Comparison of Machine Learning Algorithms for Daily Runoff Forecasting with Global Rainfall Products in Algeria
Abstract
:1. Introduction
- Compare four different satellite rainfall products with high spatial and temporal resolution: Global Precipitation Measurement Integrated Multi-Satellite Retrievals (GPM-IMERGs), Climate Hazards Group Infrared Precipitation with Station data (CHIRPS), Precipitation Estimation from Remote Sensed Information using Artificial Neural Networks (PERSIANN), Soil Moisture to Rain (SM2RAIN-ASCAT), and a reanalysis product (ERA5).
- Simulate river discharge from satellite rainfall and evaluate the performance of eight types of models: the 4-Parameter Daily Rural Engineering model (GR4J), feed-forward neural networks (FFNNs), extreme machine learning (ELM), long short-term memory (LSTM), LSTM2, gated recurrent unit (GRU), Gaussian process regression (GPR), and Support Vector Machine (SVM).
2. Materials and Methods
2.1. Study Area
2.2. Datasets
2.2.1. Global Precipitation Products
GPM-IMERG
SM2RAIN-ASCAT
PERSIANN-CCS-CDR
CHIRPS
ERA5
2.2.2. Evapotranspiration Data
2.3. Methods
2.3.1. Analysis of Catchment Response Time
2.3.2. Conceptual Hydrological Model (GR4J)
2.3.3. Machine Learning Models
Feed-Forward Neural Network (FFNN)
Extreme Learning Machine (ELM)
Long Short-Term Memory (LSTM)
Gated Recurrent Unit (GRU)
Gaussian Process Regression (GPR)
Support Vector Machine (SVM)
2.3.4. Hydrological Model Evaluation
- -
- Validation with GR4J
- -
- Validation with Machine Learning Techniques
2.3.5. Efficiency Criteria
2.3.6. Taylor Diagram
3. Results
3.1. Impact of Time Lag Between Rainfall and Runoff on Hydrological Forecast Accuracy
3.2. Which Global Rainfall Product Is the Most Effective?
3.3. The Most Effective Model Structure
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Products Evaluated | Study Region | Approach Used | Main Results and Performance | Best Performing Product | Reference |
---|---|---|---|---|---|
ERA5, soil moisture products | Morocco | Flood modeling | ERA5 and SM2RAIN-ASCAT provide a better estimate of soil moisture conditions for flood prediction. | ERA5 and ASCAT | El Khalki et al. (2020) [2] |
GPM, CHIRPS | India | Deep Learning modeling | Deep learning approaches improve rainfall–runoff simulation. GPM (R2 = 0.84, RMSE = 10.5 mm) and CHIRPS (R2 = 0.80, RMSE = 11.8 mm) offer good spatialization. | CHIRPS | Yeditha et al. (2021) [23] |
EUMETSAT H SAF, SM2RAIN-ASCAT, IMERG | Morocco | Rainfall–runoff simulation | SM2RAIN-ASCAT shows a strong correlation, particularly in basins with high interannual variability. | SM2RAIN-ASCAT | Tramblay et al. (2023) [24] |
SM2RAIN, rain gauges | South Asia | Comparison with observations | SM2RAIN-ASCAT offers precision compared with the other SM2RAIN and rain gauges. | SM2RAIN-ASCAT | Satgé et al. (2021) [28] |
GPM-IMERG, CMORPH, TRMM | Tibetan Plateau | Hydrological assessment | The GPM-IMERG products show robust results for the evaluation of precipitation in relation to the TRMM and CMORPH. | GPM-IMERG | Alazzy et al. (2017) [29] |
PERSIANN, ERA5, GPM, TRMM, merged products, etc. | Turkey | Statistical comparison | ERA5 shows robust results for precipitation assessment, but merging the products increases accuracy. | ERA5 | Akbaş and Ozdemir (2024) [30] |
CHIRPS, SM2RAIN-ASCAT, PERSIANN | Pakistan | Multi-criteria assessment | CHIRPS and SM2RAIN-ASCAT faithfully track the spatio-temporal variability of rainfall observed in the subtropical semi-arid region. | CHIRPS and SM2RAIN-ASCAT | Anjum et al. (2022) [31] |
PERSIANN, ERA5, SM2RAIN-ASCAT | Morocco | Drought assessment | ERA5 performs well for drought analysis, while SM2RAIN-ASCAT shows good reliability for rainfall characterization. | ERA5 | Najmi et al. (2023) [32] |
Bassin | Boukdir | Isser | Zddine | Malah Est | Aissi |
---|---|---|---|---|---|
Area [km2] Annual rainfall [mm] Annual temperature [°C] | 76 | 3615 | 418 | 274 | 431 |
642.4 | 660 | 461 | 467.2 | 910 | |
17.6 | 34 | 22 | 16.4 | 20 | |
Perimeter [km] Code | 95.3 | 442 | 295 | 320.2 | 109.15 |
020331 | 090501 | 011905 | 090905 | 021715 | |
Station X [km] Y [km] PERIOD | MESDOUR | LAKHDARIA | BIR OULED TAHAR | BENI SLIMANE | RN30 |
461,000 | 579,100 | 432,000 | 557,000 | 628,000 | |
355,000 | 368,750 | 312,000 | 322,000 | 372,000 | |
1993–2014 | 1986–2018 | 1990–2015 | 1985–2015 | 1986–2015 |
Catchment | Code | Station | X [km] | Y [km] | PERIOD |
---|---|---|---|---|---|
Zddine | 011901 | EL TOUAIGIA | 430.85 | 313.35 | 1972–2018 |
011903 | TOUTIA ELHASSANIA | 429.95 | 294.45 | 1927–2018 | |
011904 | ROUINA MAIRE | 419.8 | 327.3 | 1972–2018 | |
Boukdir | 020303 | MENCEUR | 458.25 | 354.45 | 1972–2019 |
020304 | IAZABENE | 462.35 | 352.15 | 1972–2012 | |
Aissi | 021705 | LARBAA NTHIRATHEN | 634.9 | 370.8 | 1972–2012 |
021712 | BENI YENNI | 635 | 365.25 | 1972–2019 | |
021716 | AIT OUABANE | 643.3 | 354.7 | 1988–2017 | |
021717 | AIT DJEMAA | 621.45 | 356.85 | 1988–2018 | |
Malah Est | 090301 | DJOUAB | 566.95 | 315.55 | 1972–2019 |
090302 | BNI SLIMANE | 557.2 | 322.65 | 1972–2019 | |
090314 | DECHMYA | 578.5 | 316.35 | 1974–2019 | |
Isser | 090502 | LAKHDARIA GORGES | 579.3 | 370 | 1972–2018 |
Rainfall Product | Spatial Resolution | Spatial Coverage | Temporal Resolution | Time Period Availability | Data Sources |
---|---|---|---|---|---|
GPM | 0.1° | 60 S/60 N | 30 min | 2000–present | (https://gpm.nasa.gov, accessed on 5 February 2025) |
SM2RAIN | 0.125° | 60 S/60 N | Daily | 2007–2020 | (https://zenodo.org/record/6136294, accessed on 5 February 2025) |
PERSIANN-CCS-CDR | 0.04° | 60 S/60 N | Every 3 h | 1983–present | (https://www.ncei.noaa.gov/data/precipitation-persiann/access/, accessed on 5 February 2025) |
CHIRPS | 0.05° | 50 S/50 N | Daily | 1981–present | (https://chc.ucsb.edu/data/chirp, accessed on 5 February 2025) |
ERA5 | 0.33° | 60 S/60 N | 1 h | 1950–2022 | (https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5, accessed on 5 February 2025) |
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Bounab, R.; Boutaghane, H.; Boulmaiz, T.; Tramblay, Y. Comparison of Machine Learning Algorithms for Daily Runoff Forecasting with Global Rainfall Products in Algeria. Atmosphere 2025, 16, 213. https://doi.org/10.3390/atmos16020213
Bounab R, Boutaghane H, Boulmaiz T, Tramblay Y. Comparison of Machine Learning Algorithms for Daily Runoff Forecasting with Global Rainfall Products in Algeria. Atmosphere. 2025; 16(2):213. https://doi.org/10.3390/atmos16020213
Chicago/Turabian StyleBounab, Rayane, Hamouda Boutaghane, Tayeb Boulmaiz, and Yves Tramblay. 2025. "Comparison of Machine Learning Algorithms for Daily Runoff Forecasting with Global Rainfall Products in Algeria" Atmosphere 16, no. 2: 213. https://doi.org/10.3390/atmos16020213
APA StyleBounab, R., Boutaghane, H., Boulmaiz, T., & Tramblay, Y. (2025). Comparison of Machine Learning Algorithms for Daily Runoff Forecasting with Global Rainfall Products in Algeria. Atmosphere, 16(2), 213. https://doi.org/10.3390/atmos16020213