Disaster Prediction System using Convolution Neural Network

Flood is a cataclysmic event that occurs far and wide by introducing extraordinary misfortunes. The floods additionally compromise the human life and the economy of the included nation likewise gets influenced. Neural Networks are generally connected in flood gauging and achieved great outcomes. The main objective of the project is to predict the flood situation and intimate about it to the people. The importance of predicting the flood is widely increased. The human life can get damaged due to floods; it can prevent loss to economy and human if it is predicted earlier. For prediction there are various methods but the most commonly used method is Neural Networks. This paper develops deep learning approach by combining Convolutional Neural Network (CNN) and Modified Particle Swarm Optimization (MPSO) for the prediction that the area is flooded or not. CNN extracts the attributes cataclysm in all respects productively. CNN is vigorous to shadow. CNN can get the attributes of calamity palatably and it additionally overpowers the misdetection or miscount by administrators, which further influence the handiness of fiasco help. MPSO is applied as optimizer to search for the optimal parameter values for the CNN training process.