applied sciences Article Conceptual Framework of an Intelligent Decision Support System for Smart City Disaster Management Daekyo Jung 1, Vu Tran Tuan 1, Dai Quoc Tran 2 , Minsoo Park 2 and Seunghee Park 2,* 1 Department of Convergence Engineering for future City, Sungkyunkwan University, Suwon 16419, Korea; [email protected] (D.J.); [email protected] (V.T.T.) 2 School of Civil, Architectural Engineering & Landscape Architecture, Sungkyunkwan University, Suwon 16419, Korea; [email protected] (D.Q.T.); [email protected] (M.P.) * Correspondence: [email protected] Received: 28 November 2019; Accepted: 13 January 2020; Published: 17 January 2020 Abstract: In order to protect human lives and infrastructure, as well as to minimize the risk of damage, it is important to predict and respond to natural disasters in advance. However, currently, the standardized disaster response system in South Korea still needs further advancement, and the response phase systems need to be improved to ensure that they are properly equipped to cope with natural disasters. Existing studies on intelligent disaster management systems (IDSSs) in South Korea have focused only on storms, floods, and earthquakes, and they have not used past data. This research proposes a new conceptual framework of an IDSS for disaster management, with particular attention paid to wildfires and cold/heat waves. The IDSS uses big data collected from open application programming interface (API) and artificial intelligence (AI) algorithms to help decision-makers make faster and more accurate decisions. In addition, a simple example of the use of a convolutional neural network (CNN) to detect fire in surveillance video has been developed, which can be used for automatic fire detection and provide an appropriate response. The system will also consider connecting to open source intelligence (OSINT) to identify vulnerabilities, mitigate risks, and develop more robust security policies than those currently in place to prevent cyber-attacks. Keywords: decision support system; big data; artificial intelligence; Internet of Things; disaster management 1. Introduction Urban climate change is an important research issue that must be investigated using multidisciplinary approaches that include both engineering and socio-environmental sciences [1]. An effective approach to strengthen disaster reduction strategies is to build a decision support system (DSS) for each region. A DSS is based on the correlation between the infrastructure, industries, and related communities in a region. Information on the technical, social, and economic properties of a region is collected to establish an effective natural disaster prevention strategy. By analyzing the collected data, we can easily identify the vulnerabilities. Disaster-preparedness strategies for all citizens will make it easier to prepare and respond to real disasters. Decision Support System A DSS is an information system that supports decision-making by selecting the best option by developing and comparing multiple alternatives to solve various issues. DSSs have a long history of development. From the late 1950s to the early 1960s at the Carnegie Institute of Technology, Nutt studied the theory of decision-making at an organization [2]. In the 1960s, MIT conducted technical research related to reciprocating computer systems. Subsequent studies by Pomerol and Adam Appl. Sci. 2020, 10, 666; doi:10.3390/app10020666 www.mdpi.com/journal/applsci Appl. Sci. 2020, 10, 666 2 of 13 Appl.Sci, 2019, 11, x; doi: 2 of 13 contributed to the development of the DSS concept [3]. In 1981, Bonczek, Holsapple, and Whinston contributedestablished to a DSSthe frameworkdevelopment in of a book the DSS entitled concept “Foundations [3]. In 1981, of Decision Bonczek, Support Holsapple, Systems and”[4 ].Whinston Similarly, establishedin 1988, Turban a DSS et framework al. [5] proposed in a abook system entitled to solve “Foundations semistructured of Decision and unstructured Support Systems problems.” [4]. Similarly,This type in of 1988, effective Turban decision-making et al. 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Decision support system components. (a) Data Management: The data management component comprises a database that stores various data for decision-making as well as a database management system (DBMS). The function of the a) Data Management: The data management component comprises a database that stores DBMS in a DSS is to store and provide the data for the decision-making. various data for decision-making as well as a database management system (DBMS). 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It is also known as the dialogue generation and management software (DGMS) because it provides user-friendly and dialogue functions that are easy to understand and use, via menus or graphics processing formats. Appl. Sci. 2020, 10, 666 3 of 13 user-friendly and dialogue functions that are easy to understand and use, via menus or graphics processing formats. (d) Knowledge Management: This module provides quantitative information about the relationships between complex data. Knowledge management provides decision-makers with knowledge and alternative solutions for problem solving. It also signals to the decision-makers if there is a difference between the predicted result and the actual result. (e) Users: The users who use a DSS are primarily the managers who are responsible for important business decisions. They choose the most appropriate model from the model base, enter the necessary data from the database or import them directly into the model, and then evaluate and analyze the