A Modeling Comparison of Groundwater Potential Mapping in a Mountain Bedrock Aquifer: QUEST, GARP, and RF Models

Davoud Davoudi Moghaddam,Ali Haghizadeh,Zahra Kalantari,Omid Rahmati

doi:10.3390/w12030679

Abstract

In some arid regions, groundwater is the only source of water for human needs, so understanding groundwater potential is essential to ensure its sustainable use. In this study, three machine learning models (Genetic Algorithm for Rule-Set Production (GARP), Quick Unbiased Efficient Statistical Tree (QUEST), and Random Forest (RF)) were applied and verified for spatial prediction of groundwater in a mountain bedrock aquifer in Piranshahr Watershed, Iran. A spring location dataset consisting of 141 springs was prepared by field surveys, and from this three different sample datasets (S1–S3) were randomly generated (70% for training and 30% for validation). A total of 10 groundwater conditioning factors were prepared for modeling, namely slope percent, relative slope position (RSP), plan curvature, altitude, drainage density, slope aspect, topographic wetness index (TWI), terrain ruggedness index (TRI), land use, and lithology. The area under the receiver operating characteristic curve (AUC) and true skill statistic (TSS) were used to evaluate the accuracy of models. The results indicated that all models had excellent goodness-of-fit and predictive performance, but that RF (AUCmean = 0.995, TSSmean = 0.89) and GARP (AUCmean = 0.957, TSSmean = 0.82) outperformed QUEST (AUCmean = 0.949, TSSmean = 0.74). In robustness analysis, RF was slightly more sensitive than GARP and QUEST, making it necessary to consider several random partitioning options for preparing training and validation groups. The outcomes of this study can be useful in sustainable management of groundwater resources in the study region.

Highlights

Groundwater is the most vital natural resource in many arid and semi-arid regions, where it is often the only source of water for human needs
The outputs of multicollinearity analysis revealed that the highest variance inflation factor (VIF) value was 3.547 and the lowest TOL value was 0.238, which shows that there was no multicollinearity among the predictive factors (Table 2)
This study evaluated the capability of two machine learning techniques, Genetic Algorithm for Rule-Set Production (GARP) and Quick Unbiased Efficient Statistical Tree (QUEST), for predicting groundwater potential for the first time, and compared their capability and robustness with that of Random Forest (RF), a state-of-the-art model

Summary

Introduction

Groundwater is the most vital natural resource in many arid and semi-arid regions, where it is often the only source of water for human needs. Population growth leads to increasing demand for water in the domestic, industrial, and agricultural sectors. In both rural and urban areas in arid and semi-arid regions, almost 90% of all water used primarily derives from groundwater [1]. As the demand for groundwater increases around the world, groundwater potential mapping is becoming an essential tool in enabling successful groundwater management schemes, determination, and safety [2]. The methods used for groundwater potential mapping can be grouped into two main categories: knowledge-driven techniques based on the conceptual component and data-driven techniques based

Objectives

Discussion

Conclusion