Data Mining of 24-Hour Cumulative Precipitation Data in Iran Using Machine Learning: Multilayer Perceptron Neural Network and Decision Tree
Research Topic: This study examines 24-hour cumulative precipitation data in Iran and predicts rainfall amounts over various time periods using data mining and machine learning. Objective: To develop an accurate model for predicting 24-hour cumulative precipitation in regions of Iran using multilaye...
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University of Terhan press
2025-06-01
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| Series: | اکوهیدرولوژی |
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| Online Access: | https://ije.ut.ac.ir/article_102896_64d49f114bf5f3cb8b1a02f03686bef2.pdf |
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| author | mozaffar faraji Majid Rezaii Banafsheh Daragh Behroz sarisarraf Ali Mohammad Khorshid Dust |
| author_facet | mozaffar faraji Majid Rezaii Banafsheh Daragh Behroz sarisarraf Ali Mohammad Khorshid Dust |
| author_sort | mozaffar faraji |
| collection | DOAJ |
| description | Research Topic: This study examines 24-hour cumulative precipitation data in Iran and predicts rainfall amounts over various time periods using data mining and machine learning.
Objective: To develop an accurate model for predicting 24-hour cumulative precipitation in regions of Iran using multilayer neural networks and decision trees to improve hydrological planning and water resource management.
Method: A daily precipitation dataset D was collected from Iranian stations and prepared using normalization. Two machine learning models including MLP with activation function σ and decision tree with entropy criterion were implemented. The models’ performance was evaluated and compared with accuracy, precision, and error criteria.
Results: The MLP model demonstrated efficiency in estimating monthly precipitation by minimizing MSE to 0.04. The decision tree analysis classified Iran provinces into seven clusters based on precipitation characteristics; clusters 4 and 7 represent provinces with minimum (including Isfahan, Sistan and Baluchestan, Yazd) and maximum precipitation (including Gilan, Kohgiluyeh and Boyer-Ahmad, Mazandaran), respectively. Linear regression showed a significant effect of the time variable with 0.209 on precipitation variance.
Conclusions: The use of machine learning, especially neural networks, is effective in analyzing hydrological data in Iran and can help improve precipitation forecasting systems. |
| format | Article |
| id | doaj-art-2b94eb7c05da45379c88992fc950ad94 |
| institution | Kabale University |
| issn | 2423-6101 |
| language | fas |
| publishDate | 2025-06-01 |
| publisher | University of Terhan press |
| record_format | Article |
| series | اکوهیدرولوژی |
| spelling | doaj-art-2b94eb7c05da45379c88992fc950ad942025-08-20T03:47:02ZfasUniversity of Terhan pressاکوهیدرولوژی2423-61012025-06-0112279581110.22059/ije.2025.395276.1870102896Data Mining of 24-Hour Cumulative Precipitation Data in Iran Using Machine Learning: Multilayer Perceptron Neural Network and Decision Treemozaffar faraji0Majid Rezaii Banafsheh Daragh1Behroz sarisarraf2Ali Mohammad Khorshid Dust3PhD Student, Department of Climatology, University of Tabriz, Tabriz, IranProfessor, Department of Climatology, University of Tabriz, Tabriz, IranProfessor, Department of Climatology, University of Tabriz, Tabriz, IranProfessor, Department of Climatology, University of Tabriz, Tabriz, IranResearch Topic: This study examines 24-hour cumulative precipitation data in Iran and predicts rainfall amounts over various time periods using data mining and machine learning. Objective: To develop an accurate model for predicting 24-hour cumulative precipitation in regions of Iran using multilayer neural networks and decision trees to improve hydrological planning and water resource management. Method: A daily precipitation dataset D was collected from Iranian stations and prepared using normalization. Two machine learning models including MLP with activation function σ and decision tree with entropy criterion were implemented. The models’ performance was evaluated and compared with accuracy, precision, and error criteria. Results: The MLP model demonstrated efficiency in estimating monthly precipitation by minimizing MSE to 0.04. The decision tree analysis classified Iran provinces into seven clusters based on precipitation characteristics; clusters 4 and 7 represent provinces with minimum (including Isfahan, Sistan and Baluchestan, Yazd) and maximum precipitation (including Gilan, Kohgiluyeh and Boyer-Ahmad, Mazandaran), respectively. Linear regression showed a significant effect of the time variable with 0.209 on precipitation variance. Conclusions: The use of machine learning, especially neural networks, is effective in analyzing hydrological data in Iran and can help improve precipitation forecasting systems.https://ije.ut.ac.ir/article_102896_64d49f114bf5f3cb8b1a02f03686bef2.pdfrainfallmultilayerperceptrondecisiontree |
| spellingShingle | mozaffar faraji Majid Rezaii Banafsheh Daragh Behroz sarisarraf Ali Mohammad Khorshid Dust Data Mining of 24-Hour Cumulative Precipitation Data in Iran Using Machine Learning: Multilayer Perceptron Neural Network and Decision Tree اکوهیدرولوژی rainfall multilayer perceptron decision tree |
| title | Data Mining of 24-Hour Cumulative Precipitation Data in Iran Using Machine Learning: Multilayer Perceptron Neural Network and Decision Tree |
| title_full | Data Mining of 24-Hour Cumulative Precipitation Data in Iran Using Machine Learning: Multilayer Perceptron Neural Network and Decision Tree |
| title_fullStr | Data Mining of 24-Hour Cumulative Precipitation Data in Iran Using Machine Learning: Multilayer Perceptron Neural Network and Decision Tree |
| title_full_unstemmed | Data Mining of 24-Hour Cumulative Precipitation Data in Iran Using Machine Learning: Multilayer Perceptron Neural Network and Decision Tree |
| title_short | Data Mining of 24-Hour Cumulative Precipitation Data in Iran Using Machine Learning: Multilayer Perceptron Neural Network and Decision Tree |
| title_sort | data mining of 24 hour cumulative precipitation data in iran using machine learning multilayer perceptron neural network and decision tree |
| topic | rainfall multilayer perceptron decision tree |
| url | https://ije.ut.ac.ir/article_102896_64d49f114bf5f3cb8b1a02f03686bef2.pdf |
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