Comparison of Machine Learning Algorithms for Daily Runoff Forecasting with Global Rainfall Products in Algeria
Rainfall–runoff models are crucial tools for managing water resources. The absence of reliable rainfall data in many regions of the world is a major limitation for these models, notably in many African countries, although some recent global rainfall products can effectively monitor rainfall from spa...
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MDPI AG
2025-02-01
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| Online Access: | https://www.mdpi.com/2073-4433/16/2/213 |
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| author | Rayane Bounab Hamouda Boutaghane Tayeb Boulmaiz Yves Tramblay |
| author_facet | Rayane Bounab Hamouda Boutaghane Tayeb Boulmaiz Yves Tramblay |
| author_sort | Rayane Bounab |
| collection | DOAJ |
| description | Rainfall–runoff models are crucial tools for managing water resources. The absence of reliable rainfall data in many regions of the world is a major limitation for these models, notably in many African countries, although some recent global rainfall products can effectively monitor rainfall from space. In Algeria, to identify a relevant modeling approach using this new source of rainfall information, the present research aims to (i) compare a conceptual model (GR4J) and seven machine learning algorithms (FFNN, ELM, LSTM, LSTM2, GRU, SVM, and GPR) and (ii) compare different types of precipitation inputs, including four satellite products (CHIRPS, SM2RAIN, GPM, and PERSIANN), one reanalysis product (ERA5), and observed precipitation, to assess which combination of models and precipitation data provides the optimal performance for river discharge simulation. The results show that the ELM, FFNN, and LSTM algorithms give the best performance (NSE > 0.6) for river runoff simulation and provide reliable alternatives compared to a conceptual hydrological model. The SM2RAIN-ASCAT and ERA5 rainfall products are as efficient as observed precipitation in this data-scarce context. Consequently, this work is the first step towards the implementation of these tools for the operational monitoring of surface water resources in Algeria. |
| format | Article |
| id | doaj-art-cd2d0a365fe24c7f9155c5fdd31f8637 |
| institution | DOAJ |
| issn | 2073-4433 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj-art-cd2d0a365fe24c7f9155c5fdd31f86372025-08-20T03:12:01ZengMDPI AGAtmosphere2073-44332025-02-0116221310.3390/atmos16020213Comparison of Machine Learning Algorithms for Daily Runoff Forecasting with Global Rainfall Products in AlgeriaRayane Bounab0Hamouda Boutaghane1Tayeb Boulmaiz2Yves Tramblay3Laboratory of Soils and Hydraulic, Badji Mokhtar Annaba University Annaba, Annaba 23000, AlgeriaLaboratory of Soils and Hydraulic, Badji Mokhtar Annaba University Annaba, Annaba 23000, AlgeriaMaterials, Energy Systems Technology and Environment Laboratory, Ghardaia University, Ghardaia 47000, AlgeriaEspace-Dev (University Montpellier, IRD), 34000 Montpellier, FranceRainfall–runoff models are crucial tools for managing water resources. The absence of reliable rainfall data in many regions of the world is a major limitation for these models, notably in many African countries, although some recent global rainfall products can effectively monitor rainfall from space. In Algeria, to identify a relevant modeling approach using this new source of rainfall information, the present research aims to (i) compare a conceptual model (GR4J) and seven machine learning algorithms (FFNN, ELM, LSTM, LSTM2, GRU, SVM, and GPR) and (ii) compare different types of precipitation inputs, including four satellite products (CHIRPS, SM2RAIN, GPM, and PERSIANN), one reanalysis product (ERA5), and observed precipitation, to assess which combination of models and precipitation data provides the optimal performance for river discharge simulation. The results show that the ELM, FFNN, and LSTM algorithms give the best performance (NSE > 0.6) for river runoff simulation and provide reliable alternatives compared to a conceptual hydrological model. The SM2RAIN-ASCAT and ERA5 rainfall products are as efficient as observed precipitation in this data-scarce context. Consequently, this work is the first step towards the implementation of these tools for the operational monitoring of surface water resources in Algeria.https://www.mdpi.com/2073-4433/16/2/213Algeriamachine learninghydrologic modelsrainfall–runoff simulationsatellite rainfall |
| spellingShingle | Rayane Bounab Hamouda Boutaghane Tayeb Boulmaiz Yves Tramblay Comparison of Machine Learning Algorithms for Daily Runoff Forecasting with Global Rainfall Products in Algeria Atmosphere Algeria machine learning hydrologic models rainfall–runoff simulation satellite rainfall |
| title | Comparison of Machine Learning Algorithms for Daily Runoff Forecasting with Global Rainfall Products in Algeria |
| title_full | Comparison of Machine Learning Algorithms for Daily Runoff Forecasting with Global Rainfall Products in Algeria |
| title_fullStr | Comparison of Machine Learning Algorithms for Daily Runoff Forecasting with Global Rainfall Products in Algeria |
| title_full_unstemmed | Comparison of Machine Learning Algorithms for Daily Runoff Forecasting with Global Rainfall Products in Algeria |
| title_short | Comparison of Machine Learning Algorithms for Daily Runoff Forecasting with Global Rainfall Products in Algeria |
| title_sort | comparison of machine learning algorithms for daily runoff forecasting with global rainfall products in algeria |
| topic | Algeria machine learning hydrologic models rainfall–runoff simulation satellite rainfall |
| url | https://www.mdpi.com/2073-4433/16/2/213 |
| work_keys_str_mv | AT rayanebounab comparisonofmachinelearningalgorithmsfordailyrunoffforecastingwithglobalrainfallproductsinalgeria AT hamoudaboutaghane comparisonofmachinelearningalgorithmsfordailyrunoffforecastingwithglobalrainfallproductsinalgeria AT tayebboulmaiz comparisonofmachinelearningalgorithmsfordailyrunoffforecastingwithglobalrainfallproductsinalgeria AT yvestramblay comparisonofmachinelearningalgorithmsfordailyrunoffforecastingwithglobalrainfallproductsinalgeria |