Rainstorm hazard early warning system in mountainous cities based on groundwater level change fast prediction

Abstract Flood and landslides in mountainous cities triggered by rainstorm can severely impact people’s lives, property and socioeconomic development. The pre-hazard early warning system are crucial to the disaster prevention, and would be an important part of smart city planning. This paper introduced a way to support the pre-hazard identification based on ground water level change fast prediction, which is the key factor for occurrence of the rainstorm-induced hazard. Firstly, the remote monitoring stations supplied by solar power are established, the data about the water content of surface soil and rainfall were real-time collected from different sensors. By introducing the sliding windows of historical data and for prediction, the early warning system are effective in pre-hazard identification as considering the vulnerable environmental factors such as rainfall, surface runoff, temperature, sunshine, vegetations, soil properties and structures. Based on the model analysis on the 4982 time-series samples, taking the sliding window T=7d of historical data and sliding window G=7d for prediction as an example, the root-mean-square-error (RMSE) of the predicted result of the model reached 0.0812, with R2 up to 0.9776. Thus, Our study aims to provide a strategic way in quick response for the connection of pre-disaster planning and urban planning, and the improvement of disaster prevention, mitigation capacities, increasing the resilience of mountainous cities and their inhabitants.