WITHDRAWN: An integrated approach for weather forecasting and disaster prediction using deep learning architecture based on memory Augmented Neural Network’s (MANN’s)

Abstract

Abstract Weather forecasting is a real time challenge that has been proven to be worst among the disaster in world over the last decade. Prediction begins even more complicated due to the ever-changing weather conditions. Many attributes have been negotiated with weather forecasting data that consider related attributes as independent variables. It is the cause of such natural disasters floods and droughts that meet people across globe every year. The higher accuracy of predicting rainfall is importance for countries like India as their economy depending on agriculture. Because of the mighty nature, few Statistical and Mathematical techniques fail to provide good rainfall accuracy prediction. The imbalance data of rainfall makes Artificial Neural Network, Recurrent Neural Networks (RNN), MANN’s (LSTM, GRU, NTM) the best process. An effective climate analysis is required to understand the various factors that contribute to climate change. It is therefore necessary to identify the relationship among these qualities to better understanding of the weather data. The purpose of this paper to give non-professionals easy access to strategies as well methods used in the field of rain forecast and compare various results of various methods and algorithms used in research.