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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Veröffentlicht in:Atmosphere Jg. 16; H. 2; S. 213
Hauptverfasser: Bounab, Rayane, Boutaghane, Hamouda, Boulmaiz, Tayeb, Tramblay, Yves
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.02.2025
MDPI
Schlagworte:
Algeria
Algorithms
Artificial intelligence
Basins
Comparative analysis
Computer Aided Engineering
Computer Science
Daily forecasts
Daily runoff
Dams
Forecasts and trends
Global precipitation
Hydrologic data
Hydrologic models
Hydrology
Learning algorithms
Machine learning
Meteorological satellites
Neural networks
Ocean, Atmosphere
Precipitation
Precipitation data
Precipitation monitoring
Rain
Rain and rainfall
Rainfall
Rainfall data
Rainfall-runoff modeling
Rainfall-runoff relationships
rainfall–runoff simulation
River discharge
River flow
River runoff
Rivers
Runoff
Runoff forecasting
Runoff models
satellite rainfall
Sciences of the Universe
Stream flow
Surface water
Surface water resources
Water discharge
Water management
Water monitoring
Water resources
Water resources management
Watersheds
Algeria
North Africa
Rainfall–runoff simulation
Satellite rainfall
Algeria
Hydrologic models
Machine learning
ISSN:2073-4433, 2073-4433
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Zusammenfassung: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.
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ISSN:2073-4433
2073-4433
DOI:10.3390/atmos16020213