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Aderemi, B.A.; Olwal, T.O.; Ndambuki, J.M.; Rwanga, S.S. |
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Groundwater levels forecasting using machine learning models: A case study of the groundwater region 10 at Karst Belt, South Africa |
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Journal Article |
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2023 |
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Systems and Soft Computing |
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5 |
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200049 |
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Artificial intelligence, Forecasting model, Groundwater levels, Machine learning, Neural networks, Rainfall, Regression, Temperature, Time series |
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Abstract |
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. |
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2772-9419 |
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THL @ christoph.kuells @ Aderemi2023200049 |
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219 |
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Rajfur, M.; Kłos, A.; Wacławek, M. |
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Title |
Sorption properties of algae Spirogyra sp. and their use for determination of heavy metal ions concentrations in surface water |
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Journal Article |
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Year |
2010 |
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Bioelectrochemistry |
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80 |
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1 |
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81-86 |
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Biomonitoring, Heavy metal ions, Algae sp., Sorption kinetics, Langmuir isotherm |
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Kinetics of heavy-metal ions sorption by alga Spirogyra sp. was evaluated experimentally in the laboratory, using both the static and the dynamic approach. The metal ions – Mn2+, Cu2+, Zn2+ and Cd2+ – were sorbed from aqueous solutions of their salts. The static experiments showed that the sorption equilibria were attained in 30min, with 90-95% of metal ions sorbed in first 10min of each process. The sorption equilibria were approximated with the Langmuir isotherm model. The algae sorbed each heavy metal ions proportionally to the amount of this metal ions in solution. The experiments confirmed that after 30min of exposition to contaminated water, the concentration of heavy metal ions in the algae, which initially contained small amounts of these metal ions, increased proportionally to the concentration of metal ions in solution. The presented results can be used for elaboration of a method for classification of surface waters that complies with the legal regulations. |
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1567-5394 |
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A Selection of Papers presented at the 4th International Workshop on Surface Modification for Chemical and Biochemical Sensing (SMCBS 2009) |
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THL @ christoph.kuells @ Rajfur201081 |
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283 |
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Wolfe, P. |
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The Simplex Method For Quadratic Programming |
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1959 |
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Econometrica |
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27 |
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170 |
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THL @ christoph.kuells @ Wolfe1959 |
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285 |
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United Nations |
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Stampriet Transboundary Aquifer System Assessment: governance of Groundwater resources in Transboundary Aquifers (GGRETA), phase 1: technical report |
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Miscellaneous |
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1998 |
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Incl. bibl. |
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THL @ christoph.kuells @ |
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286 |
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Krüger, N.; Külls, C.; Bruggeman, A.; Eliades, M.; Christophi, C.; Rigas, M.; Eracleous, T. |
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Title |
Groundwater recharge estimates with soil isotope profiles-is there a bias on coarse-grained hillslopes? |
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2020 |
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EGU General Assembly Conference Abstracts |
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9840 |
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THL @ christoph.kuells @ Krueger2020groundwater |
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42 |
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