Rainfall Analysis and Rainstorm Prediction using MapReduce Framework

Rainfall data is collected to predict the storm warnings from the hydrological data. This is considered as a research idea as it consumes huge number of records from the distributed system. This paper describes a novel solution to manage the data based on spatial temporal characteristics using a Map Reduce Framework. The workload is classified using Support Vector Machine (SVM). It uses feature selection and reduction algorithm associated with the dataset. Various rainstorm concept prediction is achieved using the big raw rainfall data. The dataset impact parameters are classified into local, hourly, and overall storms. The proposed system serves as a tool for predicting rainstorm from a large amount of rainfall data in a efficient manner. The result indicates the proposed system improves the performance in terms of accuracy and efficiency.