Titlebook: Applied Geography and Geoinformatics for Sustainable Development; Proceedings of ICGGS Wuttichai Boonpook,Zhaohui Lin,Parichat Wetchayont C

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Measurement of PM10, PM2.5, NO2, and SO2 Using Sensors,amely, MS-1, MS-2, and MS-3. Current study results are compared with the previous studies. The results obtained in the current study have been compared with the previous studies. The percentage difference for PM. is found to be (−) 107.28% between MS-1 and reference study. The difference for MS-2 an

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Noise Mapping of Different Zones in an Urban Area During Deepawali Festival,0.60 dBA, and 67.10 dBA, respectively. Based on the observations, noise maps for all 3 days have been prepared using inverse distance weighted (IDW) interpolation method in ArcGIS. Results of L., L., and L. are plotted using GIS tools. Comparison with the standard limits has also been carried out. I

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Digital Twins in Farming with the Implementation of Agricultural Technologies,ant solutions for efficient food production. We also perform a case study on a digital twin paradigm in a solar energy-supplied farm and its contribution to two of the Sustainable Development Goals (SDGs): “Zero hunger” and “Affordable and clean energy.” Furthermore, we outline the purpose of broad

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Machine Learning Approach with Environmental Pollution and Geospatial Information for Mapping Poverlearning models: XGboost, lasso, random forest, and ridge regression for poverty estimation. The result of the study reveals that poverty-related areas are highly correlated with environmental pollution. The random forest technique has the best performance prediction among the four methods, with an

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Sugarcane and Cassava Classification Using Machine Learning Approach Based on Multi-temporal Remoteurvey was used to assess the classification performance and resulted in 68% accuracy for sugarcane and cassava classification. Multi-temporal remote sensing can aid in the mapping of sugarcane and cassava. The developed approach can be used for crop mapping, management, and estimation of crop produc

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