# Conceptual Framework of an Intelligent Decision Support System for Smart City Disaster Management
## 3 Methodology
### Machine Learning Based on a Data Analysis Algorithm for AI Management  
The algorithm modeled for the machine learning data analysis is displayed in Figure 5. It uses convolutional neural network (CNN) and generative adversarial network (GAN) algorithms for natural disaster detection and employs reinforcement learning (RL) to predict wildfire direction. A CNN is a specific type of artificial neural network that uses perceptron, a machine learning unit algorithm, to analyze the data. A CNN can be applied to image processing, natural language processing, and other types of cognitive tasks. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Disaster Type** & **Data Source** & **Question** \\ \hline \multirow{2}{*}{Extreme Weather} & Statistical Geographic Information & Current status of population and households \\  & Service (Open API) & in heat/cold wave expected areas \\ \hline \multirow{2}{*}{Extreme Weather} & Statistical Geographic Information & Status of land use and age of buildings in \\  & Service (Open API) & heat/cold wave expected areas \\ \hline \multirow{2}{*}{Extreme Weather} & Data Portal (Open API) & Status of operation of hot/cold shelter in \\  & & heat/cold wave expected areas \\ \hline \multirow{2}{*}{Extreme Weather} & Korea Centers for Disaster Control & Cases of human damage from past \\  & and Prevention (Document) & heat/cold waves \\ \hline \multirow{2}{*}{Extreme Weather Wildfire} & Korea Meteorological Administration & Weather status (temperature, precipitation, \\  & (Open API) & wind, humidity, atmospheric pressure) \\ \hline \multirow{2}{*}{Extreme Weather Wildfire} & CCTV, UAV, Satellite (Image, Video) & Disaster image information \\ \hline \multirow{2}{*}{Heat Wave} & K-Water (Open API) & Status of water supply in heat/cold wave \\  & & expected areas \\ \hline \multirow{2}{*}{Wildfire} & Data Portal (Open API) & Forest fire danger rating index \\ \hline \multirow{2}{*}{Wildfire} & Data Portal (Open API) & Past wildfire disaster cases \\ \hline \multirow{2}{*}{Wildfire} & Korea Database on Protected Areas & Status of cultural assets and hazardous \\  & (CSV) & facilities near the forest area \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Questions and data sources required for disaster response (Application Programming Interface (API), Closed-Circuit Television (CCTV), Unmanned Aerial Vehicle (UAV)). Figure 5: Wildfire response: the proposed system diagram. The GAN algorithm, which is an unsupervised learning algorithm used in the model, utilizes image data both from fires and from normal conditions for learning. Real-time CCTV and Unmanned Aerial Vehicle (UAV) data are then fed into and trained in the models to determine the occurrence of a fire. If it is not a forest fire, then the used data will be stored and utilized for subsequent training. In the case of forest fires, significant information about the weather and real-time satellite images are required. The weather data include the temperature, wind speed, wind direction, rainfall, and humidity. At this point, the model is transformed into an enhanced model that predicts the propagation speed and the direction of the fire per unit of space. The following discussion presents how the proposed algorithm can be used for fire detection and response. CNNs were first introduced by [PERSON] et al. [31]. By collective knowledge from the training data, computers can learn and understand a structure based on a hierarchy of concepts. A common CNN architecture contains various types of processing layers including convolution, pooling, and fully connected layers [32]. The optimization process of a CNN is conducted by trial and error and guided by checking the validation set error [33]. In this study, the overall architecture and layers of FireNet [33] were used. The FireNet CNN architecture contains three convolutional layers of sizes 64, 128, and 256, with kernel filters of sizes \(5\times 5\), \(4\times 4\), and \(1\times 1\), respectively.