Evolution of Flood Forecasting: A Comprehensive Literature Review of Traditional and Sophisticated Approaches

Abstract

Flood forecasting is considered critical in the world, where communities , infrastructure and the environment are placed at significant risk by floods. In this study, a comprehensive analysis is provided of traditional and sophisticated flood forecasting methods with an emphasis on their strength, limitation and an applicability in different scenarios. Traditional methods including empirical rainfall runoff relationships  and historical flood data analysis, have been relied upon as foundational approaches to predicting flood events based on historical patterns and local knowledge. However these methods are often lacking in precision and responsiveness to real time changes in climate and land use. In contrast, an accuracy and lead time of flood forecast have been improved through the leveraging of advanced computational models, remote sensing and machine learning, deep learning algorithms in modern techniques. Technologies such as hydrodynamic modelling ,satellite based monitoring ,machine learning, deep learning and hybrid models have been demonstrated to offer high predictive capabilities by integrating real time data and spatial analysis. Case studies from recent flood events are analyzed in this study , with comparison drawn between accuracy and applicability of both approaches. The findings suggest that while traditional methods are valued for their simplicity and low cost, modern methods are provide greater precision and adaptability which are essential for proactive disaster management in a changing climate. This study recommends a hybrid approach that integrate traditional methods with modern technology to enhance the accuracy and dependability of flood forecasting systems