Deep learning based framework for flood prediction

Abstract

Floods are among the most frequent and devastating natural disasters, newlineresulting in significant loss of life, destruction of property, and disruption of newlinecommunities. The increasing unpredictability of weather patterns, exacerbated by newlineclimate change, has made accurate flood prediction a critical component of newlinedisaster management and water resource planning. Flood prediction refers to newlineconsidering various meteorological, hydrological, and atmospheric factors to newlinepredict the likelihood of a flood event and its potential severity. Flood prediction newlinehas come a long way, from relying on basic observations and local knowledge to newlineusing intricate computer models incorporating various data sources for precise newlinepredictions. newlineIn recent years, there has been a rise in the usage of Artificial newlineIntelligence (AI) and its subfields, such as Machine Learning (ML) and Deep newlineLearning (DL) approaches, to address the manifoldness of modeling issues related newlineto the environment. Deep learning, a branch of machine learning, leverages its newlineability to extract valuable patterns and insights from extensive datasets. Deep newlinelearning captures hidden associations between meteorological forcing and newlinehydrological responses, enabling more accurate predictions. Deep Learning newlinemodels have proven valuable tools in flood analysis and prediction in hydro newlinemeteorological studies, especially flood prediction. Deep learning models offer a newlinewide range of applications, covering diverse prediction units such as daily or newlineseasonal predictions and varying lead times from short-term to long-term newlinepredictions with their efficacy in accurately predicting floods. Therefore, data newlinedriven approaches based on artificial intelligence are investigated to predict newlinefloods. newline