Groundwater levels forecasting using machine learning models: A case study of the groundwater region 10 at Karst Belt, South Africa
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...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-12-01
|
| Series: | Systems and Soft Computing |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772941923000029 |
| _version_ | 1797448746331537408 |
|---|---|
| author | Banjo A. Aderemi Thomas O. Olwal Julius M. Ndambuki Sophia S. Rwanga |
| author_facet | Banjo A. Aderemi Thomas O. Olwal Julius M. Ndambuki Sophia S. Rwanga |
| author_sort | Banjo A. Aderemi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T14:14:54Z |
| format | Article |
| id | doaj.art-9934142749854a1082aac7cbd268d43e |
| institution | Directory Open Access Journal |
| issn | 2772-9419 |
| language | English |
| last_indexed | 2024-03-09T14:14:54Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Systems and Soft Computing |
| spelling | doaj.art-9934142749854a1082aac7cbd268d43e2023-11-29T04:25:16ZengElsevierSystems and Soft Computing2772-94192023-12-015200049Groundwater levels forecasting using machine learning models: A case study of the groundwater region 10 at Karst Belt, South AfricaBanjo A. Aderemi0Thomas O. Olwal1Julius M. Ndambuki2Sophia S. Rwanga3Department of Electrical Engineering, Tshwane University of Technology Pretoria, South Africa; Corresponding author.Department of Electrical Engineering, Tshwane University of Technology Pretoria, South AfricaDepartment of Civil Engineering, Tshwane University of Technology Pretoria, South AfricaDepartment of Civil Engineering, Vaal University of Technology Vanderbijlpark, South AfricaThe 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.http://www.sciencedirect.com/science/article/pii/S2772941923000029Artificial intelligenceForecasting modelGroundwater levelsMachine learningNeural networksRainfall |
| spellingShingle | Banjo A. Aderemi Thomas O. Olwal Julius M. Ndambuki Sophia S. Rwanga Groundwater levels forecasting using machine learning models: A case study of the groundwater region 10 at Karst Belt, South Africa Systems and Soft Computing Artificial intelligence Forecasting model Groundwater levels Machine learning Neural networks Rainfall |
| title | Groundwater levels forecasting using machine learning models: A case study of the groundwater region 10 at Karst Belt, South Africa |
| title_full | Groundwater levels forecasting using machine learning models: A case study of the groundwater region 10 at Karst Belt, South Africa |
| title_fullStr | Groundwater levels forecasting using machine learning models: A case study of the groundwater region 10 at Karst Belt, South Africa |
| title_full_unstemmed | Groundwater levels forecasting using machine learning models: A case study of the groundwater region 10 at Karst Belt, South Africa |
| title_short | Groundwater levels forecasting using machine learning models: A case study of the groundwater region 10 at Karst Belt, South Africa |
| title_sort | groundwater levels forecasting using machine learning models a case study of the groundwater region 10 at karst belt south africa |
| topic | Artificial intelligence Forecasting model Groundwater levels Machine learning Neural networks Rainfall |
| url | http://www.sciencedirect.com/science/article/pii/S2772941923000029 |
| work_keys_str_mv | AT banjoaaderemi groundwaterlevelsforecastingusingmachinelearningmodelsacasestudyofthegroundwaterregion10atkarstbeltsouthafrica AT thomasoolwal groundwaterlevelsforecastingusingmachinelearningmodelsacasestudyofthegroundwaterregion10atkarstbeltsouthafrica AT juliusmndambuki groundwaterlevelsforecastingusingmachinelearningmodelsacasestudyofthegroundwaterregion10atkarstbeltsouthafrica AT sophiasrwanga groundwaterlevelsforecastingusingmachinelearningmodelsacasestudyofthegroundwaterregion10atkarstbeltsouthafrica |