A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Groundwater potentiality mapping using ensemble machine learning algorithms for sustainable groundwater management
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 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/value – Two new EML models have been constructed for GPM. These findings will aid in proposing sustainable water resource management plans.
Groundwater potentiality mapping using ensemble machine learning algorithms for sustainable groundwater management
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 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/value – Two new EML models have been constructed for GPM. These findings will aid in proposing sustainable water resource management plans.
Groundwater potentiality mapping using ensemble machine learning algorithms for sustainable groundwater management
Showmitra Kumar Sarkar (author) / Swapan Talukdar (author) / Atiqur Rahman (author) / Shahfahad (author) / Sujit Kumar Roy (author)
2022
Article (Journal)
Electronic Resource
Unknown
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