Evaluating Variations in Tropical Cyclone Precipitation in Eastern Mexico Using Machine Learning Techniques

Keywords: Tropical Cyclone, Precipitation, Machine Learning, Mexico
Session Type: Virtual Paper Abstract
Day: Sunday
Session Start / End Time: 2/27/2022 09:40 AM (Eastern Time (US & Canada)) - 2/27/2022 11:00 AM (Eastern Time (US & Canada))
Room: Virtual 48

Abstract

Tropical Cyclone Precipitation (TCP) is one of the major triggers of flash flooding and landslide in eastern Mexico. We apply different statistical and machine learning techniques to study a 99 years TCP climatology in high resolution. Strong correlations exist between location variables and annual mean TCP, as well as between dynamic variables and event TCP. The Random Forest (RF) model is a powerful tool with excellent fitting and predicting skills for TCP variations. It has a very small out-of-sample cross-validation error and well captures the spatial variations of historical TCP events. The RF algorithm is an efficient machine learning approach showing potentials for future Quantitative Precipitation Forecasting (QPF).