大型水电工程蓄水运行期堆积层滑坡往往集中变形，长期受蓄水运行影响而产生变形的滑坡具有类似生物的自组织自适应变形行为特征，三峡库区和溪洛渡库区皆是如此，对于这一现象及其机理，目前的理论和认识难以科学解释、常用的力学分析方法也无法准确预测。项目聚焦大型水电工程防灾减灾重大需求，围绕水库滑坡灾害预防中的关键科学问题，以地球系统科学思想为指导，应用工程地质、岩土力学、遥感、人工智能和非线性理论方法进行研究。通过资料收集、遥感和InSAR时序监测、现场调研，分析建立水库滑坡大数据信息库。进行大数据的深入挖掘研究变形演化规律、提取变形关联特征参量。采用模型试验分析水库滑坡的地质力学机理。融合变形演化规律与地质力学机理，建立四种不同类型水库滑坡变形演化过程的非线性模型，分析揭示滑坡自组织自适应变形机理。构建机器学习模型进行反复训练，建立基于人工智能算法的水库滑坡变形预测模型，最后进行验证和应用。

The accumulation landslides tend to concentrate on deformation during the impoundment and operation period of large-scale hydropower projects. These landslides have the characteristics of self-organizing and self-adaptive behavior similar to organisms, for example, the Three Gorges Reservoir Area and Xiluodu Reservoir Area. At present, it is difficult to explain scientifically and predict accurately. This problem is related to the operation safety of dozens of mega hydropower projects under construction and built in China, and Is the bottleneck restricting reservoir operation. This research focus on major demands for disaster prevention and mitigation of large hydropower projects, and the key scientific problems in the prevention of reservoir landslide disasters. We carry out research guided by the scientific thought of earth system, and application of Engineering Geology, Geotechnical Mechanics, Remote Sensing, Artificial Intelligence and Nonlinear Theory and Method. Through data collection, remote sensing, InSAR time series monitoring and field investigation, the large data database of reservoir landslide is analyzed and established. Deep mining of large data is carried out to study the evolution law of deformation and extract the feature parameters of deformation correlation. It is analyzed that the geomechanics mechanism of reservoir landslide by model test. Combining deformation evolution law with geological mechanics mechanism, four non-linear models of deformation evolution process of different types of reservoirs landslide are established, and self-organizing adaptive deformation mechanism of landslide is analyzed and revealed. Machine learning model is constructed for repeated training, and the prediction model of reservoir landslide deformation based on artificial intelligence algorithm is established. Finally, the model is verified and applied.