Flood Disaster Prediction System Based on Remote Sensing GIS with Neural Turing Machine Approach

Abstract

With the advancement of remote sensing and land data framework advancements, they are being applied to each time of flood disaster analysis and assessment to an ever-increasing extent. At present, it is the head issue that structure and build up an integrative application stage for the flood debacle crisis reaction, the quick misfortune assessment and the salvation dynamic bolster dependent on existing information, programming, and techniques. At a similar time, the pattern of innovation creating must be an ideal solution to predict the flood disasters in and out of the coastal regions around the globe. These systems indeed intend for flood disaster management and examine the input parameters which includes of soil humidity, air pressure, and course of the wind, humidity, and rainfall. The sustaining consequence of a kind of disaster is an imperative public health issue, explicitly for defining the positive sustenancerequired by the victims. One such relevant factor in assessing the psychosomaticsignificances remains the valuation of individual disaster-related experiences. This research work presents an efficient machine learning model that enhances the research and provides an efficient based system. This system also gives the prediction of the Research in a location-based data that enhances the Quality of the Research. The proposed research is also immensely helpful in forecasting the upcoming disasters and taking obligatory actions by emergency and release consultants to protect the life of thousands of people before this perilous condition arises using Machine learning approach. The algorithm used for the Research is an Adaptive based Neural turing machine with RNN concept