Flood risk assessment in the Karamana river basin, Kerala, using HEC-RAS

Vijayachandran, Lekshmi; Singh, Ajit Pratap

doi:10.1007/s10661-023-11450-z

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…This software offers an array of capabilities for assessing and simulating river hydraulics, encompassing aspects such as floodplain deluge scenarios, sediment conveyance, water surface profiles, and flow rates [26][27][28][29]. It demonstrates its competence in accommodating diverse hydraulic conditions essential for the cartography of floodplains and handling intricate river systems, utilizing Geographic Information System data [30,31]. Nevertheless, it is worth noting that the computational demands of running simulations in HEC-RAS can vary based on the scale and intricacy of the model, sometimes necessitating substantial computational resources [32].…”

Section: Introductionmentioning

confidence: 99%

Modeling Hydrodynamic Behavior of the Ottawa River: Harnessing the Power of Numerical Simulation and Machine Learning for Enhanced Predictability

Cardi,

Dussel,

Letessier

et al. 2023

Hydrology

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The Ottawa River Watershed is a vast area that stretches across Ontario and Quebec and holds great importance for Canada’s people, economy, and collective history, both in the present and the future. The river has faced numerous floods in recent years due to climate change. The most significant flood occurred in 2019, surpassing a 100-year flood event, and serves as a stark reminder of how climate change impacts our environment. Considering the limitations of machine learning (ML) models, which heavily rely on historical data used during training, they may struggle to accurately predict such “non-experienced” or “unseen” floods that were not encountered during the training process. To tackle this challenge, our study has utilized a combination of numerical modeling and ML to create an integrated methodology. Indeed, a comprehensive dataset of river flow discharge was generated using a numerical model, encompassing a wide range of potential future floods. This significantly improved the ML training process to generalize the accuracy of results. Utilizing this dataset, a novel ML model called the Expanded Framework of Group Method of Data Handling (EFGMDH) has been developed. Its purpose is to provide decision-makers with explicit equations for estimating three crucial hydrodynamic characteristics of the Ottawa River: floodplain width, flow velocity, and river flow depth. These predictions rely on various inputs, including the location of the desired cross-section, river slope, Manning roughness coefficient at different river sections (right, left, and middle), and river flow discharge. To establish practical models for each of the aforementioned hydrodynamic characteristics of the Ottawa River, different input combinations were tested to identify the most optimal ones. The EFGMDH model demonstrated high accuracy throughout the training and testing stages, achieving an R2 value exceeding 0.99. The proposed model’s exceptional performance demonstrates its reliability and practical applications for the study area.

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