FEEMD and GWO methodology for flood early warning prediction model

Abstract

Flood disasters are natural hazards that cause many great losses either in terms of lives, property, and even the structure of the earth's surface. Investigations on this topic have become one of the ongoing studies because it has a great impact on the environment and community life. This study also highlights the improvement of flood warning measurement methods to ensure that the adverse effects of flood disasters can be controlled better than before. This paper will present a more efficient method to model flood early warning prediction in Malaysian districts particularly. This study focuses on the use of Fast Ensemble Empirical Mode Decomposition (FEEMD) to decompose selected rainfall dataset. Furthermore, the Gray Wolf Optimizer (GWO) was used as an optimization approach to optimize between FEEMD hybrids with Artificial Intelligence models to find the most accurate flood early warning prediction model. The study also aims to improve the process flow of the flood early warning prediction system delivered to flood victims by ensuring that they are able to prepare for the consequences of impending flood disasters in their areas of residence.