Groundwater potentiality mapping using ensemble machine learning algorithms for sustainable groundwater management

Showmitra Kumar Sarkar,Shahfahad Shahfahad,Sujit Kumar Roy,Swapan Talukdar,Atiqur Rahman

doi:10.1108/febe-09-2021-0044

Abstract

PurposeThe present study aims to construct ensemble machine learning (EML) algorithms for groundwater potentiality mapping (GPM) in the Teesta River basin of Bangladesh, including random forest (RF) and random subspace (RSS).Design/methodology/approachThe RF and RSS models have been implemented for integrating 14 selected groundwater condition parametres with groundwater inventories for generating GPMs. The GPM were then validated using the empirical and bionormal receiver operating characteristics (ROC) curve.FindingsThe very high (831–1200 km2) and high groundwater potential areas (521–680 km2) were predicted using EML algorithms. The RSS (AUC-0.892) model outperformed RF model based on ROC's area under curve (AUC).Originality/valueTwo new EML models have been constructed for GPM. These findings will aid in proposing sustainable water resource management plans.

Highlights

IntroductionGroundwater is the world’s largest source of freshwater (i.e. one-third of worldwide freshwater consumption) but there is a shortage of data at a micro-spatial level on the
Groundwater is the world’s largest source of freshwater but there is a shortage of data at a micro-spatial level on the© Showmitra Kumar Sarkar, Swapan Talukdar, Atiqur Rahman, Shahfahad and Sujit Kumar Roy
Curvature map, which was produced by using the digital elevation model (DEM) ranged from 0.32–0.82 (Figure 2a)

Summary

Introduction

Groundwater is the world’s largest source of freshwater (i.e. one-third of worldwide freshwater consumption) but there is a shortage of data at a micro-spatial level on the. © Showmitra Kumar Sarkar, Swapan Talukdar, Atiqur Rahman, Shahfahad and Sujit Kumar Roy. Published in Frontiers in Engineering and Built Environment. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

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