TY  - JOUR
AU  - Aderemi, B. A.
AU  - Olwal, T. O.
AU  - Ndambuki, J. M.
AU  - Rwanga, S. S.
PY  - 2023//
TI  - Groundwater levels forecasting using machine learning models: A case study of the groundwater region 10 at Karst Belt, South Africa
JO  - Systems and Soft Computing
SP  - 200049
VL  - 5
KW  - Artificial intelligence
KW  - Forecasting model
KW  - Groundwater levels
KW  - Machine learning
KW  - Neural networks
KW  - Rainfall
KW  - Regression
KW  - Temperature
KW  - Time series
N2  - The crucial role which groundwater resource plays in our environment and the overall well-being of all living things can not be underestimated. Nonetheless, mismanagement of resources, over-exploitation, inadequate supply of surface water and pollution have led to severe drought and an overall drop in groundwater resources’ levels over the past decades. To address this, a groundwater flow model and several mathematical data-driven models have been developed for forecasting groundwater levels. However, there is a problem of unavailability and scarcity of the on-site input data needed by the data-driven models to forecast the groundwater level. Furthermore, as a result of the dynamics and stochastic characteristics of groundwater, there is a need for an appropriate, accurate and reliable forecasting model to solve these challenges. Over the years, the broad application of Machine Learning (ML) and Artificial Intelligence (AI) models are gaining attraction as an alternative solution for forecasting groundwater levels. Against this background, this article provides an overview of forecasting methods for predicting groundwater levels. Also, this article uses ML models such as Regressions Models, Deep Auto-Regressive models, and Nonlinear Autoregressive Neural Networks with External Input (NARX) to forecast groundwater levels using the groundwater region 10 at Karst belt in South Africa as a case study. This was done using Python Mx. Version 1.9.1., and MATLAB R2022a machine learning toolboxes. Moreover, the Coefficient of Determination (R2);, Root Mean Square Error (RMSE), Mutual Information gain, Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Mean Absolute Error (MAE), and the Mean Absolute Scaled Error (MASE)) models were the forecasting statistical performance metrics used to assess the predictive performance of these models. The results obtained showed that NARX and Support Vector Machine (SVM) have higher performance metrics and accuracy compared to other models used in this study.
SN  - 2772-9419
UR  - https://www.sciencedirect.com/science/article/pii/S2772941923000029
N1  - exported from refbase (http://www.uhydro.de/base/show.php?record=219), last updated on Fri, 26 Jan 2024 19:51:25 +0100
ID  - Aderemi_etal2023
ER  -