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

Patiwat Littidej,Nutchanat Buasri

doi:10.3390/w11071496

Abstract

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.

Highlights

Land-use in areas typically involves dynamical processes that develop land which changes over time, based on the evolution of the economy, society, and population in the areas [1]
The results of interpreting satellite imagery from the years 2014, 2016, and 2018 using the interpretation method interpreted visually (Visual interpretation) based on the composition of the interpretation consisting of the shape (Shape), size (Pattern), the intensity of colors, and colors (Tone and color), the texture (Shadow) location, and the association (Site) and its relevance are classified into five categories, including; (1) community areas and buildings: areas with all types of buildings and residential trade zones as well as government offices and transportation routes; (2) Miscellaneous areas including open space; (3) other agricultural areas such as areas, fields, garden areas, etc.; (4) water source areas that are both natural and man-made water sources; and (5) forest areas, including natural forest areas and planted forests
The data processing takes a long time, but the results show the resolution and amount of descriptive data that will improve the accuracy of the Geographic Weighted Regression (GWR) model

Summary

Introduction

Land-use in areas typically involves dynamical processes that develop land which changes over time, based on the evolution of the economy, society, and population in the areas [1]. Population growth results and increases the demands and expansion of land-use in such areas. As a consequence, this fabricates land-use planning (especially the expansion of residential and building areas), which leads to substantial replacements owning to restricted resources [2,3,4]. There is an inevitable rational in some areas to replace dwelling and development zones in agricultural lands [5]. In the last five years, the increased development and expansion of built-up areas has impacted dramatically on land-use and land-cover characteristics. In Nakhon Ratchasima (urban zone), Thailand, large areas of agricultural land have essentially been transformed for development, housing, and dwelling

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Conclusion