Prediction of Rainfall Trends using Mahalanobis-Taguchi System
Full comprehension of precipitation patterns is crucially needed, especially in Pekan, a district in Pahang, Malaysia. The area is renowned for its elevated levels of precipitation, making it imperative to precisely categorize and enhance the analysis of rainfall patterns to facilitate effective res...
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Institute for Research and Community Services, Institut Teknologi Bandung
2025
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my.uniten.dspace-365832025-03-03T15:43:13Z Prediction of Rainfall Trends using Mahalanobis-Taguchi System Jamil M.A.M. Abu M.Y. Zaini S.N.A.M. Aris N.H. Pinueh N.S. Jaafar N.N. Muhammad W.Z.A.W. Ramlie F. Harudin N. Sari E. Ghani N.A.A.A. 59182931100 55983627200 57196441481 58832351700 58830536300 56083925700 55860800560 55982859700 56319654100 55983050300 57214749865 Errors Weather information services Data set Elevated level Mahalanobis distances Mahalanobis-taguchi systems Malaysia Optimisations Precipitation patterns Rainfall patterns Rainfall trends Weather stations Rain Full comprehension of precipitation patterns is crucially needed, especially in Pekan, a district in Pahang, Malaysia. The area is renowned for its elevated levels of precipitation, making it imperative to precisely categorize and enhance the analysis of rainfall patterns to facilitate effective resource allocation, agricultural productivity, and catastrophe readiness. The variability of rainfall patterns is contingent upon geographical location, necessitating the collection of a comprehensive data set that includes several characteristics that influence precipitation to make reliable predictions. Data were collected from the Vantage Pro2 weather station, which is located on the UMP Pekan campus. This study used the RT method to classify rainfall and T-Method 1 to determine the degree of contribution of each parameter. Significant parameters were validated using a data set from the same type of weather station but in a different district. The results showed that the Mahalanobis-Taguchi Bee Algorithm (MTBA) is more effective than the Mahalanobis-Taguchi System (MTS) in finding the significant parameters, but the parameters were a subset of MTS Teshima. Finally, the validation with T mean-based error (Tmbe) using Mean Absolute Error (MAE) revealed a pattern of errors to provide insight to find the significant parameters of MTS. ? 2024 Published by IRCS-ITB. Final 2025-03-03T07:43:13Z 2025-03-03T07:43:13Z 2024 Article 10.5614/j.eng.technol.sci.2024.56.2.9 2-s2.0-85196657123 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196657123&doi=10.5614%2fj.eng.technol.sci.2024.56.2.9&partnerID=40&md5=bb7a819259e7d9d35bd2d342eacb440a https://irepository.uniten.edu.my/handle/123456789/36583 56 2 287 303 All Open Access; Gold Open Access Institute for Research and Community Services, Institut Teknologi Bandung Scopus |
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Errors Weather information services Data set Elevated level Mahalanobis distances Mahalanobis-taguchi systems Malaysia Optimisations Precipitation patterns Rainfall patterns Rainfall trends Weather stations Rain |
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Errors Weather information services Data set Elevated level Mahalanobis distances Mahalanobis-taguchi systems Malaysia Optimisations Precipitation patterns Rainfall patterns Rainfall trends Weather stations Rain Jamil M.A.M. Abu M.Y. Zaini S.N.A.M. Aris N.H. Pinueh N.S. Jaafar N.N. Muhammad W.Z.A.W. Ramlie F. Harudin N. Sari E. Ghani N.A.A.A. Prediction of Rainfall Trends using Mahalanobis-Taguchi System |
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Full comprehension of precipitation patterns is crucially needed, especially in Pekan, a district in Pahang, Malaysia. The area is renowned for its elevated levels of precipitation, making it imperative to precisely categorize and enhance the analysis of rainfall patterns to facilitate effective resource allocation, agricultural productivity, and catastrophe readiness. The variability of rainfall patterns is contingent upon geographical location, necessitating the collection of a comprehensive data set that includes several characteristics that influence precipitation to make reliable predictions. Data were collected from the Vantage Pro2 weather station, which is located on the UMP Pekan campus. This study used the RT method to classify rainfall and T-Method 1 to determine the degree of contribution of each parameter. Significant parameters were validated using a data set from the same type of weather station but in a different district. The results showed that the Mahalanobis-Taguchi Bee Algorithm (MTBA) is more effective than the Mahalanobis-Taguchi System (MTS) in finding the significant parameters, but the parameters were a subset of MTS Teshima. Finally, the validation with T mean-based error (Tmbe) using Mean Absolute Error (MAE) revealed a pattern of errors to provide insight to find the significant parameters of MTS. ? 2024 Published by IRCS-ITB. |
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59182931100 |
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59182931100 Jamil M.A.M. Abu M.Y. Zaini S.N.A.M. Aris N.H. Pinueh N.S. Jaafar N.N. Muhammad W.Z.A.W. Ramlie F. Harudin N. Sari E. Ghani N.A.A.A. |
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Article |
| author |
Jamil M.A.M. Abu M.Y. Zaini S.N.A.M. Aris N.H. Pinueh N.S. Jaafar N.N. Muhammad W.Z.A.W. Ramlie F. Harudin N. Sari E. Ghani N.A.A.A. |
| author_sort |
Jamil M.A.M. |
| title |
Prediction of Rainfall Trends using Mahalanobis-Taguchi System |
| title_short |
Prediction of Rainfall Trends using Mahalanobis-Taguchi System |
| title_full |
Prediction of Rainfall Trends using Mahalanobis-Taguchi System |
| title_fullStr |
Prediction of Rainfall Trends using Mahalanobis-Taguchi System |
| title_full_unstemmed |
Prediction of Rainfall Trends using Mahalanobis-Taguchi System |
| title_sort |
prediction of rainfall trends using mahalanobis-taguchi system |
| publisher |
Institute for Research and Community Services, Institut Teknologi Bandung |
| publishDate |
2025 |
| _version_ |
1825816111186182144 |
| score |
13.244109 |