Big data is a collection of a large number of data which contains structured and unstructured information. The process of investigation, sharing, visualizing and dealing with the conventional database and programming procedures is difficult because of this data structure. Nowadays, big data analysis has been applied in different areas such as human services, business process, logical research, natural resource administration, share marketing, network organization and climate modelling. Rainfall prediction is the usage of science and innovation to anticipate the condition of the environment. Therefore, in order to create precise results of rainfall and weather forecasting, a few techniques have been produced. This paper presents an effective rainfall prediction technique namely Adaptive Moment Optimization (AMO) based Long Short-term Memory Neural Network (LSTM-NN) in the big data environment. The AMO is used to find the relevant features for rainfall prediction and LSTM is used to predict the rainfall. Here, LSTM-NN based AMO technique is proposed to perform the prediction of rainfall. This technique utilizes large scale rainfall information for the duration of 1901-2002 Years. The AMO has a higher learning rate and selects the relevant features that help to improve the performance of the model. This information is gathered from different regions of Coastal Andhra Pradesh Region. The test results demonstrated that the proposed strategy achieved nearly 95% of accuracy in all over the districts of coastal Andhra Pradesh Region.
Volume 11 | 02-Special Issue
Pages: 1967-1979