我国国土面积辽阔，自然环境和气候条件复杂多样，地质灾害频繁发生，给人民生命财产安全和社会经济发展造成了极大危害。调查数据显示，滑坡在所有地质灾害中占比高达 70%以上，并具有危害性强、治理困难和分布地域广泛等特点，因此如何有效地进行滑坡地质灾害预测、为滑坡地质灾害的防治工作提供科学依据具有重要意义。本文以陕西省宁强县为研究区域，开展对滑坡地质灾害预测模型的研究，具体的研究内容和成果如下：
(1)首先通过分析研究区地质环境和滑坡形成条件，选取了滑坡地质灾害的主要影响因素。然后基于 GIS 技术对研究区 Landsat8 遥感影像、高程数字模型等数据进行解析，提取了 18 种滑坡影响因子数据，并采用随机森林和 Min-Max 法处理了数据中的缺失值以及量纲差异。最后通过主成分分析法和 Pearson 相关系数法得到了权重占比较大且线性相关性较弱的 10 种滑坡影响因子。
(2)利用支持向量机、逻辑回归、随机森林和 Adaboost 机器学习算法分别构建了四种滑坡地质灾害预测模型，并采用贝叶斯优化算法对模型中的重要超参数进行了优化。将优化后的模型在滑坡数据集上进行实验，结果表明，除多层感知器神经网络模型外，其余四种模型的 Accuracy、F1 Score 和 AUC 均达到了 0.8 以上，为下一步的模型融合奠定了基础。
(3)针对传统 Stacking 模型融合算法忽略了特征变量与输出值之间的关联性和基学习器学习效果的差异性问题，提出了结合次级学习层加入原始特征和精度反馈加权改进的 Stacking 算法，并以此将支持向量机、逻辑回归、随机森林和 Adaboost 模型进行融合，构建了融合型滑坡地质灾害预测模型。实验结果表明，与其他模型相比， 融合型滑坡地质灾害预测模型的 Accuracy、F1 Score 和 AUC 均有明显提升，验证了改进后Stacking 算法的有效性以及对研究区滑坡地质灾害预测的适用性。
(4)基于融合型滑坡地质灾害预测模型，开发了集气象监测、环境展示、滑坡预测和数据管理功能为一体的滑坡地质灾害预测系统，提升了研究区气象监测的及时性、环境展示的直观性、滑坡预测的科学性和数据管理的便捷性。

China has a vast territory and complex natural environment and climate conditions, which lead to frequent occurrence of geological disasters. At the same time, geological disasters had 
caused great harm to the safety of people's lives and property and social economic development.According to the survey data, landslides account for more than 70% of all geological disasters, 
which is difficult to control and widely distributed. Therefore, how to effectively predict landslide geological hazards has a great significance to provide scientific basis for the prevention and control of landslide geological hazards. In this paper, Ningqiang County of Shaanxi Province was taken as the research area to study the landslide geological disaster prediction model. The specific research content and results were as follows:
(1) Firstly, the main influencing factors of landslide geological disasters are selected by analyzing geological environment and landslide formation conditions in the study area. Then, 
Landsat8 remote sensing images and elevation digital model were analyzed based on GIS technology, and 18 landslide impact factors were extracted. The missing values and dimensional differences in the data were processed by random forest and Min-max method. Finally, principal component analysis and Pearson correlation coefficient method were used to obtain 10 landslide influence factors with large weight and weak linear correlation..
(2) Four types of landslide disaster prediction models were built based by using vector machine, logistic regression, random forest, and Adaboost machine learning algorithm, respectively. Notably, bayesian optimization algorithm was used to optimize the important hyperparameters of the model. The results show that except for the multilayer perceptron neural network model, the accuracy, F1 score and AUC of the other four models have reached more than 0.8, which lays a foundation for the next step of model fusion.
(3) Owing to the traditional stacking model fusion algorithm ignores the correlation between the characteristic variables and the output values and the differences in the learning effect of the basic learning machine, this paper proposes an improved stacking algorithm which combines the secondary learning layer with the original features and precision feedback weighting. Based on this, support vector machine, logistic regression, random forest and 
AdaBoost model are fused to build a fusion landslide geological disaster prediction model. The experimental results showed that, compared with other models, the accuracy, F1 score and AUC 
of the integrated landslide geological disaster prediction model were significantly improved, which verifies the effectiveness of the improved stacking algorithm and its applicability to the landslide geological hazard prediction in the study area.
(4) Based on the integrated landslide geological disaster prediction model, a landslide geological disaster prediction system integrating meteorological monitoring, environmental display, landslide prediction and data management functions was developed. The development of the system improved the timeliness of meteorological monitoring, the intuitiveness of 
environmental display, the scientificity of landslide prediction and the convenience of data management in the study area.

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