Glacial Lake Outburst Floods

The "Glacial Lake Outburst Floods (GLOFs) Prediction using Machine Learning" project aims to predict the occurrence of GLOFs based on various lake, glacier, environmental, and geospatial characteristics using machine learning models. The system utilizes a synthetic dataset that includes crucial factors such as lake volume, water level, glacier meltwater contribution, temperature, precipitation, wind speed, seismic activity, and past flood events. Additionally, geospatial data like elevation and proximity to fault lines are incorporated for enhanced prediction accuracy. Machine learning algorithms including Decision Tree, Random Forest, and XGBoost are employed to build a predictive model that analyzes these factors and estimates the likelihood of a GLOF occurring.

The project is designed to assist in disaster preparedness and management by providing timely warnings of potential flood events, based on real-time and historical data. The system is built using Python for the backend, with Flask as the framework, while the frontend is developed using HTML, CSS, and JavaScript to offer a user-friendly interface. This solution offers valuable insights for monitoring glacial lakes and mitigating the risks of catastrophic flooding in vulnerable regions. The predictive model can help prioritize resources, enhance safety measures, and contribute to a more resilient infrastructure in areas prone to glacial lake outburst floods.

Keywords: GLOF prediction, machine learning, Decision Tree, Random Forest, XGBoost, synthetic dataset, glacial lake characteristics, environmental conditions, disaster management, Flask, real-time prediction, geospatial data.