Characterizing and forecasting UPLB rainfall through neural networks approach

Authors

Brylle B. Balad-on ⋅ PH Institute of Mathematical Sciences and Physics, University of the Philippines Los Baños
Hannah Rissah F. Abad ⋅ PH Department of Mathematics, Physics and Statistics, Visayas State University
Alvin Karlo G. Tapia ⋅ PH Institute of Mathematical Sciences and Physics, University of the Philippines Los Baños
Sharon P. Lubag ⋅ PH Institute of Mathematical Sciences and Physics, University of the Philippines Los Baños

Abstract

This paper presents an overview of the observed behavior of the UPLB rainfall from 1959-2008 through neural networks approach. A 50 year daily rainfall data, daily mean temperature, daily relative humidity and daily sunshine duration was obtained from the Agrometeorology and Farm Structure Division, College of Engineering and AgroIndustrial Technology. This study introduces the effect of forecasting UPLB rainfall by the conventions in most of the Neural Network studies based on three different time horizons; 1) the 366 days basis, 2) days in a month basis, and 3) monthly basis. A nonlinear function was approximated showing rainy season from the month of June to November and dry season throughout the rest of the year. The days in a month basis showed that Neural Network performed better in forecasting dry season than rainy season. The monthly basis forecast performed best with almost the same peaks as the actual values.

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Issue

2014: Proceedings of the 32nd Samahang Pisika ng Pilipinas Physics Congress

Article ID

SPP2014-PB-01

Section

Poster Session PB

Published

2014-10-17

How to Cite

[1]

BB Balad-on, HRF Abad, AKG Tapia, and SP Lubag, Characterizing and forecasting UPLB rainfall through neural networks approach, Proceedings of the Samahang Pisika ng Pilipinas 32, SPP2014-PB-01 (2014). URL: https://proceedings.spp-online.org/article/view/1873.