摘要
针对传统空间分析技术不易发掘多源、海量滑坡数据中隐藏的模式、趋势和关系等问题,以三峡库区为研究对象,通过多源数据融合提取滑坡孕灾环境和影响因素信息,进而利用动态构建算法建立BP神经网络模型,定量预测滑坡空间易发性,生成滑坡易发性分区图。采用成功率曲线分析预测结果,三层BP神经网络的预测精度达到89.75%,预测结果与野外调查实际情况吻合较好。
Landslide hazard is influenced by many temporal and spatial factors. Traditional spatial analytical techniques cannot easily discover new and unexpected patterns, trends, and relationships that can be hidden deep within very large diverse geographic datasets. Focusing on Three Gorges Reservoir Area, environmental and triggering factors for landslide occur- rences were extracted from multi-source data. Then, quantitative landslide susceptibility in- dices were calculated using the trained three-layered BP neural network, and the landslide susceptibility maps were generated. Finally, success rate curve was used to verify the results of landslide susceptibility mapping, and the results showed the best accuracy of 89. 75%. The validation showed sufficient agreement between the prediction results and existing land- slide. Therefore, the proposed model is an efficient method for landslide intelligent predic- tion, and can provide a significant reference for landslide hazard prediction and assessment.
出处
《武汉大学学报(信息科学版)》
EI
CSCD
北大核心
2013年第8期963-968,共6页
Geomatics and Information Science of Wuhan University
基金
国家自然科学基金资助项目(41271455/D0108)
数字制图与国土信息应用工程国家测绘地理信息局重点实验室开放研究基金资助项目(GCWD201101)
地理空间信息工程国家测绘地理信息局重点实验室经费资助项目(201115)
中央高校基本科研业务费专项资金资助项目(CUGL120207)
关键词
多源数据
三峡库区
滑坡
智能
空间预测
multi-source data
Three Gorges Reservoir Area
landslide
intelligent
spatialprediction