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The intelligent space-time prediction and analysis of the 3D loess slope geological disaster based on GIS

机译:基于GIS的3D黄土坡地地质灾害的智能时空预测与分析

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Because of combined effects of climate, soil conditions, earthquake and wider area of loess in the loess region, the landslide disasters occur frequently in China's northwest. The authors propose an intelligent space-time prediction and analysis method which can be applied widely. The method can achieve 3D expression of prediction and analysis of landslide geological disasters, and it can provide a visual 3D information platform for the results and methods. This paper provides technical methods and theoretical basis to reduce and prevent the landslide geological disasters based on intelligent space-time predicting and analysis method. The main work of this paper is as follows: 1) intelligent modeling; 2) space-time model establishment. It includes two parts: the first part is forecasting 3D space. The program quantitatively calculated 3D stability modulus by taking advantage of GIS-based 3D cell model, and then searched out the most unfavorable slip surface by genetic algorithm. The second part is time predicting; 3) intelligent knowledge base construction. Using artificial intelligence technology to analyze, extract landslide factors and estimate factors' weights, the authors construct an intelligent knowledge base of 3D landslide. It can guide the research and prediction of unknown landslide. Finally, based on this research, the authors search out a most dangerous slip surface successfully by using Jiagou Sun landslide data in the loess, the results of this research have been verified.
机译:由于气候,土壤条件,地震和黄土地震和更广泛的黄土地区的效果,山体滑坡灾害频繁发生在中国的西北部。作者提出了一种智能时空预测和分析方法,可广泛应用。该方法可以实现对滑坡地质灾害的预测和分析的3D表达,并且可以为结果和方法提供可视3D信息平台。本文提供了基于智能时空预测和分析方法的减少和防止滑坡地质灾害的技术方法和理论依据。本文的主要工作如下:1)智能建模; 2)时空模型建立。它包括两部分:第一部分是预测3D空间。该程序通过利用基于GIS的3D电池模型来定量计算3D稳定模量,然后通过遗传算法搜索最不利的滑动表面。第二部分是时间预测; 3)智能知识库建设。作者用人工智能技术分析,提取山地车因素和估算因素的重量,构建了3D滑坡的智能知识库。它可以指导未知滑坡的研究和预测。最后,根据这项研究,作者通过在黄土中使用Jiay Sun Landslide数据成功搜索了最危险的滑动面,这项研究的结果已经过验证。

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