Meteorological and satellite-based data for drought prediction using data-driven model
This work presents a data-driven model, the Artificial Neural Network-Multilayer Perceptron Neural Network (ANN-MLP), for use in meteorological drought deciles index (DDI) predictions over various climatic sub-zone. Two types of rainfall data from meteorological weather stations (WSs) and satellite...
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| Format: | Article |
| Language: | English |
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Association of agrometeorologists
2024-12-01
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| Series: | Journal of Agrometeorology |
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| Online Access: | https://journal.agrimetassociation.org/index.php/jam/article/view/2734 |
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| author | ALI H. AHMED SULIMAN |
| author_facet | ALI H. AHMED SULIMAN |
| author_sort | ALI H. AHMED SULIMAN |
| collection | DOAJ |
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This work presents a data-driven model, the Artificial Neural Network-Multilayer Perceptron Neural Network (ANN-MLP), for use in meteorological drought deciles index (DDI) predictions over various climatic sub-zone. Two types of rainfall data from meteorological weather stations (WSs) and satellite-based estimates of PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network) were adopted. This work considered the calculated DDI (DDI original) from WSs to train and develop the proposed algorithm at three sub-zones (ANN-MLP-DDI models). The newly developed model was tested for DDI prediction using PERSIANN, and compared with the calculated DDI original from WSs. The results positively revealed that the ANN-MLP-DDI models showed high performance (Correlation coefficient r= 0.981) for DDI prediction against the DDI original from WSs. It can be concluded that data-driven models are feasible for drought prediction, and this work could help water managers in mitigating drought impacts and in providing information for policy makers
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| format | Article |
| id | doaj-art-48327772711e4c6abaef9094e5625a79 |
| institution | Kabale University |
| issn | 0972-1665 2583-2980 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Association of agrometeorologists |
| record_format | Article |
| series | Journal of Agrometeorology |
| spelling | doaj-art-48327772711e4c6abaef9094e5625a792024-12-17T15:33:47ZengAssociation of agrometeorologistsJournal of Agrometeorology0972-16652583-29802024-12-0126410.54386/jam.v26i4.2734Meteorological and satellite-based data for drought prediction using data-driven modelALI H. AHMED SULIMAN 0Department of Physics, College of Education for Pure Sciences, University of Al-Hamdaniya, Nineveh Plain, Nineveh, Iraq This work presents a data-driven model, the Artificial Neural Network-Multilayer Perceptron Neural Network (ANN-MLP), for use in meteorological drought deciles index (DDI) predictions over various climatic sub-zone. Two types of rainfall data from meteorological weather stations (WSs) and satellite-based estimates of PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network) were adopted. This work considered the calculated DDI (DDI original) from WSs to train and develop the proposed algorithm at three sub-zones (ANN-MLP-DDI models). The newly developed model was tested for DDI prediction using PERSIANN, and compared with the calculated DDI original from WSs. The results positively revealed that the ANN-MLP-DDI models showed high performance (Correlation coefficient r= 0.981) for DDI prediction against the DDI original from WSs. It can be concluded that data-driven models are feasible for drought prediction, and this work could help water managers in mitigating drought impacts and in providing information for policy makers https://journal.agrimetassociation.org/index.php/jam/article/view/2734Drought Deciles indexMeteorological DroughtMultilayer PerceptronHydrology |
| spellingShingle | ALI H. AHMED SULIMAN Meteorological and satellite-based data for drought prediction using data-driven model Journal of Agrometeorology Drought Deciles index Meteorological Drought Multilayer Perceptron Hydrology |
| title | Meteorological and satellite-based data for drought prediction using data-driven model |
| title_full | Meteorological and satellite-based data for drought prediction using data-driven model |
| title_fullStr | Meteorological and satellite-based data for drought prediction using data-driven model |
| title_full_unstemmed | Meteorological and satellite-based data for drought prediction using data-driven model |
| title_short | Meteorological and satellite-based data for drought prediction using data-driven model |
| title_sort | meteorological and satellite based data for drought prediction using data driven model |
| topic | Drought Deciles index Meteorological Drought Multilayer Perceptron Hydrology |
| url | https://journal.agrimetassociation.org/index.php/jam/article/view/2734 |
| work_keys_str_mv | AT alihahmedsuliman meteorologicalandsatellitebaseddatafordroughtpredictionusingdatadrivenmodel |