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).
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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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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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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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DOI: https://doi.org/10.1007/s10668-024-04687-2