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All Journal Jurnal Informatika dan Teknik Elektro Terapan Jurnal Ilmu Kedokteran dan Kesehatan JURNAL FARMASIMED (JFM) Jurnal Matematika UNAND JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Pendidikan Jasmani dan Adaptif (JPJA) Marketing Management Studies TECHSI - Jurnal Teknik Informatika Al-DYAS : Jurnal Inovasi dan Pengabdian kepada Masyarakat Jurnal Pengabdian kepada Masyarakat Current Biomedicine Indonesian Council of Premier Statistical Science Jurnal Novem Medika Farmasi Sociality: Journal of Public Health Service Jurnal Pengabdian Kepada Masyarakat Bersinergi Inovatif Vokasi UNESA Bulletin of Engineering, Technology and Applied Science ETNOPEDAGOGI: Jurnal Pendidikan dan Kebudayaan Gifted: Journal of Early Childhood Education
Jannah, Zahratul
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Religion Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Transportation Veterinary Other

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Modeling of Multiple Statistical Distributions for Extreme Rainfall Data Using Maximum Likelihood Estimation Methods and Bayesian Methods Marizal, Muhammad; Jannah, Zahratul
Vokasi Unesa Bulletin of Engineering, Technology and Applied Science Vol. 3 No. 1 (2026): (In Progress)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v3i1.44270

Abstract

The city of Pekanbaru has rapidly developed into a metropolitan hub, facing challenges such as floods and haze caused by extreme rainfall events. This study proposes a novel combination of Generalized Extreme Value (GEV), Generalized Logistic (GLO), and Generalized Pareto (GP) distributions, utilizing Bayesian Markov Chain Monte Carlo (MCMC) and Maximum Likelihood Estimation (MLE) methods, to model annual extreme rainfall data for the period 2010–2024. Rainfall data were sourced from NASA/POWER. Model performance was evaluated using Relative Root Mean Square Error (RRMSE), Relative Absolute Square Error (RASE), and Probability Plot Correlation Coefficient (PPCC). The Bayesian method yielded superior performance with RRMSE = 0.3166, RASE = 0.2682, and PPCC = 0.00485 for the GEV distribution, outperforming MLE. The novelty lies in applying this methodological combination to Pekanbaru's rainfall dataset for the first time, providing valuable insights for flood mitigation, drainage planning, and urban water resource management.
Co-Authors Ade Novia Rahma Akhmad Faizin Andi Miftahul Jannah, Andi Miftahul Arfah, Nur Hasanah Arief Maulana Budayawan, Khairi Dani, Agus Darmayanti, Firdha E Haodudin Nurkifli Egianta, Anggun Nidia Eka Kurnia, Eka Emmi Bujawati Erianza, Dava Fitria, Nia Hedrin, Diki Herwin Herwin Pisestyani Hikmah, Intan Daratul Hilmi Iman Firmansyah Ida Ayu Putu Sri Widnyani Isdoni Bustaman Jihan Safitri Komala, Iyep M Marizal Maulida, Dafa Riska Muthmainnah Muthmainnah Nazila, Nazila Ningsih, Andi Asma Nisyra, Mailani Nur Harizma, Maisya Nurul Hanifah Prawibowo, Muhammad Rahmawati, Rahmawati Rudi Kurniawan Rusman Rusman Saeful Anwar, Saeful Sangka, Agus Sani K, Fathnur Saputra, Aidil Sari, Nurlia Susilo Yuda Irawan, Agung Suwandhy , Nicky Syahrizal Syahrizal Tiksah, Yusniar Umari, Taufik Firdaus Ummul Huda Yuliawati Yusnita