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Integrating HEC-RAS and AI for Enhanced Flood Prediction and Management in the Rideau River, Eastern Ontario
* 1, 2 , * 3 , * 2
1  Ecole Nationale du Genie de l'Eau et de l'Environnement de Strasbourg, 1 Cour des Cigarières, 67000 Strasbourg, France
2  Department of Civil Engineering, University of Ottawa, 161 Louis Pasteur Private, Ottawa, ON K1N 6N5, Canada
3  Department of Soils and Agri-Food Engineering, Université Laval, Québec, QC G1V 0A6, Canada
Academic Editor: Junye Wang

Abstract:

This study focuses on the application of Hydrologic Engineering Center's River Analysis System (HEC-RAS) to simulate the dynamic behavior of the Rideau River, located in eastern Ontario, Canada. The study area spans between 25 and 30 km of the river, including a bifurcation at Hogs Back Falls in Ottawa, where the river splits into the Rideau Canal and the Rideau River before merging with the Ottawa River. HEC-RAS is utilized to simulate river discharge, leveraging a substantial database of flow measurements from various gauging stations. These measurements allow for the determination of discharge rates for return periods ranging from 2 to 1000 years, calculated using the Gaussian method. The table below outlines the discharge values to be input into the HEC-RAS model. In addition to hydraulic simulations of the Rideau River, this study employs artificial intelligence (AI) to predict river discharge based on meteorological variables such as maximum temperature (Tmax), minimum temperature (Tmin), mean temperature (Tmean), and precipitation. By integrating AI with traditional hydrological modeling, the study aims to enhance the accuracy and reliability of flood predictions. By gaining a deeper understanding of the influence of temperature on the occurrence of spring floods, researchers and practitioners can improve the effectiveness and applicability of machine learning techniques in flood prediction and management. This study's findings underscore the importance of considering temperature fluctuations, precipitation levels, and historical discharge data in flood modeling. These insights, coupled with the reliable predictions provided by the AI model, empower decision-makers to make more accurate and effective decisions in flood management strategies. This leads to improved mitigation and adaptation measures in response to increasing flood risks. This integrated approach enhances flood prediction capabilities and supports the development of robust and adaptive flood management strategies for the Rideau River and similar watersheds.

Keywords: Water Resource Management; Climate Modeling; Hydrological Modeling; Flood; River; Ontario

 
 
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