Improvising flood prediction analysis using dynamic wave routing and data driven hydrological approach

The research aimed to enhance flood prediction analysis through the utilization of dynamic wave routing and data-driven hydrological approaches. A comprehensive dataset, comprising historical hydrological data such as rainfall patterns, river discharge levels, and topographical information of flood-prone regions, was analyzed. Two primary methodologies were explored: dynamic wave routing, which simulated flood propagation through river networks by integrating hydraulic principles and digital elevation models, and datadriven hydrological approaches, employing machine learning algorithms like regression and neural networks for predictive modeling. Performance evaluation metrics such as Nash-Sutcliffe Efficiency (NSE) and Root Mean Square Error (RMSE) were utilized to assess the accuracy of the models. The results revealed that dynamic wave routing exhibited superior accuracy, characterized by high NSE and low RMSE values compared to conventional static modelling techniques. However, data-driven hydrological approaches also demonstrated promising outcomes, particularly when employing ensemble methods. Rigorous validation against observed flood events led to the conclusion that the integration of dynamic wave routing and data-driven approaches forms a robust framework for enhancing flood prediction accuracy and reliability. This research offers valuable insights for the development of effective flood mitigation strategies. Analysing flood predictions is essential to lessening the effects of floods, which can have disastrous effects on infrastructure and populations. Planning for infrastructure, resource allocation, and prompt evacuation all depend on accurate flood event forecasting. There is, however, a research vacuum in evaluating the effectiveness of data-driven hydrological techniques and dynamic wave routing for improving the accuracy of flood predictions. By performing a comparison analysis to identify the best strategy for enhancing flood forecast accuracy, this study seeks to close this gap.