# Conceptual Framework of an Intelligent Decision Support System for Smart City Disaster Management
## 1 Introduction
### 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, [PERSON] 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 [PERSON] and [PERSON] to the development of the DSS concept [3]. In 1981, [PERSON], [PERSON], and [PERSON] established a DSS framework in a book entitled _\"Foundations of Decision Support Systems\"_[4]. Similarly, in 1988, [PERSON] et al. [5] proposed a system to solve semistructured and unstructured problems. This type of effective decision-making by combining the information gained from data processing, learning, and decision-making experience is known as \"intelligence DSS.\" Today, DSSs are proven to be an effective support system in businesses. In addition, DSSs have been the foundation for the development of numerous hypotheses and tools, such as artificial intelligence (AI), human-computer interaction, simulation methods, software engineering, and telecommunication for applications in DSSs. The use of DSSs has grown rapidly since the 1950s, particularly with the aid of effective data analysis tools, enabling improved decision-making using data and information. Several attempts are being made by scientists to improve the effectiveness of decision-making by combining technologies from related fields. The components of a DSS include data management, model management, user interface, knowledge management, and users, as presented in Figure 1. A DSS is an interactive system that allows decision-makers to easily analyze and evaluate decision models and to process the data to solve complex, unstructured, and nonrepetitive decision-making tasks. The decision-makers access the system via the interface and management components and extract various types of data and information from the database model according to their requirements. 1. _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 DBMS in a DSS is to store and provide the data for the decision-making. 2. _Model Management:_ The model management component comprises a model base that stores various models necessary for the decision-making as well as a model base management system (MBMS). Particularly, the MBMS plays a key role in decision support by providing functions to develop, modify, and control the models required for the decision-making. 3. _User Interface:_ The user interface is the module system providing an interface between the user and the system for importing and exporting data and performing various analytical procedures. It is also known as the dialogue generation and management software (DGMS) because it provides  
Figure 1: Decision support system components. user-friendly and dialogue functions that are easy to understand and use, via menus or graphics processing formats. 4. _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. 5. _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 options to determine the best alternative. This paper is organized as follows. Section 2 presents a literature review about the applications of DSSs, AI, and big data for disaster management.