Estimating Land-Use Change Using Machine Learning: A Case Study on Five Central Coastal Provinces of Vietnam

Nguyen Hong Giang, Yu-Ren Wang, Tran Dinh Hieu, Nguyen Huu Ngu and Thanh-Tuan Dang
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Nguyen Hong Giang: Faculty of Architecture, Thu Dau Mot University, Thu Dau Mot 820000, Vietnam
Yu-Ren Wang: Department of Civil Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 807618, Taiwan
Tran Dinh Hieu: Faculty of Architecture, Thu Dau Mot University, Thu Dau Mot 820000, Vietnam
Nguyen Huu Ngu: Faculty of Land Resources and Agricultural Environment, Hue University, Hue 49118, Vietnam
Thanh-Tuan Dang: Department of Industrial Engineering and Management, National Kaohsiung University of Science and Technology, Kaohsiung 807618, Taiwan

Sustainability, 2022, vol. 14, issue 9, 1-20

Abstract: Population growth is one factor relevant to land-use transformation and expansion in urban areas. This creates a regular mission for local governments in evaluating land resources and proposing plans based on various scenarios. This paper discussed the future trend of three kinds of land-use in the five central coast provinces. Afterwards, the paper deployed machine learning such as Multivariate Adaptive Regression Splines (MARS), Random Forest Regression (RFR), and Lasso Linear Regression (LLR) to analyze the trend of rural land use and industrial land-use to urban land-use in the Central Coast Region of Vietnam. The input variables of land-use from 2010 to 2020 were obtained by the five provinces of the Department of Natural Resources and Environment (DONRE). The results showed that these models provided pieces of information about the relationship between urban, rural, and industrial land-use change data. Furthermore, the MARS model proved to be accurate in the Quang Binh, Quang Tri, and Quang Nam provinces, whereas RFR demonstrated efficiency in the Thua Thien-Hue province and Da Nang city in the fields of land change prediction. Furthermore, the result enables to support land-use planners and decision-makers to propose strategies for urban development.

Keywords: Multivariate Adaptive Regression Spline (MARS); Random Forest Regression (RFR); Lasso Linear Regression (LLR); rural land-use; industrial land-use; urban land-use; decision-making (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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