The fire situation during the dry season of Thailand, in the last 10 years, has become more severe. The Tad Sung Forest Park area has reported the intensity of wildfires for the past 7 years. This research has applied the geographic weighted regression (GWR) model to generate a spatial relationship analysis model for wildfires. This research aims to create a spatial model to analyze the risk of hazardous areas against wildfire and to analyze the factors that affect forest fire risks in order to protect against wildfires. The service area (SA LY) model was obtained through the first approach. The wildfire-GWR results of the study showed that the model can predict at the R 2 level greater than 82% and varies according to the sub-area boundaries. Factors affecting the acceleration of wildfires are (positive coefficient) the digital elevation model (DEM), normalized burn ratio (NBR), land surface temperature (LST) and (negative coefficient) normalized difference vegetation index (NDVI), slope and aspect. In addition, the distance from the road factor has little effect on wildfire intensity in most areas. The results of the research are used to create a risk-sensitive map of wildfires through surveillance by importing the independent variable factors in the model and using it as a prototype of the same kind of space.

Khon Kaen District is in the central, north-east part of Thailand and is being developed to handle the country’s growth. Khon Kaen District is undertaking the project of building a light rail as a facility for the people. Consequently, one of the problems is ensuring adequate parking for people using the light rail service. In general, the symmetry concept naturally used in decision making to finding an optimal solution for decision and optimization problems. In this paper, multi-criteria decision analysis (MCDA) and multi-objective decision making (MODM) were used to solve the parking site selection problem, which made the decision easier. This paper proposed an analytic hierarchy process (AHP) technique, combined with the geographical information system (GIS), to evaluate the weight of the criteria used in the analysis and find potential parking solutions. Furthermore, this paper proposed the application of a linguistic technique with fuzzy TOPSIS methods to analyze the appropriateness of parking site selections from potential candidates to support use of the light rail. The results of the MCDA show that the most suitable parking lot location is along the light rail and closest to the business area. The results of the fuzzy TOPSIS method, both positive and negative ideal decisions, can help inform decision makers in selecting which candidate site is optimal for parking.