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Mapping groundwater potentiality by using hybrid machine learning models under the scenario of climate variability: a national level study of Bangladesh

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Abstract

A severe threat to natural resources and human livelihood is groundwater scarcity. Therefore, mapping groundwater potentiality (GWP) is necessary for future resource management. In this article, a framework for conducting ensemble modeling is introduced. This framework is used to map GWP at the national level under the scenario of climatic variability. Thirteen elements linked to topography, geology, hydrology, and land cover, as well as six climatic indicators based on historical time series data, were used to map the GWP. This study has used three conventional machine learning algorithms (< MLAs), such as logistic model tree, logistic regression, and artificial neural network and five ensemble models by combining standalone models with random forest under stacking framework to produce GWP map. Using the empirical and binormal receiver operating characteristic curves, the GWP mapping has been validated. Result shows that Bangladesh's major rivers run along the high GWP zones in the country's southern and central regions. In addition, the validation using the area under curve (AUC) of ROC curve demonstrates that the stacking model which combined all three MLAs outperformed other models (AUC: 0.971). The findings of this study may help the authorities and stakeholders to formulate the adequate groundwater management plans at national level. In addition, the suggested method might be applied to map GWP on a broader scale in additional nations as well as at the continental level.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

ANN:

Artificial neural network

AUC:

Area under curve

BMD:

Bangladesh meteorological department

CART:

Classification and regression tree

CDD:

Consecutive dry days

DEM:

Digital elevation model

EML:

Ensemble machine learning

ET:

Evapotranspiration

ETCCDI:

Expert team on climate change detection and indices

FPR:

False positive rate

GIS:

Geographic information systems

GWP:

Groundwater potentiality

IDW:

Inverse distance weighted

LULC:

Land use land cover

LMT:

Logistic model tree

LR:

Logistic regression

MDA:

Mean decrease accuracy

MDG:

Mean decrease gini

ML:

Machine learning

MLA:

Machine learning algorithm

MMK:

Modified Mann–Kendall

OOB:

Out-of-bag

RF:

Random forest

ROC:

Receiver operating characteristic

SPI:

Stream power index

SRTM:

Shuttle radar topography mission

STI:

Sediment transport index

SVM:

Support vector machine

TFPW:

Trend-free pre-whitening

TPI:

Topographic position index

TPR:

True positive rate

TRI:

Topographic roughness index

TWI:

Topographic wetness index

USGS:

United States geological survey

VIF:

Variance inflation factors

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Acknowledgements

The authors extend their appreciation to the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project no. (IFKSUOR3- 622-1).

Funding

This research has been funded by the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project no. (IFKSUOR3- 622-1).

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Authors and Affiliations

  1. Department of Urban and Regional Planning, Khulna University of Engineering and Technology, Khulna, 9203, Bangladesh

    Showmitra Kumar Sarkar

  2. Abdullah Alrushaid Chair for Earth Science Remote Sensing Research, Geology and Geophysics Department, King Saud University, P.O. Box 2455, 11451, Riyadh, Saudi Arabia

    Fahad Alshehri & Muhammad Shahab

  3. Department of Geography, Faculty of Sciences, Jamia Millia Islamia, New Delhi, 110025, India

    Shahfahad & Atiqur Rahman

  4. The Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Information, Systems and Modelling, University of Technology Sydney, Ultimo, NSW, 2007, Australia

    Biswajeet Pradhan

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SKS and S. designed the study and was responsible for the data collection as well as analysis of the data and wrote the initial draft; FA and AM were responsible for the data analysis, data curation and modeling as well as editing of the initial draft and supervised the project; BP helped in the data preparation as well as provided technical support; AR provided software guidance, helped in validation as well as reviewed the final manuscript.

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Correspondence to Fahad Alshehri.

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Sarkar, S.K., Alshehri, F., Shahfahad et al. Mapping groundwater potentiality by using hybrid machine learning models under the scenario of climate variability: a national level study of Bangladesh. Environ Dev Sustain (2024). https://doi.org/10.1007/s10668-024-04687-2

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