基于多场信息数据驱动的滑坡演化多模式切换概率预测和控制研究

项目名称： 基于多场信息数据驱动的滑坡演化多模式切换概率预测和控制研究

项目编号： No.61503144

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 其他

项目作者： 廉城

作者单位： 华中科技大学

项目金额： 22万元

中文摘要： 我国三峡库区地质环境复杂，滑坡灾害多发且严重威胁库区人民生命财产安全。滑坡孕灾机理复杂，外界环境影响因素及作用过程多样，传统的工程地质方法难以建立精确的滑坡机理模型。随着滑坡监测技术的不断提高，急需发展新的滑坡数据挖掘和系统建模方法来有效地分析滑坡多场信息大数据。本项目以三峡库区典型滑坡为研究对象，发展先进的计算智能方法建立多场信息数据驱动的滑坡演化多模式切换概率预测控制模型。利用协同半监督学习和不平衡数据分类技术，试图创建更加合理的滑坡演化状态自适应划分方法。发展新的随机权值神经网络快速学习算法，建立依滑坡演化状态切换的概率预测模型，量化滑坡预测不确定性。进一步研究将滑坡点概率预测模型推广到滑坡面概率预测模型。分析滑坡不同演化状态下的不同孕灾模式和关键控制因子，初步建立滑坡演化分阶段过程控制体系。本课题的研究将对滑坡多场信息数据挖掘、切换预测、过程控制理论等研究产生一定的推动作用。

中文关键词： 滑坡预测；神经网络；机器学习；数据驱动；切换预测

英文摘要： With the complexity of the geologic environments in the Three Gorges region, frequent landslide hazards are serious threats to people's life and property. Due to the complicated disaster-pregnant mechanisms of landslide, the various environmental factors and action processes, precise mechanism model of landslide can hardly be established by traditional engineering geological methods. With the fast development of landslide monitoring technologies, it's urgent to develop new landslide data mining and system modeling methods which can effectively analyze the landslide multi-field information big data. This project will focus on several typical kinds of landslides in the Three Gorges region. Advanced computational intelligence will be employed to establish multi-pattern switched probabilistic predictive control models of landslide evolution based on multi-field information data-driven methods. We will create more reasonable adaptive methods to divide the evolutional state of landslide using co-training style semi-supervised learning and imbalanced data classification approaches. To quantify the uncertainty of landslide prediction, new fast learning algorithms of neural networks with random weights will be developed to establish a probabilistic prediction model which will be switched based on the evolutional state of landslide. And then, we will generalize the landslide point probabilistic prediction model to a landslide surface probabilistic prediction model. Through the analysis of the different disaster-pregnant mechanisms and key control factors in the different evolutional states of landslide, we will lay a theoretical foundation for landslide process control based on different evolutional stages of landslide. In summary, the research of this project will play an important role in the fields of landslide multi-field information data mining, switched prediction, process control theory, etc.

英文关键词： Landslide prediction;Neural network;Machine learning;Data driven;Switched prediction