Quantitative Estimation of Rainfall from Remote Sensing Data Using Machine Learning Regression Models
To estimate rainfall from remote sensing data, three machine learning-based regression models, K-Nearest Neighbors Regression (K-NNR), Support Vector Regression (SVR), and Random Forest Regression (RFR), were implemented using MSG (Meteosat Second Generation) satellite data. Daytime and nighttime da...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Hydrology |
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| Online Access: | https://www.mdpi.com/2306-5338/10/2/52 |
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| author | Yacine Mohia Rafik Absi Mourad Lazri Karim Labadi Fethi Ouallouche Soltane Ameur |
| author_facet | Yacine Mohia Rafik Absi Mourad Lazri Karim Labadi Fethi Ouallouche Soltane Ameur |
| author_sort | Yacine Mohia |
| collection | DOAJ |
| description | To estimate rainfall from remote sensing data, three machine learning-based regression models, K-Nearest Neighbors Regression (K-NNR), Support Vector Regression (SVR), and Random Forest Regression (RFR), were implemented using MSG (Meteosat Second Generation) satellite data. Daytime and nighttime data from a rain gauge are used for model training and validation. To optimize the results, the outputs of the three models are combined using the weighted average. The combination of the three models (hereafter called Com-RSK) markedly improved the predictions. Indeed, the MAE, MBE, RMSE and correlation coefficient went from 23.6 mm, 10.0 mm, 40.6 mm and 89% for the SVR to 20.7 mm, 5.5 mm, 37.4 mm, and 94% when the models were combined, respectively. The Com-RSK is also compared to a few methods using the classification in the estimation, such as the ECST Enhanced Convective Stratiform Technique (ECST), the MMultic technique, and the Convective/Stratiform Rain Area Delineation Technique (CS-RADT). The Com-RSK show superior performance compared to ECST, MMultic and CS-RADT methods.The Com-RSK is also compared to the two products of satellite estimates, namely CMORPH and CHIRPS. The results indicate that Com-RSK performs better than CMORPH and CHIRPS according to MBE, RMSE and CC (coefficient correlation). A comparison with three types of satellite precipitation estimation products, such as global product, regional product, and near real-time product, is performed. Overall, the methodology developed here shows almost the same results as regional product methods and exhibits better results than near real-time and global product methods. |
| first_indexed | 2024-03-11T08:45:17Z |
| format | Article |
| id | doaj.art-bf2caf3f7c914665ad6195c6219630cf |
| institution | Directory Open Access Journal |
| issn | 2306-5338 |
| language | English |
| last_indexed | 2024-03-11T08:45:17Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Hydrology |
| spelling | doaj.art-bf2caf3f7c914665ad6195c6219630cf2023-11-16T20:51:51ZengMDPI AGHydrology2306-53382023-02-011025210.3390/hydrology10020052Quantitative Estimation of Rainfall from Remote Sensing Data Using Machine Learning Regression ModelsYacine Mohia0Rafik Absi1Mourad Lazri2Karim Labadi3Fethi Ouallouche4Soltane Ameur5Loboratoire d’Analyse et de Modélisation des Phénomènes Aléatoires, LAMPA, Faculté du Génie Electrique et d’Informatique (FGEI), University Mouloud MAMMERI of Tizi Ouzou (UMMTO), Tizi Ouzou 15000, AlgeriaECAM-EPMI, LR2E-Lab, Laboratoire Quartz, 95092 Cergy Pontoise, FranceLoboratoire d’Analyse et de Modélisation des Phénomènes Aléatoires, LAMPA, Faculté du Génie Electrique et d’Informatique (FGEI), University Mouloud MAMMERI of Tizi Ouzou (UMMTO), Tizi Ouzou 15000, AlgeriaECAM-EPMI, LR2E-Lab, Laboratoire Quartz, 95092 Cergy Pontoise, FranceLoboratoire d’Analyse et de Modélisation des Phénomènes Aléatoires, LAMPA, Faculté du Génie Electrique et d’Informatique (FGEI), University Mouloud MAMMERI of Tizi Ouzou (UMMTO), Tizi Ouzou 15000, AlgeriaLoboratoire d’Analyse et de Modélisation des Phénomènes Aléatoires, LAMPA, Faculté du Génie Electrique et d’Informatique (FGEI), University Mouloud MAMMERI of Tizi Ouzou (UMMTO), Tizi Ouzou 15000, AlgeriaTo estimate rainfall from remote sensing data, three machine learning-based regression models, K-Nearest Neighbors Regression (K-NNR), Support Vector Regression (SVR), and Random Forest Regression (RFR), were implemented using MSG (Meteosat Second Generation) satellite data. Daytime and nighttime data from a rain gauge are used for model training and validation. To optimize the results, the outputs of the three models are combined using the weighted average. The combination of the three models (hereafter called Com-RSK) markedly improved the predictions. Indeed, the MAE, MBE, RMSE and correlation coefficient went from 23.6 mm, 10.0 mm, 40.6 mm and 89% for the SVR to 20.7 mm, 5.5 mm, 37.4 mm, and 94% when the models were combined, respectively. The Com-RSK is also compared to a few methods using the classification in the estimation, such as the ECST Enhanced Convective Stratiform Technique (ECST), the MMultic technique, and the Convective/Stratiform Rain Area Delineation Technique (CS-RADT). The Com-RSK show superior performance compared to ECST, MMultic and CS-RADT methods.The Com-RSK is also compared to the two products of satellite estimates, namely CMORPH and CHIRPS. The results indicate that Com-RSK performs better than CMORPH and CHIRPS according to MBE, RMSE and CC (coefficient correlation). A comparison with three types of satellite precipitation estimation products, such as global product, regional product, and near real-time product, is performed. Overall, the methodology developed here shows almost the same results as regional product methods and exhibits better results than near real-time and global product methods.https://www.mdpi.com/2306-5338/10/2/52remote sensingrainfallMSG SatelliteSVRRFRR |
| spellingShingle | Yacine Mohia Rafik Absi Mourad Lazri Karim Labadi Fethi Ouallouche Soltane Ameur Quantitative Estimation of Rainfall from Remote Sensing Data Using Machine Learning Regression Models Hydrology remote sensing rainfall MSG Satellite SVR RF RR |
| title | Quantitative Estimation of Rainfall from Remote Sensing Data Using Machine Learning Regression Models |
| title_full | Quantitative Estimation of Rainfall from Remote Sensing Data Using Machine Learning Regression Models |
| title_fullStr | Quantitative Estimation of Rainfall from Remote Sensing Data Using Machine Learning Regression Models |
| title_full_unstemmed | Quantitative Estimation of Rainfall from Remote Sensing Data Using Machine Learning Regression Models |
| title_short | Quantitative Estimation of Rainfall from Remote Sensing Data Using Machine Learning Regression Models |
| title_sort | quantitative estimation of rainfall from remote sensing data using machine learning regression models |
| topic | remote sensing rainfall MSG Satellite SVR RF RR |
| url | https://www.mdpi.com/2306-5338/10/2/52 |
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