aiNet- and GIS-based regional prediction system for the spatial and temporal probability of rainfall-triggered landslides

Changjiang Li (), Tuhua Ma and Xinsheng Zhu

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2010, vol. 52, issue 1, 57-78

Abstract: We developed a real-time forecasting system, aiNet-GISPSRIL, for evaluating the spatiotemporal probability of occurrence of rainfall-triggered landslides. In this system, the aiNet (a kind of artificial neutral network based on a self-organizing system) and GIS are merged for integrating the rainfall conditions into various environmental factors that influence the landslide occurrence and for simulating the complex non-linear relationships between landslide occurrence and its related conditions. Zhejiang Province (101,800 km 2 in area), located in the southeast coastal region of China, is highly prone to the occurrence of landslides during intensive rainfall. Since 2003, the aiNet-GISPSRIL has been used to predict landslides during the rainy seasons in the region. The aiNet-GISPSRIL uses the regional 24-h forecast rainfall information and the real-time rainfall monitoring data from the rain-gauge network as its inputs, and then provides 24-h forecast of the landslide probability for every 1 × 1-km grid cell within the region. Verification studies on the performance of the aiNet-GISPSRIL show that the system has successfully predicted the dates and localities of 304 landslides (accounting for 66.2% of reported landslides during the period). During the period from 2003 to 2007, because the system provided the probability levels of landslide occurrences up to 24-h in advance, gave locations of potential landslides, and timely warned those individuals at high-risk areas, more than 1700 persons living in the risk sites had been evacuated to safe ground before the landslides occurred and thus casualty was avoided. This highly computerized, easy-operating system can be used as a prototype for developing forecasting systems in other regions that are prone to rainfall-triggered landslides. Copyright Springer Science+Business Media B.V. 2010

Keywords: Rainfall-triggered landslides; Spatio-temporal probability forecast; Artificial neural network; Geographic information system; Zhejiang; China (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s11069-009-9351-x

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