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

机译：基于aiNet和GIS的降雨触发滑坡的时空概率区域预测系统

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摘要

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 km2 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. Keywords Rainfall-triggered landslides - Spatio-temporal probability forecast - Artificial neural network - Geographic information system - Zhejiang - China
机译：我们开发了一个实时预报系统aiNet-GISPSRIL，用于评估降雨触发的滑坡发生的时空概率。在该系统中，将aiNet（一种基于自组织系统的人工神经网络）与GIS合并在一起，以将降雨条件整合到影响滑坡发生的各种环境因素中，并模拟滑坡之间的复杂非线性关系。发生及其相关条件。位于中国东南沿海地区的浙江省（面积为101,800 km 2 ）极易在强降雨期间发生滑坡。自2003年以来，aiNet-GISPSRIL被用于预测该地区雨季的滑坡。 aiNet-GISPSRIL使用区域24小时预报降雨信息和雨量计网络的实时降雨监测数据作为输入，然后为每1×1公里网格提供滑坡概率的24小时预报该区域内的单元格。对aiNet-GISPSRIL性能的验证研究表明，该系统已成功预测304个滑坡的发生日期和位置（占该期间报告滑坡的66.2％）。在2003年至2007年期间，由于该系统可以提前24小时提供滑坡发生的概率级别，确定潜在滑坡的位置，并及时警告那些处于高风险地区的人，因此有1700多人居住在该地区。在发生山体滑坡之前，已将危险地点疏散到安全地面，从而避免了人员伤亡。这种高度计算机化，易于操作的系统可以用作在其他容易受到降雨触发的滑坡的地区开发预报系统的原型。降雨触发的滑坡-时空概率预测-人工神经网络-地理信息系统-浙江-中国

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