The transformation of land-use and land cover in Nakhon Ratchasima province, Thailand has rapidly changed over the last few years. The major factors affecting the growth in the province arise from the huge expansion of developing areas, according to the government’s development plans that aim to promote the province as a central business-hub in the region. This development expansion has eventually intruded upon and interfered with sub-basin areas, which has led to environmental problems in the region. The scope of this study comprises three objectives, i.e., (i) to optimize the Cellular Automata (CA) model for predicting the expansion of built-up sites by 2022; (ii) to model a linear regression method for deriving the transition of the digital elevation model (DEM); and (iii) to apply Geographic Weighted Regression (GWR) for analyzing the risk of the stativity of flood areas in the province. The results of this study show that the optimized CA demonstrates accurate prediction of the expansion of built-up areas in 2022 using Land use (LU) data of 2-year intervals. In addition, the predicting model is generalized and converged at the iteration no. 4. The prediction outcomes, including spatial locations and ground-water touch points of the construction, are used to estimate and model the DEM to extract independent hydrology variables that are used in the determination of Flood Risk Susceptibility (FRS). In GWR in the research called FRS-GWR, this integration of quantitative GIS and the spatial model is anticipated to produce promising results in predicting the growth and expansion of built-up areas and land-use change that lead to an effective analysis of the impacts on spatial change in water sub-basin areas. This research may be beneficial in the process of urban planning with respect to the study of environmental impacts. In addition, it can indicate and impose important directions for development plans in cities to avoid and minimize flood area problems.

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.

The main purpose of the study is to apply symmetry principles to general mathematical modelling based on multi-criteria decision making (MCDM) approach for use in development in conjunction with geographic weighted regression (GWR) model and optimize the artificial neural network-cellular automaton (ANN-CA) model for forecasting the sugarcane plot burning area of Northeast Thailand. First, to calculate the service area boundaries of sugarcane transport that caused the burning of sugarcane with a fire radiative power (FRP) values using spatial correlation analysis approach. Second, the analysis of the spatial factors influencing sugarcane burning. The study uses the approach of symmetry in the design of algorithm for finding the optimal service boundary distance (called as cut-off) in the analysis of hot-spot clustering and uses calculations with the geographic information system (GIS) approach, and the final stage is the use of screened independent variable factors to predict the plots of burned sugarcane in 2031. The results showed that the positively related factors for the percentage of cane plot sintering in the sub-area units of each sugar plant's service were the distance to transport sugarcane plots index and percentage of sugarcane plantations in service areas, while the negative coefficients were FRP differences and density of sugarcane yield factors, according to the analysis with a total of seven spatial variables. The best GWR models display local R2 values at levels of 0.902 to 0.961 in the service zones of Khonburi and Saikaw. An influential set of independent variables can increase the accuracy of the ANN-CA model in forecasting with kappa statistical estimates in the range of 0.81 to 0.85 The results of the study can be applied to other regions of Thailand, including countries with similar sugarcane harvesting industries, to formulate policies to reduce the exposure of sugarcane harvested by burning methods and to support the transportation of sugarcane within the appropriate scope of service so that particulate matter less than 2.5 microns () can be reduced.

The objective this research for estimate minimum cost of gabage from its sources to disposal sites in Nonsung District Nakhon Ratchasima Province, Thailand. By using genetic algorithms run in microsoft excel add-ins find the minimum cost and appropriate waste allocation.The distance applied in the simulation was displacement between two points not the true distance along the route. It was found from the study that the factors affecting the pattern of the hauling system and waste allocation were number of sources and waste quantities produced, number and capacity of transfer stations and disposal sites and hauling cost from different points.