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• W3134006181 endingPage "102817" @default.
• W3134006181 startingPage "102817" @default.
• W3134006181 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." @default.
• W3134006181 created "2021-03-15" @default.
• W3134006181 creator A5044340283 @default.
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• W3134006181 date "2021-06-01" @default.
• W3134006181 modified "2023-09-27" @default.
• W3134006181 title "Rainstorm-induced landslides early warning system in mountainous cities based on groundwater level change fast prediction" @default.
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• W3134006181 doi "https://doi.org/10.1016/j.scs.2021.102817" @default.
• W3134006181 hasPublicationYear "2021" @default.
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