Abstract
Groundwater (GW) is being uncontrollably exploited in various parts of the world resulting from huge needs for water supply as an outcome of population growth and industrialization. Bearing in mind the importance of GW potential assessment in reaching sustainability, this study seeks to use remote sensing (RS)-derived driving factors as an input of the advanced machine learning algorithms (MLAs), comprising deep boosting and logistic model trees to evaluate their efficiency. To do so, their results are compared with three benchmark MLAs such as boosted regression trees, k-nearest neighbors, and random forest. For this purpose, we firstly assembled different topographical, hydrological, RS-based, and lithological driving factors such as altitude, slope degree, aspect, slope length, plan curvature, profile curvature, relative slope position, distance from rivers, river density, topographic wetness index, land use/land cover (LULC), normalized difference vegetation index (NDVI), distance from lineament, lineament density, and lithology. The GW spring indicator was divided into two classes for training (434 springs) and validation (186 springs) with a proportion of 70:30. The training dataset of the springs accompanied by the driving factors were incorporated into the MLAs and the outputs were validated by different indices such as accuracy, kappa, receiver operating characteristics (ROC) curve, specificity, and sensitivity. Based upon the area under the ROC curve, the logistic model tree (87.813%) generated similar performance to deep boosting (87.807%), followed by boosted regression trees (87.397%), random forest (86.466%), and k-nearest neighbors (76.708%) MLAs. The findings confirm the great performance of the logistic model tree and deep boosting algorithms in modelling GW potential. Thus, their application can be suggested for other areas to obtain an insight about GW-related barriers toward sustainability. Further, the outcome based on the logistic model tree algorithm depicts the high impact of the RS-based factor, such as NDVI with 100 relative influence, as well as high influence of the distance from river, altitude, and RSP variables with 46.07, 43.47, and 37.20 relative influence, respectively, on GW potential.
Highlights
The demand for water supply is continuously rising as an outcome of population growth and development [1]
The results reveal that 110.77, 92.90, 78.44, 73.93, and 62.83 km2 of the Hesare-No Basin are classified as very high GW potential by the k-nearest neighbors, logistic model tree, boosted regression trees, random forest, and deep boosting, respectively
The deep boosting and logistic model tree models produced accurate and similar maps compared to the boosted regression trees, and outperformed the random forest and k-nearest neighbors algorithm
Summary
The demand for water supply is continuously rising as an outcome of population growth and development [1]. A population greater than approximately 2 billion is exposed to extreme water stress [2]. Long-term over-use of groundwater (GW) supplies has adverse consequences such as GW table decline and consequent land subsidence, saltwater intrusion, and water quality deterioration. It is crucial to study GW potential at watershed or even larger scales to aid the managers to utilize GW in a sustainable manner. In this respect, GW assessment is a useful strategy to define the potential in different regions to be used for different exploitation purposes and or conservation plans. There are several indicators for GW productivity, i.e., spring, well discharge [4], and qanat [5], albeit springs are the most accessible data all around the world; this study focuses on this indicator
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