JACIII Vol.28 No.1 pp. 5-11
doi: 10.20965/jaciii.2024.p0005

Research Paper:

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

Marvin Jade Genoguin*1,*2,† ORCID Icon, Ronnie S. Concepcion II*1,*3 ORCID Icon, Andres Philip Mayol*1,*3 ORCID Icon, Aristotle Ubando*1,*4 ORCID Icon, Alvin Culaba*1,*4 ORCID Icon, and Elmer P. Dadios*1,*3 ORCID Icon

*1Center for Engineering and Sustainability Development Research, De La Salle University
2401 Taft Avenue, Malate, Manila 1004, Philippines

*2Department of Civil Engineering, Eastern Visayas State University
Lino Gonzaga Avenue, Tacloban City 6500, Philippines

*3Department of Manufacturing Engineering and Management, De La Salle University
2401 Taft Avenue, Malate, Manila 1004, Philippines

*4Department of Mechanical Engineering, De La Salle University
2401 Taft Avenue, Malate, Manila 1004, Philippines

Corresponding author

April 22, 2022
July 10, 2022
January 20, 2024
computational intelligence, data orchestration and transformation, multigene genetic programming, rainfall 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 because it needs intensive and continuous historical data. However, missing data due to equipment failure that gathers the rainfall data could be a problem. Rainfall data is not only useful in designing flood mitigating structures but also in planning our day-to-day activities ahead of time. To address this problem, this paper 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, Gaussian processing regression, and the proposed 6-gene genetic 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 removing negative values. Two datasets were created, the first (RFDS1) dataset which makes use of three indices (year, month, and days), and the second (RFDS2) dataset which was orchestrated and transformed to increase correlation and reduce prediction errors which had an additional two datasets (ave(t-1,t-2),t-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 improved the correlation and reduced errors. However, MGGP showed the best results among the three methods.

rain-t model development architecture

rain-t model development architecture

Cite this article as:
M. Genoguin, R. Concepcion II, A. Mayol, A. Ubando, A. Culaba, and E. Dadios, “rain-t: Daily Rainfall Predictive Model Using 6-Gene Genetic Expression for Historical Data-Based Forecasting,” J. Adv. Comput. Intell. Intell. Inform., Vol.28 No.1, pp. 5-11, 2024.
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Last updated on Jul. 12, 2024