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Built-Up Growth Impacts on Digital Elevation Model and Flood Risk Susceptibility Prediction in Muaeng District, Nakhon Ratchasima (Thailand)

机译:Muaeng District,Nakhon Ratchasima(泰国)中的数字高度模型和洪水风险易感性预测的建立增长影响

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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.
机译:在过去几年中,泰国在纳克荷西亚省土地利用和陆地覆盖的转变迅速发生了变化。根据政府的发展计划,影响省内增长的主要因素产生了发展领域的巨大扩张,旨在将该省作为该地区的中央商务中心推广。这种发展扩张最终侵入并干扰了子盆地地区,这导致了该地区的环境问题。该研究的范围包括三个目标,即(i),以优化蜂窝自动机(CA)模型,用于预测2022的内置网站的扩展; (ii)模拟用于导出数字高度模型过渡(DEM)的线性回归方法; (iii)应用地理加权回归(GWR),以分析省内洪水区统治性的风险。本研究的结果表明,优化的CA使用2年间隔的土地使用(LU)数据显示了2022年2022年建筑区域扩展的准确预测。另外,预测模型是广泛化的并且在迭代中融合。 4.预测结果包括施工的空间位置和地下水触摸点,用于估计和模拟DEM以提取用于确定洪水风险易感性(FRS)的独立水文变量。在GWR中的研究中称为FRS-GWR,预计该定量GIS的整合和空间模型将产生有希望的结果预测内置区域的增长和扩大,导致对影响的有效分析论水管盆地区域的空间变化。该研究可能有利于城市规划过程中对环境影响研究的过程。此外,它还可以表明和强加城市发展计划的重要方向,以避免和最大限度地减少洪水区问题。

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