We aim at alerting users the urban flood deposit using RNN model to identify Weibo posts that report flood waterlogging. We manually curated more than 4,400 waterlogging posts to train the RNN model so that it can precisely identify waterlogging-related posts of Sina Weibo to timely find out urban waterlogging. The RNN model has been thoroughly evaluated, and showed higher accuracy than traditional machine learning methods, such as SVM and GBDT. Furthermore, we have built a nationwide map of more than 8000 urban waterlogging points based on recent three-year microblogging data.
As far as our knowledge, this is the first manually validated dataset of Weibo posts related to urban flood deposits. Also, this is the largest training dataset for building text classification model to identify Weibo posts that truly report waterlogging events. The RNN model trained on our dataset can precisely identify flood deposits via online Weibo classification, and our monitoring system based on WeChat applet would effectively benefit users to reduce the risk and loss caused by flood.