rain-t: Daily Rainfall Predictive Model Using 6-Gene Genetic Expression for Historical Data-Based Forecasting

Extreme weather conditions such as heavy rainfalls have been wreaking havoc not only in urban areas but also in an entire watershed. The development of a flood management plan and flood mitigating structures to alleviate the impacts of flooding is very crucial be-cause it needs intensive and continuous historical data. However, missing data due to equipment failure that gathers the rainfall data could be a problem. Rain-fall data is not only useful in designing flood mitigating structures but also in planning our day-to-day activi-ties ahead of time. To address this problem, this pa-per proposes a predictive model which able to forecast in a short lead-time and predict missing data within the dataset. In this paper, three predictive models will be compared namely recurrent neural network, Gaus-sian processing regression, and the proposed 6-gene ge-netic expression-based predictive modeling (MGGP). 29-year 24-hour cumulative rainfall data which were sourced in PAGASA Tacloban city weather station, Philippines, was used. The data were cleaned by re-moving negative values. Two datasets were created, the first (RFDS1) dataset which makes use of three in-dices (year, month, and days), and the second (RFDS2) dataset which was orchestrated and transformed to in-crease correlation and reduce prediction errors which had an additional two datasets (ave (������ -1, ������ - 2), ������ - 1). Each method used three and five time-based indices. The result shows an erratic behavior of the model from three methods that used the RFDS1, while RFDS2 had a more stable predictive model. This shows that the data orchestration and transformation greatly im-proved the correlation and reduced errors. However, MGGP showed the best results among the three meth-ods.