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
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...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-09-01
|
| Series: | Symmetry |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-8994/14/10/1989 |
| _version_ | 1797469840697458688 |
|---|---|
| author | Patiwat Littidej Theeraya Uttha Benjamabhorn Pumhirunroj |
| author_facet | Patiwat Littidej Theeraya Uttha Benjamabhorn Pumhirunroj |
| author_sort | Patiwat Littidej |
| collection | DOAJ |
| description | 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 R<sup>2</sup> 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 (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>PM</mi></mrow><mrow><mn>2.5</mn></mrow></msub></mrow></semantics></math></inline-formula>) can be reduced. |
| first_indexed | 2024-03-09T19:26:44Z |
| format | Article |
| id | doaj.art-55a1da2a83484bb0b028d69b6764deed |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-09T19:26:44Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-55a1da2a83484bb0b028d69b6764deed2023-11-24T02:50:13ZengMDPI AGSymmetry2073-89942022-09-011410198910.3390/sym14101989Spatial 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-CAPatiwat Littidej0Theeraya Uttha1Benjamabhorn Pumhirunroj2Department of Geoinformatics, Geoinformatics Research Unit for Spatial Management, Faculty of Informatics, Mahasarakham University, Maha Sarakham 44150, ThailandDepartment of Geoinformatics, Geoinformatics Research Unit for Spatial Management, Faculty of Informatics, Mahasarakham University, Maha Sarakham 44150, ThailandProgram in Animal Science, Faculty of Agricultural Technology, Sakon Nakhon Rajabhat University, Sakon Nakhon 47000, ThailandThe 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 R<sup>2</sup> 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 (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>PM</mi></mrow><mrow><mn>2.5</mn></mrow></msub></mrow></semantics></math></inline-formula>) can be reduced.https://www.mdpi.com/2073-8994/14/10/1989sugarcane burningfire radiative power (FRP)land-use changespatial GWR modelingANN-CAPM<sub>2.5</sub> |
| spellingShingle | Patiwat Littidej Theeraya Uttha Benjamabhorn Pumhirunroj 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 Symmetry sugarcane burning fire radiative power (FRP) land-use change spatial GWR modeling ANN-CA PM<sub>2.5</sub> |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | sugarcane burning fire radiative power (FRP) land-use change spatial GWR modeling ANN-CA PM<sub>2.5</sub> |
| url | https://www.mdpi.com/2073-8994/14/10/1989 |
| work_keys_str_mv | AT patiwatlittidej spatialpredictivemodelingoftheburningofsugarcaneplotsinnortheastthailandwithselectionoffactorsetsusingagwrmodelandmachinelearningbasedonanannca AT theerayauttha spatialpredictivemodelingoftheburningofsugarcaneplotsinnortheastthailandwithselectionoffactorsetsusingagwrmodelandmachinelearningbasedonanannca AT benjamabhornpumhirunroj spatialpredictivemodelingoftheburningofsugarcaneplotsinnortheastthailandwithselectionoffactorsetsusingagwrmodelandmachinelearningbasedonanannca |