Urban Flood Hazard Assessment: Harnessing Ensemble Machine Learning for Next-Generation Risk Analytics
Authors:-Mrs.T.Sankaramma, Ch.Mahesh, M.Venkata Sai Harshith, Shaik Saad, V.Venkata Sai Sanjay, Shaik Mohammad Ashiq Ilahi
Abstract-Urban flood hazard assessment through an ensemble machine learning approach minimizes the bias of individual models and offers a more detailed insight into the evolution of flood risks over time. By integrating diverse models, this approach increases the precision of flood event predictions. In this research, we utilized an ensemble machine learning framework to analyse flood hazards. The results reveal that the ensemble model outperforms conventional methods, such as the classification and regression tree (CART) and random forest (RF). The generated hazard maps confirm the accuracy of the data, facilitating public awareness and pinpointing regions vulnerable to flooding.
