, Scarascia-Mugnozza G. 2015. Comparison of Logistic Regression and Random Forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy). Geomorphology. 249:119–136.
  • Tu JV. 1996. Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J Clin Epidemiol. 49(11):1225–1231.
  • Vellaikannu A, Palaniraj U, Karthikeyan S, Senapathi V, Viswanathan PM, Sekar S. 2021. Identification of groundwater potential zones using geospatial approach in Sivagangai district, South India. Arab J Geosci. 14(1):1–17.
  • Wang G, Lei X, Chen W, Shahabi H, Shirzadi A. 2020. Hybrid computational intelligence methods for landslide susceptibility mapping. Symmetry. 12(3):325.
  • Wu M, Feng Q, Wen X, Yin Z, Yang L, Sheng D. 2021. Deterministic Analysis and Uncertainty Analysis of Ensemble Forecasting Model Based on Variational Mode Decomposition for Estimation of Monthly Groundwater Level. Water. 13(2):139.
  • Xie Z, Chen G, Meng X, Zhang Y, Qiao L, Tan L. 2017. A comparative study of landslide susceptibility mapping using weight of evidence, logistic regression and support vector machine and evaluated by SBAS-InSAR monitoring: Zhouqu to Wudu segment in Bailong River Basin, China. Environ Earth Sci. 76(8):313.
  • Xu C, Xu X, Dai F, Xiao J, Tan X, Yuan R. 2012. Landslide hazard mapping using GIS and weight of evidence model in Qingshui river watershed of 2008 Wenchuan earthquake struck region. J Earth Sci. 23(1):97–120.
  • Yang L, Cervone G. 2019. Analysis of remote sensing imagery for disaster assessment using deep learning: a case study of flooding event. Soft Comput. 23(24):13393–13408.
  • Yariyan P, Avand M, Omidvar E, Pham QB, Linh NTT, Tiefenbacher JP. 2021. Optimization of statistical and machine learning hybrid models for groundwater potential mapping. Geocarto Int. :1–35.
  • Yen HPH, Pham BT, Van Phong T, Ha DH, Costache R, Van Le H, Nguyen HD, Amiri M, Van Tao N, Prakash I. 2021. Locally weighted learning based hybrid intelligence models for groundwater potential mapping and modeling: A case study at Gia Lai province, Vietnam. Geosci Front. 12(5):101154.
  • Yu X, Moraetis D, Nikolaidis NP, Li B, Duffy C, Liu B. 2019. A coupled surface-subsurface hydrologic model to assess groundwater flood risk spatially and temporally. Environ Model Softw. 114:129–139.
  • Zhu Q, Abdelkareem M. 2021. Mapping groundwater potential zones using a knowledge-driven approach and GIS analysis. Water. 13(5):579.
  • Zhu F, Ma S, Liu T, Deng X. 2018. Green synthesis of nano zero-valent iron/Cu by green tea to remove hexavalent chromium from groundwater. J Cleaner Prod. 174:184–190.

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