Jingxian Wang, Lida Huang, Guofeng Su, Tao Chen, Chunhui Liu, & Xiaomeng Wang. (2021). UAV and GIS Based Real-time Display System for Forest Fire. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 527–535). Blacksburg, VA (USA): Virginia Tech.
Abstract: When a forest fire occurs, the commander cannot obtain information in time, and the rescue command is like groping in the dark. In order to solve the problem, this research establishes a real-time forest fire display system based on UAV and GIS. The UAV is equipped with visible light and thermal imaging cameras to transmit back forest fire scenes in real time. Based on GIS, the system can extract the boundary of the fire field through image processing and 3D modeling technology, and display various forest fire information on the screen. Through image processing and 3D modeling technology, the boundary of the fire field can be extracted and displayed on the screen. We conducted several experiments to test the accuracy and the reliability of the system. The result shows that the accuracy, reliability and real-time capability can be guaranteed in small-scale forest fires.
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Lida Huang, Guoray Cai, Hongyong Yuan, Jianguo Chen, Yan Wang, & Feng Sun. (2018). Modeling Threats of Mass Incidents Using Scenario-based Bayesian Network Reasoning. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 121–134). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Mass incidents represent a global problem, putting potential threats to public safety. Due to the complexity and uncertainties of mass incidents, law enforcement agencies lack analytical models and structured processes for anticipating potential threats. To address such challenge, this paper presents a threat analysis framework combining the scenario analysis method and Bayesian network (BN) reasoning. Based on a case library
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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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Lizheng Deng, Hongyong Yuan, & Lida Huang. (2018). Optimal UAV 3D Path Planning in Mountainous Environments for Post-Earthquake Multi-region Search. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (p. 1122). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: During the earthquake rescue, rapidly locating the trapped person is a critical issue to reduce casualties. Compared with the ground search after the earthquake, the unmanned aerial vehicle (UAV) life detection is not only more expeditious but also safer. For shortening the mission completion time of UAV, we propose the coupling method of Dijkstra's algorithm and simulated annealing (SA) algorithm to optimize the search path. Concisely, the mathematical model is further abstracted as the Traveling Salesman Problem (TSP) and the shortest loop can be obtained by SA algorithm. The real geo-environment of Jiuzhaigou and the actual large-scale rescue scenarios are taken into consideration. Setting six key search areas as our life detection objects, the UAV 3D path simulation is conducted with MATLAB, which achieves the obstacle avoidance. Our UAV path planning method can significantly speed up the search process and save more people in the post-disaster search.
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Pengfei Zhou, Tao Chen, Guofeng Su, Bingxu Hou, & Lida Huang. (2020). Research on the Forecasting and Risk Analysis Method of Snowmelt Flood. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 545–557). Blacksburg, VA (USA): Virginia Tech.
Abstract: Risk analysis of snowmelt flood is an urgent demand in cold highland areas. This paper focuses on the method for the rapid and reliable forecast of daily snowmelt, snow water runoff, and snowmelt flood risk. A neural network algorithm is used to calculate snow density distribution, snow depth and snow-water equivalent with the brightness temperature data. Then, daily snowmelt is predicted using the degree-day factor method with the temperature distribution. On this basis, we use the steepest descent method and Manning formula with hydrographic information to simulate snow water runoff. We also propose a method to predict the snowmelt flood risk with the geographic feature and historical flood data. The evaluated risk is compared with monitored data in the Xinjiang Autonomous Region of China, which shows good consistency. At last, we develop a risk analysis system to generate the snowmelt flood risk map and provide risk analysis service.
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Tinghao Zhang, Lida Huang, Tao Chen, & Shuo Bai. (2021). GIS Based Emergency Management Framework for Large-scale Events: A Case Study of the Torch Relay Activity. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 503–514). Blacksburg, VA (USA): Virginia Tech.
Abstract: Due to the high popular concentration of large-scale events, once an emergency (like a stampede) occurs, it will often cause severe casualties. Moreover, since the widespread of the COVID-19, the prevention of the novel coronavirus should also be considered during mass gatherings. How to reduce the probability and potential consequence of emergencies is of great significance. This research designs an emergency management framework using ArcGIS-based geographic information technology for large-scale events. To verify the effectiveness of our framework, we take the Winter Olympic torch relay in university as an example. The paper is mainly divided into two parts, emergency resource allocation and the emergency prevention model. The former part focuses on the site selection of emergency sentries and emergency hospitals during the torch relay. In the latter part, an emergency prevention model is designed for two significant emergencies: stampede and epidemic.
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Yan Wang, Hong Huang, Lida Huang, Minyan Han, Yiwu Qian, & Boni Su. (2017). An Agile Framework for Detecting and Quantifying Hazardous Gas Releases. In eds Aurélie Montarnal Matthieu Lauras Chihab Hanachi F. B. Tina Comes (Ed.), Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management (pp. 42–49). Albi, France: Iscram.
Abstract: In response to the threat of hazardous gas releases to public safety and health, we propose an agile framework for detecting and quantifying gas emission sources. Emerging techniques like high-precision gas sensors, source term estimation algorithms and Unmanned Aerial Vehicles are incorporated. The framework takes advantage of both stationary sensor network method and mobile sensing approach for the detection and quantification of hazardous gases from fugitive, accidental or deliberate releases. Preliminary results on street-level detection of urban natural gas leakage is presented. Source term estimation is demonstrated through a synthetic test case, and is verified using Cramér-Rao bound analysis.
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Zewei Zhang, Hongyong Yuan, & Lida Huang. (2018). Study on the Utility of Emergency Map in Emergency Response. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 377–387). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: As modern cities expand rapidly, the loss of emergency has been more serious. To reduce or even avoid losses caused by disasters, using emergency maps to collect, aggregate, analyze, and communicate information is a prerequisite for efficient response. In this paper, we analyzed the impact factors of information transfer efficiency, and constructed the communication model provided by Emergency Map. By comparing the difference with case deduction between the traditional communication mode in emergency response and the new communication mode based on Emergency Map, which is called Group Communication Mode. We proved the Group Communication Mode had the advantages to improve information transfer efficiency in emergency response. Emergency Map can be an effective tool for the timely transfer of information among departments, which put forward a novel communication mode in emergency decision-making process.
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