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Time series [14] analysis such as frequent changes in the market, sensor updates in space, and instant observation maintenance are some of the examples for sequential data. The research work focuses on time series [22, 23] sequential data such as weather forecasting to predict the accuracy of natural disaster through the changes in sequential information. Since the longitudinal and latitude values from GPS are not always the same and the feed forward network is used to analyze the information which can have multiple hidden states. We have the various features from the Kaggle dataset for identifying the current state without a hidden model using a Bayesian Markov chain model, which can access the columns such as entity and economic changes due to various disasters [2].

Information processing are of two types: i) behavioral processes and ii) cognitive processes that are mostly used in knowledge engineering [15, 16]. Big data streaming data are through online processing via social users from Instagram, Twitter, etc., as video files, audio files, text data, and so on. They are organized according to the need of sequential changes. Transition based on current state and its path as directed graph are the most important analysis for maintaining the network flow. Applications such as weather forecast for detecting the severity through the temperature changes, climate changes, and complex tasks to predict the climate that brings a disaster such as earthquake and volcanic difference [12].