Seyed Hossein Chavoshi, Mahmoud Reza Delavar, Mahdieh Soleimani, & Motahareh Chavoshi. (2008). Toward developing an expert GIS for damage evaluation after an earthquake (case study: Tehran). In B. V. de W. F. Fiedrich (Ed.), Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management (pp. 734–741). Washington, DC: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: In an earthquake disaster, having proper estimation about destructed buildings and the degree of destruction, can considerably facilitate decision-making and planning for disaster managers. Using this information, the managers can estimate disaster area and number of victims to determine and allocate required resources. Scientific studies and historical data show that the faults around Tehran, the capital of Iran, are capable to create strong earthquakes which would bring the largest damages in the world history to the city. So it is necessary to be prepared for a rapid and knowledge-based response to such an earthquake. Therefore, development of a knowledge-based model to estimate destruction of buildings is ongoing. The model is going to be developed by using different spatial data obtained from the buildings and its environment in Tehran. This paper outlines the initial results of this research.
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Lida Huang, Tao Chen, Yan Wang, & Hongyong Yuan. (2015). Forecasting Daily Pedestrian Flows in the Tiananmen Square Based on Historical Data and Weather Conditions. In L. Palen, M. Buscher, T. Comes, & A. Hughes (Eds.), ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management. Kristiansand, Norway: University of Agder (UiA).
Abstract: It is important to forecast the pedestrian flows for organizing crowd activities and making risk assessments. In this article, the daily pedestrian flows in the Tiananmen Square are forecasted based on historical data, the distribution of holidays and weather conditions including rain, wind, temperature, relative humidity, and AQI (Air Quality Index). Three different methods have been discussed and the Support Vector Regression based on the Adaptive Particle Swarm Optimization (APSO-SVR) has been proved the most reliable and accurate model to forecast the daily pedestrian flows. The results of this paper can help to conduct security pre-warning system and enhance emergency preparedness and management for crowd activities.
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