Flood Forecasting Using Machine Learning: A Review
Parag Ghorpade, Aditya Gadge, Akash Lende, Hitesh Chordiya, G. R. Gosavi, Asima Mishra, Basavaraj Hooli, Yashwant S. Ingle, Nuzhat F. Shaikh
Abstract
Floods are the most frequently occurring natural disasters and result in loss of human life, destruction of livelihoods, which in turn, affects the national economies. There are several studies and novel modi operandi to design flood forecasting systems efficaciously. The authors witness and address the recent shift towards data-driven methods for flood prediction. The machine learning-based models trained using climatic parameters' historical data are increasingly useful for forecasting tasks. This paper's main objective is to demonstrate the recent advancements in the flood forecasting field using machine learning algorithms. The authors reviewed some prominent algorithms used for flood forecasting, which various professionals can use to develop their solutions.