Comparison of Machine Learning Models for Real-Time Flow Forecasting in the Semi-Arid Bouregreg Basin
Morocco is geographically located between two distinct climatic zones: temperate in the north and tropical in the south. This situation is the reason for the temporal and spatial variability of the Moroccan climate. In recent years, the increasing scarcity of water resources, exacerbated by climate...
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MDPI AG
2025-03-01
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| author | Fatima Zehrae Elhallaoui Oueldkaddour Fatima Wariaghli Hassane Brirhet Ahmed Yahyaoui Hassane Jaziri |
| author_facet | Fatima Zehrae Elhallaoui Oueldkaddour Fatima Wariaghli Hassane Brirhet Ahmed Yahyaoui Hassane Jaziri |
| author_sort | Fatima Zehrae Elhallaoui Oueldkaddour |
| collection | DOAJ |
| description | Morocco is geographically located between two distinct climatic zones: temperate in the north and tropical in the south. This situation is the reason for the temporal and spatial variability of the Moroccan climate. In recent years, the increasing scarcity of water resources, exacerbated by climate change, has underscored the critical role of dams as essential water reservoirs. These dams serve multiple purposes, including flood management, hydropower generation, irrigation, and drinking water supply. Accurate estimation of reservoir flow rates is vital for effective water resource management, particularly in the context of climate variability. The prediction of monthly runoff time series is a key component of water resources planning and development projects. In this study, we employ Machine Learning (ML) techniques—specifically, Random Forest (RF), Support Vector Regression (SVR), and XGBoost—to predict monthly river flows in the Bouregreg basin, using data collected from the Sidi Mohamed Ben Abdellah (SMBA) Dam between 2010 and 2020. The primary objective of this paper is to comparatively evaluate the applicability of these three ML models for flow forecasting in the Bouregreg River. The models’ performance was assessed using three key criteria: the correlation coefficient (R<sup>2</sup>), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). The results demonstrate that the SVR model outperformed the RF and XGBoost models, achieving high accuracy in flow prediction. These findings are highly encouraging and highlight the potential of machine learning approaches for hydrological forecasting in semi-arid regions. Notably, the models used in this study are less data-intensive compared to traditional methods, addressing a significant challenge in hydrological modeling. This research opens new avenues for the application of ML techniques in water resource management and suggests that these methods could be generalized to other basins in Morocco, promoting efficient, effective, and integrated water resource management strategies. |
| format | Article |
| id | doaj-art-b28a87d8d6504718918f394dc5c7fc98 |
| institution | DOAJ |
| issn | 2300-7575 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Limnological Review |
| spelling | doaj-art-b28a87d8d6504718918f394dc5c7fc982025-08-20T02:42:25ZengMDPI AGLimnological Review2300-75752025-03-01251610.3390/limnolrev25010006Comparison of Machine Learning Models for Real-Time Flow Forecasting in the Semi-Arid Bouregreg BasinFatima Zehrae Elhallaoui Oueldkaddour0Fatima Wariaghli1Hassane Brirhet2Ahmed Yahyaoui3Hassane Jaziri4Laboratory of Biodiversity, Ecology and Genome, Faculty of Sciences, University Mohammed V in Rabat, Rabat 10000, MoroccoLaboratory of Biodiversity, Ecology and Genome, Faculty of Sciences, University Mohammed V in Rabat, Rabat 10000, MoroccoDepartment of Water Research and Planning, Ministry of Equipment, Transport, Logistics and Water, Rabat 10000, MoroccoLaboratory of Biodiversity, Ecology and Genome, Faculty of Sciences, University Mohammed V in Rabat, Rabat 10000, MoroccoLaboratory of Biodiversity, Ecology and Genome, Faculty of Sciences, University Mohammed V in Rabat, Rabat 10000, MoroccoMorocco is geographically located between two distinct climatic zones: temperate in the north and tropical in the south. This situation is the reason for the temporal and spatial variability of the Moroccan climate. In recent years, the increasing scarcity of water resources, exacerbated by climate change, has underscored the critical role of dams as essential water reservoirs. These dams serve multiple purposes, including flood management, hydropower generation, irrigation, and drinking water supply. Accurate estimation of reservoir flow rates is vital for effective water resource management, particularly in the context of climate variability. The prediction of monthly runoff time series is a key component of water resources planning and development projects. In this study, we employ Machine Learning (ML) techniques—specifically, Random Forest (RF), Support Vector Regression (SVR), and XGBoost—to predict monthly river flows in the Bouregreg basin, using data collected from the Sidi Mohamed Ben Abdellah (SMBA) Dam between 2010 and 2020. The primary objective of this paper is to comparatively evaluate the applicability of these three ML models for flow forecasting in the Bouregreg River. The models’ performance was assessed using three key criteria: the correlation coefficient (R<sup>2</sup>), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). The results demonstrate that the SVR model outperformed the RF and XGBoost models, achieving high accuracy in flow prediction. These findings are highly encouraging and highlight the potential of machine learning approaches for hydrological forecasting in semi-arid regions. Notably, the models used in this study are less data-intensive compared to traditional methods, addressing a significant challenge in hydrological modeling. This research opens new avenues for the application of ML techniques in water resource management and suggests that these methods could be generalized to other basins in Morocco, promoting efficient, effective, and integrated water resource management strategies.https://www.mdpi.com/2300-7575/25/1/6flow forecastingtime seriesmachine learningRandom Forest modelSVR modelXGBoost model |
| spellingShingle | Fatima Zehrae Elhallaoui Oueldkaddour Fatima Wariaghli Hassane Brirhet Ahmed Yahyaoui Hassane Jaziri Comparison of Machine Learning Models for Real-Time Flow Forecasting in the Semi-Arid Bouregreg Basin Limnological Review flow forecasting time series machine learning Random Forest model SVR model XGBoost model |
| title | Comparison of Machine Learning Models for Real-Time Flow Forecasting in the Semi-Arid Bouregreg Basin |
| title_full | Comparison of Machine Learning Models for Real-Time Flow Forecasting in the Semi-Arid Bouregreg Basin |
| title_fullStr | Comparison of Machine Learning Models for Real-Time Flow Forecasting in the Semi-Arid Bouregreg Basin |
| title_full_unstemmed | Comparison of Machine Learning Models for Real-Time Flow Forecasting in the Semi-Arid Bouregreg Basin |
| title_short | Comparison of Machine Learning Models for Real-Time Flow Forecasting in the Semi-Arid Bouregreg Basin |
| title_sort | comparison of machine learning models for real time flow forecasting in the semi arid bouregreg basin |
| topic | flow forecasting time series machine learning Random Forest model SVR model XGBoost model |
| url | https://www.mdpi.com/2300-7575/25/1/6 |
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