Application of Machine Learning and Data Mining Techniques for Accurate Cloud Burst Prediction

Cloudbursts are often characterized by intense rainfall over smaller areas, during catastrophic floods and landslides in vulnerable areas. Traditional models and meteorological departments struggle to predict the occurrences due to unstable weather variations. Approximate forecasting plays an important role in meteorology; it will influence the environment and people as it decreases the impact of cloudbursts. This study leverages the power of data mining and machine learning techniques to present the cloudburst prediction system, improving accuracy and providing early warnings. All the data contained is real time and factual. Machine learning techniques like neural networks, decision trees, along with the old meteorological data and satellite imagery are used to find the events and patterns of cloudbursts in the data. Data mining is used to preprocess the data, remove inconsistencies, smooth the data to find the associative rules, which tells us which combination of data items lead to cloudbursts.