Groundwater recharge potential zonation using an ensemble of machine learning and bivariate statistical models

Abstract

Many regions in Iran are currently experience water crisis, largely driven by frequent droughts and expanding agricultural land combined with over abstraction of groundwater. Therefore, it is extremely important to identify potential groundwater recharge (GWR) zones to help in prevent water scarcity. The key objective of this research is to applying different scenarios for GWR potential mapping by means of a classifier ensemble approach, namely a combination of Maximum Entropy (ME) and Frequency Ratio (FR) models in a semi-arid mountainous, Marboreh Watershed of Iran. To consider the ensemble effect of these models, 15 input layers were generated and used in two models and then the models were combined in seven scenarios. According to marginal response curves (MRCs) and the Jackknife technique, quaternary formations (Qft1 and Qft2) of lithology, sandy-clay-loam (Sa. Cl. L) class of soil, 0–4% class of slope, and agriculture & rangeland classes of land use, offered the highest percolation potential. Results of the FR model showed that the highest weight belonged to Qft1 rocks and Sa. Cl. L textures. Seven scenarios were used for GWR potential maps by different ensembles based on basic mathematical operations. Correctly Classified Instances (CCI), and the AUC indices were applied to validate model predictions. The validation indices showed that scenarios 5 had the best performance. The combination of models by different ensemble scenarios enhances the efficiency of these models. This study serves as a basis for future investigations and provides useful information for prediction of sites with groundwater recharge potential through combination of state-of-the-art statistical and machine learning models. The proposed ensemble model reduced the machine learning and statistical models’ limitations gaps and promoted the accuracy of the model where combining, especially for data-scarce areas. The results of present study can be used for the GWR potential mapping, land use planning, and groundwater development plans.

Groundwater Potential Zone Mapping Using Analytical Hierarchy Process and GIS in Muga Watershed, Abay Basin, Ethiopia

“…Literatures reveal that several researchers have been using GIS to delineate groundwater potential zones with the integration of statistical approach such as simple additive weight (SAW) and analytic hierarchy process (AHP) [ 25 , 26 , 27 , 28 , 29 , 30 , 31 ] and, machine learning. [ 32 , 33 , 34 , 35 , 36 , 37 , 38 ] The combination of GIS and remote sensing technologies reduce the ambiguity of hydrogeological data various aspect.…”

Using Remote Sensing and Machine Learning to Locate Groundwater Discharge to Salmon-Bearing Streams

“…The ways remote sensing, geospatial modeling, and/or machine learning are used in hydrologic studies depends on the question being addressed; the spatial and temporal scale of the question; and the type, amount, and quality of the available data [24][25][26]. Nevertheless, these tools have been incorporated into strategies to forecast groundwater levels [27][28][29][30], groundwater quality [31][32][33], saltwater intrusion and groundwater salinity [34], and groundwater resource availability [35,36]. Using these approaches to better understand and predict groundwater discharge is particularly challenging (e.g., [22,23]).…”

Fusion of Remote Sensing Data Using GIS-Based AHP-Weighted Overlay Techniques for Groundwater Sustainability in Arid Regions

“…Therefore, rainfall factor is extremely important in studying the water accumulation and determining groundwater recharging as it represents the source of water in arid regions [2,18]. Rainfall has a positive relationship to groundwater recharging as the higher the annual rainfall, the higher the groundwater recharge potentials [72].…”