Improving landslide multi-hazard early warnings in Eastern Himalayas using a Bayesian belief network

研究的主要目的是找出如何进行多灾害滑坡风险评估，以改善早期预警系统在东喜马拉雅山通过了解滑坡和其他自然灾害之间的相互作用，在不同的时间或空间scales.Objectives（O）和问题（Q）：第1阶段（基于文献综述，指导监督员，和采访）O 1。总结在增加/减少滑坡和其他自然灾害风险方面突出的物理和社会变量，这些自然灾害要么引发滑坡，要么由滑坡引发，要么通常与滑坡同时发生。总结不同自然灾害在空间和时间上如何相互关联。O3.探索并总结现有贝叶斯网络如何用于预测不同的灾害。O 4.确定O 1中的哪些变量已经普遍用于多灾害滑坡风险评估的贝叶斯网络节点，哪些变量可能适用于本研究。理解和总结变量是如何相互联系的准备贝叶斯网络的链接。O 6.学习专家启发的原理，并将其作为构建多灾害滑坡风险评估贝叶斯网络的一种方法。对于所选研究区域（最有可能在印度东锡金或大吉岭），总结O4.O7中确定的主要变量的可用数据。使用从O 6中识别的变量收集的数据和专家启发，通过应用贝叶斯网络方法创建一个模型来量化多灾害滑坡风险。将模型与现有的滑坡敏感性图O 9进行对比。在其他区域使用O 7中开发的模型。O 10.制定关于如何将该模型用作早期预警系统一部分的一般准则，特别是针对东喜马拉雅地区

The main aim of the research is to find out how to carry out a multi-hazard landslide risk assessment in order to improve the early warning system in Eastern Himalayas by understanding the interaction between landslides and other natural hazards at different temporal or spatial scales.Objectives (O) and questions (Q):PHASE 1 (based on literature review, guidance of supervisors, and interviews)O1. Summarize the physical and social variables that are prominent in increasing/decreasing the risk of landslides and other natural hazards that either trigger landslides, are triggered by landslides, or are commonly coincident with landslides.O2. Summarize how the different natural hazards are interrelated spatially and temporally.O3. Explore and summarize how existing Bayesian networks are used to forecast different hazards.O4. Determine which variables from O1 are already commonly in use for the nodes of a Bayesian Network for multi-hazard landslide risk assessment and which might be appropriate for this study.O5. Understand and summarize how the variables are interconnected to prepare the linkages of Bayesian Network.O6. Learn principles of expert elicitation with the view of applying this as one methodology for constructing a Bayesian Network for multi-hazard landslide risk assessment.PHASE 2O6. For the study area chosen (most likely in East Sikkim or Darjeeling, India) summarize which data is available for the major variables identified in O4.O7. Using data gathered from variables identified in O6, and expert elicitation, create a model to quantify the multi-hazard landslide risk by applying the Bayesian Network approach.O8. Confront the model against already present landslide susceptibility map O9. Use the model developed in O7 in another region.O10. Produce general guidelines of how this model can be used as a part of Early Warning system, specific for Eastern Himalayas