Flash Flood Detection and Alert System Using Machine Learning

Abstract: Floods can be considered as one among the top natural disasters that sometimes happens regularly in particular season thereby causing harm on human lives and also resultsin reducing economic growth. So, the news about the flooding events has to be spread to nearby localities in time so as to avoid further chaos. Therefore, it is crucial to build a warning system that informs the flooding event to reduce the casualties of flood disaster. Recognizing sensitive events in images, such as flood events is significant for the maintenance of normal public opinionand social stability. By now, it is still a challenging problem. In this project, we propose a novel method for recognizing flood events from images using Keras pretrained MobileNet CNN. Flooding and non-flooding images are collected and trained in theCNN network so as to build the classifier that can differentiate the input image from flooding and non-flooding categories. At first, the feature extraction is carried to develop a model that is capable of classifying flood images from normal images. Later the MobileNet CNN helps in classifying the flood images effectively with accuracy and an appropriate warning message is sent to the people in nearby localities. To enhance the efficacy of our flood event recognition system, we are integrating cutting-edge tech- nologies and methodologies. In addition to leveraging the power of Keras pretrained MobileNet CNN for image classification, we are also implementing advanced image processing techniques for better feature extraction and analysis. By incorporating thesemethods, we aim to achieve higher accuracy and reliability in identifying flood events from images.