Multimedia big data platform with a deep learning approach for flood emergency management

Abstract

Flood emergency management has been a major issue in the last few decades as it can disrupt human lives as well as the economy and property damage. A flood occurs when the overflow of water that melts relatively dry land. In the hydrology discipline, floods are a field of study and they are the most common and widespread unpredictable weather occurrence of natural sources. Floods can look quite different because anywhere from a few inches of water to several feet is affected by flooding. They can also come on suddenly, or slowly increase. Therefore frequent identification of flood impact levels is very important. This study aims to create a multimedia big data platform with a deep learning approach for flood emergency management. It uses multimedia data as they are freely available as social media data (Twitter and Facebook), satellite image data, crowdsourcing, and sensor network data for mining purposes. As this research based on deep learning and image processing cutting edge technologies, authorities can identify impact level using satellite images and provide a real-time warning for the people or people can use this for self-estimation of flood risk level when they want in their day to day life like detecting passable or low-risk roads in flooding time. The deep neural network plays a major role in feature extraction and data augmentation helps to increase the number of images in the dataset. This study provides a comparative study between VGG16, VGG19, Densetnet169, and MobileNet deep learning models and evaluates the performance by using training and testing data. Dataset compromises of over 1500 data and the conclusions drawn from work prove that the MobileNet model worked with 86% accuracy with high performance. In the latter part of the paper, it will describe future recommendations.