Spatial Predictive Modeling of the Burning of Sugarcane Plots in Northeast Thailand with Selection of Factor Sets Using a GWR Model and Machine Learning Based on an ANN-CA

Abstract

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.

Spatial Analysis of Influence Factors Associated With Liver Fluke (Opisthorchis viverrini) Infection in Small Sub-Watershed Using GWR Modeling

“…A GWR model was developed in this study to track liver fluke infection. This spatial statistical model is suitable for analysis at the local process level, and the results were compared to confirm that it is more accurate and more appropriate than OLS models in studies [20,21]. However, to make full use of the model, the spatial unit data layer should first be designed to separate the variables accordingly and independently [43][44][45].…”

“…GWR is a geo-weighted regression model. The model serves to determine the coefficient of the relationship between the independent and dependent variables using the distance reciprocal weighting method, which differs in results from the original method (OLS), where GWR obtains a model to predict every unit area with a difference in coefficients [9,21,22]. GWR modeling must create a data layer based on this research, namely the percentage of liver fluke infection of the sub-basin region to be analyzed from 5-meter DEM data, the import of independent variables consisting of index variables generated from the wavelength correlation of satellite images in mathematical functions, and other spatial factors such as distance from water bodies and roads.…”

“…Many studies have applied spatial statistics to analyze spatial correlation factors to liver fluke infection [17], such as studies [18,19] that analyzed a large area, resulting in discrepancies and incoherence in raster data. Based on the findings of [20][21][22], GWR (geographic weighted regression) models were constructed in small area unit systems in hydrological factor analysis, resulting in high R 2 values in all other models.…”

Machine-Learning-Based Forest Classification and Regression (FCR) for Spatial Prediction of Liver Fluke Opisthorchis viverrini (OV) Infection in Small Sub-Watersheds

“…(2) When modeling the relationship between liver flukes, other types of parasites, and spatial factors, OLS uses a global model of spatial statistics, i.e., a model created specifically for each sub-basin, which allows for predicting liver flukes and other types of parasites and analyzing the relationships. The model serves to determine the coefficient of the relationship between the independent and dependent variables using the distance reciprocal weighting method, where OLS obtains a model to predict every unit area with a difference in coefficients [9,21,22]. OLS modeling must create a data layer based on this research, namely the percentage of liver fluke infection of the sub-basin region to be analyzed from 5 m DEM data, the import of independent variables, consisting of the index variables generated from the wavelength correlation of satellite images in mathematical functions, and other spatial factors, such as the distance from water bodies and roads; the detailed procedure is shown in Figure 4, and OLS is shown in Equation (1) [25].…”

“…Various studies have used spatial statistics to analyze correlation factors with liver fluke infection [17], such as [18,19], which analyzed a large area, resulting in discrepancies and incoherence in the raster data. Based on the findings of [20][21][22], the limitations of data acquisition at the area level are sometimes inconsistent with the image point size from satellite imagery, which results in model discrepancies as these limitations accumulate. As a result, models studied at the regional scale area cannot be used as representations at the small watershed level.…”