A Review of Flood Forecasting with the Motivation of Avoiding Economic Loss

Abstract

Flooding is more than a momentary influx of water onto ordinarily dry terrain. Floods are the most frequent natural calamities in certain states, like Assam, Kerala, Tamil Nādu, and Bangladesh. As per the IPCC-2022 third report, mostly 44 percent of environmental disasters globally are reported yearly, flash floods represent 22 percent of all economic damages globally, and the severity of death rate is about 10 percent worldwide. Failing to evacuate flooded regions or coming into flood waters can result in harm or death. By utilizing the different correlated data, such as the availability of water from all resources, including canals, rivers, glaciers, precipitation in that area, and past rainfall data the prediction will be accurate. This survey is related to flood forecasting (FF) based on precipitation and water obtainability index (WOI) using machine learning (ML) and deep learning. Deep Learning (DL) has become an evolutionary and adaptable technique that revolutionizes business applications and produces new and improved model creation and scientific discovery capabilities. although dl adoption in hydrology has so far been sluggish, the time is now right for innovations.