부산대학교 도서관

Widespread floods are one of the most destructive natural phenomena that frequently occur in Bangladesh. It devastates human life, food security, shelter, and social and financial losses. Due to Bangladesh's current economic situation, installing and maintaining comprehensive flood gauges for national flood assessments will not be viable. Hence, online social media platforms can be a valuable avenue to get real-world data to perform flood assessments. This study investigates whether the abundance of data collected from social media in Bangladesh could be used to achieve a reliable and consistent flood assessment in Bangladesh. The data gathered from online platforms are related to flooding and exist in the form of videos, images, and text. The data collected is analyzed with a Machine Learning approach. The digital data of images and video frames were converted into numeric values using the VGG-16 architecture. A Convolutional Neural Network takes in the numeric data produced by the VGG-16 for classification. The classification made accuracy of 92% for the image-based model and 87% for the text-based model.