Comparative analysis of classification techniques and input-output patterns for monthly rainfall prediction
Rainfall prediction is one of the crucial stages of the watershed management process. In this research, A comparison of the performance among Monte Carlo and Thomas Fiering, linear regression (LR), multiple linear regression (MLR), and SVM optimized by Simulated Annealing (SVM-SA) is carried out for...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Water Science |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/23570008.2024.2323879 |
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| author | Nadia Sedghnejad Hamed Nozari Safar Marofi |
| author_facet | Nadia Sedghnejad Hamed Nozari Safar Marofi |
| author_sort | Nadia Sedghnejad |
| collection | DOAJ |
| description | Rainfall prediction is one of the crucial stages of the watershed management process. In this research, A comparison of the performance among Monte Carlo and Thomas Fiering, linear regression (LR), multiple linear regression (MLR), and SVM optimized by Simulated Annealing (SVM-SA) is carried out for Monthly rainfall prediction. In addition, the efficiency of the input patterns to the models including single input-multiple output (SIMO), multiple input-multiple output (MIMO), single input-single output (SISO), multiple input-single output (MISO) patterns are investigated. For this purpose, the time series of 34 rain gauge stations in the Karkheh basin was used. The results showed that SISO, MISO, MIMO, SIMO, and Monte Carlo and Thomas Fiering models are ranked first to fifth respectively. By comparing the performance of the models, it can be found that there is no significant difference between the SVM-SA, LR, and MLR models, However, the LR model is a method for predicting monthly rainfall more easily than other methods. This method has fewer adjustable parameters than other models. |
| format | Article |
| id | doaj-art-0d4199b54df44f618eb8d8501c7d55a0 |
| institution | Kabale University |
| issn | 2357-0008 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Water Science |
| spelling | doaj-art-0d4199b54df44f618eb8d8501c7d55a02024-12-13T08:34:54ZengTaylor & Francis GroupWater Science2357-00082024-12-0138119220810.1080/23570008.2024.2323879Comparative analysis of classification techniques and input-output patterns for monthly rainfall predictionNadia Sedghnejad0Hamed Nozari1Safar Marofi2Department of Water Science and Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, IranDepartment of Water Science and Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, IranDepartment of Water Science and Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, IranRainfall prediction is one of the crucial stages of the watershed management process. In this research, A comparison of the performance among Monte Carlo and Thomas Fiering, linear regression (LR), multiple linear regression (MLR), and SVM optimized by Simulated Annealing (SVM-SA) is carried out for Monthly rainfall prediction. In addition, the efficiency of the input patterns to the models including single input-multiple output (SIMO), multiple input-multiple output (MIMO), single input-single output (SISO), multiple input-single output (MISO) patterns are investigated. For this purpose, the time series of 34 rain gauge stations in the Karkheh basin was used. The results showed that SISO, MISO, MIMO, SIMO, and Monte Carlo and Thomas Fiering models are ranked first to fifth respectively. By comparing the performance of the models, it can be found that there is no significant difference between the SVM-SA, LR, and MLR models, However, the LR model is a method for predicting monthly rainfall more easily than other methods. This method has fewer adjustable parameters than other models.https://www.tandfonline.com/doi/10.1080/23570008.2024.2323879Artificial intelligencepredictionrainfallsupport vector machine |
| spellingShingle | Nadia Sedghnejad Hamed Nozari Safar Marofi Comparative analysis of classification techniques and input-output patterns for monthly rainfall prediction Water Science Artificial intelligence prediction rainfall support vector machine |
| title | Comparative analysis of classification techniques and input-output patterns for monthly rainfall prediction |
| title_full | Comparative analysis of classification techniques and input-output patterns for monthly rainfall prediction |
| title_fullStr | Comparative analysis of classification techniques and input-output patterns for monthly rainfall prediction |
| title_full_unstemmed | Comparative analysis of classification techniques and input-output patterns for monthly rainfall prediction |
| title_short | Comparative analysis of classification techniques and input-output patterns for monthly rainfall prediction |
| title_sort | comparative analysis of classification techniques and input output patterns for monthly rainfall prediction |
| topic | Artificial intelligence prediction rainfall support vector machine |
| url | https://www.tandfonline.com/doi/10.1080/23570008.2024.2323879 |
| work_keys_str_mv | AT nadiasedghnejad comparativeanalysisofclassificationtechniquesandinputoutputpatternsformonthlyrainfallprediction AT hamednozari comparativeanalysisofclassificationtechniquesandinputoutputpatternsformonthlyrainfallprediction AT safarmarofi comparativeanalysisofclassificationtechniquesandinputoutputpatternsformonthlyrainfallprediction |