Groundwater potentiality mapping using ensemble machine learning algorithms for sustainable groundwater management. Issue 1 (2nd November 2021)

• Record Type: Journal Article
• Authors: Sarkar, Showmitra Kumar; Talukdar, Swapan; Rahman, Atiqur; Shahfahad, ; Roy, Sujit Kumar
• Is Part Of: Frontiers in engineering and built environment. Volume 2:Issue 1(2022)
• Page Start: 43
• Page End: 54
• Publication Date: 2021-11-02
• Subjects: Groundwater potentiality -- Data mining -- GIS -- Remote sensing -- Random subspace; Engineering; Civil engineering; Environmental engineering; Geotechnical engineering; Periodicals; 620
• DOI: 10.1108/FEBE-09-2021-0044
• Languages: English
• ISSNs: 2634-2499

Abstract

Abstract : Purpose: The 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/approach: The 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. Findings: The very high (831–1200 km 2 ) and high groundwater potential areas (521–680 km 2 ) were predicted using EML algorithms. The RSS (AUC-0.892) model outperformed RF model based on ROC's area under curve (AUC). Originality/value: Two new EML models have been constructed for GPM. These findings will aid in proposing sustainable water resource management plans.