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New publication on problem of developing a rescue plan in disaster response



Disasters pose a serious threat to people’ lives and urban environment, affecting the sustainable development of society. Then it's crucial to quickly develop an efficient rescue plan for the disaster area. However, disaster rescue is rather difficult due to the requirement to develop the optimal rescue plan as quickly as possible according to the information of trapped people and rescue teams, and the amount of information will continue to increase as the rescue proceeds. At present, most of the rescue plans are manually made based on previous rescue experience. But obviously these plans might be the not optimal one. Considering the real-time location data of trapped people, this paper develops a Mixed Integer Non-linear Programming (MINLP) model to find the highest efficient rescue plan To solve the model accurately and efficiently, a bi-level decomposition (BLD) algorithm is presented to iteratively solve a discretized Mixed Integer Linear Programming (MILP) model and its nonconvex Non-linear Programming (NLP) model until a converged solution is obtained. In addition, since more trapped people could be found over time, the built rescue units should also be considered when making a rescue plan for a new stage. To further improve the solving efficiency, an accelerated bi-level decomposition (ABLD) algorithm is also proposed. Finally, a real-world disaster rescue is given to validate the superiority of the proposed ABLD algorithm relative to particle swarm optimization (PSO) algorithm and BLD algorithm.

BDEM Coordinator's Visit to George Mason University

The BDEM project coordinator Professor Akerkar recently visited the Volgenau School of Engineering at George Mason University to discuss the bilateral research and teaching activities and to establish research cooperation on leveraging social media data for social good. Professor Akerkar had meetings with BDEM colleague Dr. Hemant Purohit, Head of Humanitarian Informatics & Social Computing Research Lab (Human Info Lab) - in addition to colleagues and students in the School. The Human Info Lab investigates human behavior in online data streams via interdisciplinary research in Information and Psychological Sciences that informs the design of intelligent/AI systems to augment human capabilities at workplaces. This research is employed in the design of information systems for social good and future smart city services for a variety of domains, including natural crises (e.g. hurricanes), societal crises (e.g. hate, gender violence), and human crises (e.g. terrorism, cyber attacks).

Seminar by Prof. Akerkar on August 19, 2019

Seminar by Prof. Akerkar on August 19, 2019

Prof. Akerkar also delivered a seminar on 'Leveraging Technology and Innovation to Build Resilient Societies' at the Volgenau School of Engineering.

Synopsis of the seminar:

The society needs a system of resilience management going beyond conventional emergency management, in order to address the complexities of integrated systems and the uncertainty of future threats. Resilience focuses on enhancing the performance of a system in the face of multiple hazards, rather than preventing or mitigating the loss of assets due to specific events; it therefore widens the approach to safety and functioning of a complex system. Looking at it from disaster management phases perspective, resilience starts with the prevention, paves to preparedness, is assessed in response and is demonstrated in effectiveness when the recovery phase is complete and lasts less. This seminar focused on our research direction to improve disaster resilience by understanding risk perception mechanisms and risk awareness in diverse communities and for diverse risks.

The BDEM researchers at the European Climate Change Adaptation conference

Dr. Minsung Hong presenting the BDEM research

Dr. Minsung Hong presenting the BDEM research

Dr. Minsung Hong and Professor Rajendra Akerkar (Western Norway Research Institute) participated in the 4th European Climate Change Adaptation conference at Lisbon from 28-31 May 2019. The biennial European Climate Change Adaptation conference is convened by international and European projects on behalf of the European Commission.

Dr. Hong presented ongoing research, in the Big Data Research Group, entitled "Improving Resilience to Extreme Events" at the conference. The research addresses the gap in knowledge that impedes a scalable and versatile emergency data integration and proposes the state-of-the-art technology in the field to a new level through enhanced techniques that bring together machine learning, big data analytics and semantic technologies. This work elaborates our experience in enhancing situational awareness in order to support decision making during extreme climate events by capitalizing on the integration and aggregated analysis of mobility, meteorological, historical, forecasting data, as well as of a multitude of pertinent multimedia data inputs.

Interaction and collaboration with the disaster risk reduction (DRR) community is a critical element in improving climate change adaptation (CCA), as the communities share similar goals and activities. This is important in relation to the goals and targets of three major international agreements: Sendai Framework for DRR and the UN Sustainable Development Goals.

Just out a new research article by the BDEM researcher team

A new research paper entitled “A Framework to Integrate Social Media and Authoritative Data for Disaster Relief Detection and Distribution Optimization“ has been published in the International Journal of Disaster Risk Reduction. In this paper, the BDEM researchers propose an interdisciplinary approach to (natural) disaster relief management. Our framework combines dynamic and static databases, which consist of social media and authoritative data of an afflicted region, respectively, to model rescue demand during a disaster situation. Using Global Particle Swarm Optimization and Mixed-Integer Linear Programming, we then determine the optimal amount and locations of temporal rescue centers. Furthermore, our disaster relief system identifies an efficient distribution of supplies between hospitals and rescue centers and rescue demand points. By leveraging the temporal dimension of the social media data, our framework manages to iteratively optimize the disaster relief distribution.

It is a joint work of Timothy Schempp (CICS, San Diego State University), Haoran Zhang (The University of Tokyo), Alexander Schmidt (Uni Hohenheim), Minsung Hong and Rajendra Akerkar (Vestlandsforsking).

Full article can be found at the journal website.

Geography colloquium series at SDSU

On 8th February, the BDEM coordinator Professor Rajendra Akerkar delivered a colloquium at the Department of Geography, San Diego State University, USA. The topic of this colloquium was “Managing Emergency Situations Using Big Data – A Perspective”. With the advent of Internet of things, social media, wearable devices, mobile phones, and planetary-scale sensing there is an ample opportunity to transform big geo-spatial data into actionable information. The talk focused on emergency management which is fundamentally a spatio-temporally distributed heterogeneous data assimilation problem for situation detection. Professor Akerkar illustrated examples from related projects in his research group and presented key challenges for managing big emergency data.

Photo credit: Tim Schempp

Photo credit: Tim Schempp

The colloquium attended by students, researchers and faculty members of the Department.

SDSU’s The Center for Information Convergence and Strategy (CICS) is one of the partners in BDEM project. The Center is active in diverse domains, such as disaster response, global security, threat reduction, business development, biomedicine, public health, and the digital humanities. It emphasizes data acquisition and analytics, social media solutions, strategic mapping, data mining, and systems integration and development. From visualization to machine learning and predictive analytics, CICS leverages information technology to evaluate current trends and develop strategies based on dynamically evolving content.