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

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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