A Novel Hybrid Model for Developing Groundwater Potentiality Model Using High Resolution Digital Elevation Model (DEM) Derived Factors

Abstract

The present work aims to build a unique hybrid model by combining six fuzzy operator feature selection-based techniques with logistic regression (LR) for producing groundwater potential models (GPMs) utilising high resolution DEM-derived parameters in Saudi Arabia’s Bisha area. The current work focuses exclusively on the influence of DEM-derived parameters on GPMs modelling, without considering other variables. AND, OR, GAMMA 0.75, GAMMA 0.8, GAMMA 0.85, and GAMMA 0.9 are six hybrid models based on fuzzy feature selection. The GPMs were validated by using empirical and binormal receiver operating characteristic curves (ROC). An RF-based sensitivity analysis was performed in order to examine the influence of GPM settings. Six hybrid algorithms and one unique hybrid model have predicted 1835–2149 km2 as very high and 3235–4585 km2 as high groundwater potential regions. The AND model (ROCe-AUC: 0.81; ROCb-AUC: 0.804) outperformed the other models based on ROC’s area under curve (AUC). A novel hybrid model was constructed by combining six GPMs (considering as variables) with the LR model. The AUC of ROCe and ROCb revealed that the novel hybrid model outperformed existing fuzzy-based GPMs (ROCe: 0.866; ROCb: 0.892). With DEM-derived parameters, the present work will help to improve the effectiveness of GPMs for developing sustainable groundwater management plans.

Novel hybrid models to enhance the efficiency of groundwater potentiality model

“…Also, these technologies help to assimilate multi-parameters and are able to produce highly accurate groundwater potential maps for large to a small areas within a short time (Dau et al 2021;Namous et al 2021). Therefore, these technologies have added a new dimension to groundwater research (Mallick et al 2021c;Nguyen et al 2020). The robust and effective models generally rely on the choice of conditioning variables and standard assimilation methods (Kumar et al Table 1 Literature review for groundwater potentiality conditioning parameters selection *EL-Elevation, AS-Aspect, CUR-Curvature, SL-Slope, TRI-Topographic roughness index, TWI-Topographic wetness index, SPI-Stream power index, STI-Sediment transport index, ST-Soil types, DR-Distance to river, LULC-Land use land cover, RF-Rainfall ).…”

“…We chose those variables, which many researchers have extensively used. In the plain regions, topographic and climatic parameters have been recognized as important variables, while in the mountains, along with topographic, geological variables have been described as critical variables for GWP mapping (Mallick et al 2021c;Al-Djazouli et al 2021;Pathak et al 2021;Namous et al 2021;Al-Abadi et al 2021). For example, drainage density could be a valid variable in flood plains, not in mountainous regions (Bhattacharya et al 2021;Fadhillah et al 2021).…”

“…Several approaches have been developed and used for GWP modeling (Pande et al 2020). Therefore, we classified all methods as per their operational background, such as (1) statistical approaches for zoning groundwater potential, which have a long history of use (Mallick et al 2021c;Pham et al 2021). Statistical procedures that are now in use include frequency ratio (Abd Manap et al 2014;Guru et al 2017), logistic regression (Rizeei et al 2019), weight of evidence (Rane and Jayaraj 2021;Das et al 2021), certainty factor (Razandi et al 2015), and evidential belief function (Tahmassebipoor et al 2016).…”

“…The conditions necessary to enhance groundwater capacity have been established using machine learning algorithms based on data mining. (3) Machine learning (ML) models include CART (Gayen and Pourghasemi 2019), random forest (RF) (Golkarian et al 2018), support vector machine (Panahi et al 2020), artificial neural network (Nguyen et al 2020;Naghibi et al 2017;Mallick et al 2021c), neuro-fuzzy (Termeh et al 2019, and decision trees (Choubin et al 2019).…”

“…Susceptibility, vulnerability, hazards, potentiality, and other issues can now be studied using a multi-model approach and ensemble modeling (Talukdar and Pal, 2019;Islam et al, 2021;Mahato et al 2021;Talukdar et al 2021a). The ensemble models include AdaBoost (Ha et al 2021), bagging (Yen et al 2021), Reptree-bagging (Chen et al 2019a), dagging (Talukdar et al 2021a, b), and rotation forest (Mallick et al 2021c). Therefore, to increase the model's resilience for GWP mapping, we utilized six ensemble machine learning techniques in the present study, including RF, RS, bagging, dagging, NBT, and stacking.…”