A comparison of ensemble and deep learning algorithms to model groundwater levels in a data-scarce aquifer of Southern Africa

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gaffoor_a comparison of ensemble_2022.pdf (2.5 MB)

Date

2022

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MDPI

Abstract

Machine learning and deep learning have demonstrated usefulness in modelling various groundwater phenomena. However, these techniques require large amounts of data to develop reliable models. In the Southern African Development Community, groundwater datasets are generally poorly developed. Hence, the question arises as to whether machine learning can be a reliable tool to support groundwater management in the data-scarce environments of Southern Africa. This study tests two machine learning algorithms, a gradient-boosted decision tree (GBDT) and a long short-term memory neural network (LSTM-NN), to model groundwater level (GWL) changes in the Shire Valley Alluvial Aquifer.

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Keywords

Groundwater, Neural networks, Machine learning, South Africa, Earth science

Citation

Gaffoor, Z. et al. (2022). A comparison of ensemble and deep learning algorithms to model groundwater levels in a data-scarce aquifer of Southern Africa. Hydrology, 9(7), 25. https://doi.org/10.3390/hydrology9070125

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https://doi.org/10.3390/hydrology9070125
 http://hdl.handle.net/10566/7785

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Research Articles (Earth Sciences)