Forecasting of Precipitation in India by Different Data Types using Investigation of Radial Basis Function Neural Network Model
Premananda Sahu, Sanjeev Kumar, Rakesh Ahuja, Amandeep Kaur · 2021
There is a rainy season occurs during the period from June to august in almost all geographical parts of India. Moreover, some of the states like Uttarakhand, Cherapunji, Mumbai, Tamil Nadu etc. may suffer from some natural disaster. If we early predict such misfortunes through the variety of big data collected for such distinct positions at a particular amount of time then certainly can save the life and goods from such big natural calamities. Such normalized data can be updated at a regular interval of time. In view of this, the time series data analysis provides a method to early aware and protects the life of people from such natural disasters. The proposed method exploited the use the Radial Basis Function Neural Network Model with back propagation algorithm to make compatible with time series data analysis to forecast the predication of rainfall for the state of Punjab, India. In this technique, two types of predictions are used which are based on fifteen and twenty days. The comparison results reveal those fifteen days prediction provides more effective classification accuracy than twenty days prediction.