Estimating Yearly Accumulated Rainfall in Batangas City: An Analysis Through Modeling and Simulation - Siman, Bea Jeanette L.; Maligaya, Krisha Mari Faye C.; Convento, Irrish Shane E.; Antonio, Dia Valora C.; De Chavez, John Kervin I.; Cay, Rae Christian Y.; Caguicla, John Jerick F.; Gubot, Ephraim; Dolor, Joss Marron I.; Lara, James Darrel M.; Magon, Selverino A. (2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS), 2024)
This research investigates heavy rainfall dynamics in Batangas City, employing the trapezoidal rule and quadratic regression for cumulative rainfall estimation and curve fitting. Utilizing multi-year datasets sourced from the Weather and Climate website, the study not only addresses immediate impacts but also discerns long-term trends, contributing vital insights into potential indicators of climate change. The Trapezoidal Rule application reveals integral approximations, showcasing variations and trends in rainfall datasets from 2018 to 2020. Visual representations and quadratic regression aid in curve fitting, enhancing the study's precision. The development of a user-friendly Python program with a graphical interface ensures accessibility, facilitating further exploration by researchers and stakeholders. In conclusion, this research not only answers the immediate research question but significantly contributes to climate science, fostering resilience and adaptive strategies for communities facing the challenges posed by heavy rainfall events.