Towards rainstorm event identification: A transfer learning framework using citizen-report texts and multi-source spatial data

Urban rainstorms are often accompanied by various hazardous events (e.g., water inundation and small-scale debris flow) and subsequent consequential events (e.g., infrastructure failure and trapped citizens). It is imperative to achieve rapid identification of such events for timely and ef-fective rainstorm disaster management. However, due to small-sample event classes, it becomes challenging to achieve the desired identification results. In this paper, a cross-domain transfer learning scheme is proposed to identify rainstorm events by combining citizen-report texts with multi-source spatial data. In the proposed framework, we recommend joint distribution adapta-tion (JDA) as the key to improving the identification of target events with inadequate samples by transferring knowledge from similar event domains. Meanwhile, multi-source spatial features are extracted and embedded in the text with linear discriminant analysis (LDA) to overcome the fea-ture incompleteness that is caused by short text length. The proposed approach is verified based on the real data in Wuhan, China. Experimental results show that the knowledge transfer be-tween different event classes helps cities to improve the performance of rainstorm event identifi-cation, with the accuracy increasing from 81% to 92%. Under data imbalance scenarios, the pro-posed scheme outperforms the state-of-the-art methods, such as sampling-based methods, ERNIE, and TCA. At the same time, the combination of textual and spatial features results in significant improvement in event identification, as evident by an 8% increase in accuracy. This framework can be followed to build a rainstorm event warning and assistance system to rapidly adapt man-agement responses.