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This paper aims to focus on devising a comprehensive approach for avalanche susceptibility mapping leveraging the analytic hierarchical process (AHP) coupled with multi-criteria weighted overlay (MCWO) technique and further prioritizing based on ASSI and flow modeling.

The research methodology comprises four main stages. Initially, relevant spatial data sets, including terrain attributes and meteorological factors, are collected, processed and reclassified. The AHP with MCWO is then applied to establish hierarchical criteria and determine the relative importance of each criterion, resulting in a composite avalanche susceptibility map (ASM). Avalanche sites identified and vectorized from ASM, prioritized using avalanche site susceptibility index (ASSI). Final prioritization is based on RAMMS flow modeling for three sites with the highest ASSI. Finally, a Web-based application, AvalSAFE-LPR, is created using Google Earth Engine for visualization and dissemination of results.

The final analysis of the study area shows that 28.5% is classified as low susceptibility, 56.6% as moderate susceptibility and 14.9% as high susceptibility zones. Additionally, 28 avalanche sites were identified along the LPR, and the three sites with the highest ASSI were modeled using the RAMMS: Avalanche Module.

This research represents a novel approach to identify, vectorize and prioritize the avalanche prone sites by integrating AHP with RAMMS: Avalanche Module and ASSI.

In an era overshadowed by the alarming consequences of climate change and the escalating peril of recurring floods for communities worldwide, the significance of proficient disaster risk management has reached unprecedented levels. The successful implementation of disaster risk management necessitates the ability to make informed decisions. To this end, the utilization of three-dimensional (3D) visualization and Web-based rendering offers decision-makers the opportunity to engage with interactive data representations. This study aims to focus on Thiruvananthapuram, India, where the analysis of flooding caused by the Karamana River aims to furnish valuable insights for facilitating well-informed decision-making in the realm of disaster management.

This work introduces a systematic procedure for evaluating the influence of flooding on 3D building models through the utilization of Web-based visualization and rendering techniques. To ensure precision, aerial light detection and ranging (LiDAR) data is used to generate accurate 3D building models in CityGML format, adhering to the standards set by the Open Geospatial Consortium. By using one-meter digital elevation models derived from LiDAR data, flood simulations are conducted to analyze flow patterns at different discharge levels. The integration of 3D building maps with geographic information system (GIS)-based vector maps and a flood risk map enables the assessment of the extent of inundation. To facilitate visualization and querying tasks, a Web-based graphical user interface (GUI) is developed.

The efficiency of comprehensive 3D building maps in evaluating flood consequences in Thiruvananthapuram has been established by the research. By merging with GIS-based vector maps and a flood risk map, it becomes possible to scrutinize the extent of inundation and the affected structures. Furthermore, the Web-based GUI facilitates interactive data exploration, visualization and querying, thereby assisting in decision-making.

The study introduces an innovative approach that merges LiDAR data, 3D building mapping, flood simulation and Web-based visualization, which can be advantageous for decision-makers in disaster risk management and may have practical use in various regions and urban areas.