Cloudburst Prediction System Using Machine Learning

Abstract

Abstract: Cloudbursts are intense spells of rainfall and sudden occurrences resulting in much damage to the environment and loss of life. Hence predicting these events with high accuracy plays a critical role during such events. This paper presents systematic study on development and implementation of cloudburst prediction system utilizing a binary classification model. The primary aim of our project is to enhance predictive accuracy and reliability. In this manuscript, the authors propose a cloudburst prediction model that utilizes a multitude of ways for the forecast of cloudburst events based on a variety of features across India to predict real-time cloudburst disasters using certain cloudburst data.. The experiments conducted using logistic regression, SVM, and decision tree classifier models of machine learning. The SVM attained an initial stage of training AUC(Area Under Curve) ranging between 86% for training and 96% for testing. Discussion has been made concerning graphical result and comparison against other models. The system integrates with a weather API that will have real and updated meteoro-logical information like temperature, humidity, sea level pressure, dew point, cloud coverage, and wind speed. The results are then displayed on a web-based user interface that provides users the opportunity to interact with the prediction system