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Muhammad Arieffuddin Mohd Jamil
Faculty of Manufacturing and Mechatronics Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600 Pekan, Pahang, Malaysia
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Electrical & Electronics Engineering

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