Assessing the nature of potential groundwater zones through machine learning (ML) algorithm in tropical plateau region, West Bengal, India

Kundu, Moumita; Zafor, Abu; Maiti, Ramakrishna

doi:10.1007/s11600-023-01042-3

Artykuł - szczegóły

Tytuł artykułu

Assessing the nature of potential groundwater zones through machine learning (ML) algorithm in tropical plateau region, West Bengal, India

Autorzy

Kundu Moumita , Zafor Abu , Maiti Ramakrishna

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

DOI

10.1007/s11600-023-01042-3

Warianty tytułu

Języki publikacji

Abstrakty

Groundwater recharge is essential for managing surface and subsoil water resources. Not only for supplying people with daily drinking water, groundwater use for agricultural land and people's livelihood is also continuously increasing. As a result, there has been a decline in groundwater supply in various parts of the world, and it is highly desirable to identify the potential groundwater zones for specific sustainable development. This study aims to use an easy-to-use tool package named Landslide Sustainability Mapping Tool pack (LSM tool Pack) for preparing potential groundwater zone based on R and ArcGIS software integration. This tool uses five modules for processing. Among them, the Feature selection (FS) module brings a novel approach, determining the best subset feature for demarcating the groundwater potential zone. As a result, this best factor subset is used as an input of this tool pack. Additionally, PE modules evaluate the performance of proposed models in statistical performance metrics. In addition, the receiver operating characteristic (ROC) curve was obtained with the integration of Performance Evaluation (PE) modules and ARC maps, which helps visual interpretation in evaluating models. This study uses the LSM tool Pack in the Rupnarayan river basin to map the potential groundwater zone based on fourteen controlling factors selected through the FS module, which will further help the local government to make a substitute policy.

Słowa kluczowe

groundwater potential zone machine learning LSM tool pack ROC curve

strefa potencjału wód gruntowych nauczanie maszynowe pakiet narzędzi LSM krzywa ROC

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2024

Tom

Vol. 72, no. 1

Strony

433--448

Opis fizyczny

Bibliogr. 53 poz.

Twórcy

autor

Kundu Moumita

moumita.kundu1002@gmail.com

Department of Geography, Vidyasagar University, Midnapore, West Bengal, India

autor

Zafor Abu

zaforce@baiust.edu.bd

Department of Civil Engineering, Bangladesh Army International, University of Science and Technology, Cumilla, Bangladesh

autor

Maiti Ramakrishna

ramkrishna@mail.vidyasagar.ac.in

Department of Geography, Vidyasagar University, Midnapore, West Bengal, India

Bibliografia

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Bibliografia

Identyfikator YADDA

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