Authors: K Lakshma Reddy, Srinath Khemkar, T Yogeshwar, Tammali Naresh
Abstract: Floods are a major natural disaster that can cause and coastal regions. The system is also widespread damage and loss of life. Machine scalable and can be easily adapted to new learning (ML)is a powerful tool that can be used t locations improve the accuracy of flood forecasting • Th paper concludes with a discussion of the and warning systems. This challenges and limitations of the proposed research paper presents a novel ML-based system. The authority flood forecasting and warning system. Als discuss the potential for future research in • The proposed system uses a combination of this area. different ML algorithms to predict the PR likelihood and severity of flood- ing. The algorithms are trained on historical data on rainfall, river levels, and other factors that can contribute to flooding. The system is also able to take into account real-time data, such as current rainfall and Designing a flood detection system using machine learning (ML) involves utilizing historical data to train models that can identify patterns indicative of flooding. Here’s proposed system architecture for flood detection using ML. river levels. • The proposed system was evaluated using dataset of historical flood events. The results showed that the system was able to accurately predict the likelihood and severity of flooding. The system was also able to generate timely warnings. which can help to save lives and reduce property damage. The proposed system has the potential to be used in a variety of settings, including river basins, urban areas,
