APPLICATION OF GEOINFORMATICS AND REMOTE SENSING IN URBAN EXPANSION PREDICTION MODELING FOR COMPARATIVE URBAN ROAD NETWORK PLANNING: A CASE STUDY OF KHON KAEN CITY
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Abstract
The objective of this research is to develop a predictive model for the urban expansion of Khon Kaen city, aimed at supporting analysis and comparison with maps produced from the prioritization of urban road networks included in the urban road network development plan by the Department of Rural Roads. This research integrates urban expansion prediction with three key factors: (1) land use change, (2) road networks, and (3) Digital Elevation Model (DEM) data. The prediction results are evaluated alongside urban road network development data to confirm the suitability of the maps included in the plan. The study area focuses on Khon Kaen Municipality in Khon Kaen Province as a case study. This research employs Landsat satellite images (1999–2019) and Google Maps data to construct a land use change model from 2024 to 2049. Additionally, artificial neural networks are applied to predict future land use trends. When comparing the 2024 simulation with actual conditions, the Kno, Klocation, and Kquantity indices range between 0.6 and 0.8, indicating an acceptable level of accuracy. The model is subsequently used to predict urban expansion and land use for the years 2024, 2029, 2034, 2039, 2044, and 2049. The research findings confirm the alignment of the proposed road network improvements, demonstrating the model's utility in supporting strategic urban planning.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The published articles are copyright of the Engineering Journal of Research and Development, The Engineering Institute of Thailand Under H.M. The King's Patronage (EIT).
References
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