Flood Prediction Notebook

In the realm of urban water management, forecasting flood events with high precision is critical for safeguarding infrastructure, managing resources, and protecting communities. The ability to predict a critical event means anticipating and preparing for flood events with the utmost accuracy. This XMPro Notebook applies a machine learning approach, utilizing Support Vector Regression (SVR), to predict the probability of flood events in an urban setting. By analyzing historical weather data and utilizing real-time environmental monitoring, we aim to enhance flood prediction models, offering a valuable tool for water utilities and disaster response teams in their efforts to mitigate the impacts of floods in urban areas.