Next Article in Journal
Sources and Mechanisms of Low-Flow River Phosphorus Elevations: A Repeated Synoptic Survey Approach
Previous Article in Journal
Assessing the Influence of Environmental Factors on Groundwater Antibiotic Occurrence by Means of Variation Partitioning
Previous Article in Special Issue
The Development and Application of the Urban Flood Risk Assessment Model for Reflecting upon Urban Planning Elements
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessArticle

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

Department of Geo-Informatics, Faculty of Informatics, Mahasarakham University, Mahasarakham 44150, Thailand
Author to whom correspondence should be addressed.
Water 2019, 11(7), 1496;
Received: 11 June 2019 / Revised: 2 July 2019 / Accepted: 12 July 2019 / Published: 18 July 2019
(This article belongs to the Special Issue Recent Advances in the Assessment of Flood Risk in Urban Areas)
PDF [4001 KB, uploaded 22 July 2019]
  |     |  


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. View Full-Text
Keywords: flood risk susceptibility; FRS-GWR modeling; built-up growth prediction; Thailand flood risk susceptibility; FRS-GWR modeling; built-up growth prediction; Thailand

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Littidej, P.; Buasri, N. Built-Up Growth Impacts on Digital Elevation Model and Flood Risk Susceptibility Prediction in Muaeng District, Nakhon Ratchasima (Thailand). Water 2019, 11, 1496.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Water EISSN 2073-4441 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top