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Muhammad, Wan Zuki Azman Wan
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Electrical & Electronics Engineering

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Journal : Journal of Engineering and Technological Sciences

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Prediction of Rainfall Trends using Mahalanobis-Taguchi System Jamil, Muhammad Arieffuddin Mohd; Abu, Mohd Yazid; Zaini, Sri Nur Areena Mohd; Aris, Nurul Haziyani; Pinueh, Nur Syafikah; Jaafar, Nur Najmiyah; Muhammad, Wan Zuki Azman Wan; Ramlie, Faizir; Harudin, Nolia; Sari, Emelia; Ghani, Nadiatul Adilah Ahmad Abdul
Journal of Engineering and Technological Sciences Vol. 56 No. 2 (2024)
Publisher : Directorate for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.eng.technol.sci.2024.56.2.9

Abstract

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.
Co-Authors Abu, Mohd Yazid Aris, Nurul Haziyani Emelia Sari Ghani, Nadiatul Adilah Ahmad Abdul Harudin, Nolia Jaafar, Nur Najmiyah Jamil, Muhammad Arieffuddin Mohd Pinueh, Nur Syafikah Ramlie, Faizir Zaini, Sri Nur Areena Mohd