Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Estimating Land-Use Change Using Machine Learning: A Case Study on Five Central Coastal Provinces of Vietnam
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.
Estimating Land-Use Change Using Machine Learning: A Case Study on Five Central Coastal Provinces of Vietnam
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.
Estimating Land-Use Change Using Machine Learning: A Case Study on Five Central Coastal Provinces of Vietnam
Nguyen Hong Giang (Autor:in) / Yu-Ren Wang (Autor:in) / Tran Dinh Hieu (Autor:in) / Nguyen Huu Ngu (Autor:in) / Thanh-Tuan Dang (Autor:in)
2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Multivariate Adaptive Regression Spline (MARS) , Random Forest Regression (RFR) , Lasso Linear Regression (LLR) , rural land-use , industrial land-use , urban land-use , Environmental effects of industries and plants , TD194-195 , Renewable energy sources , TJ807-830 , Environmental sciences , GE1-350
Metadata by DOAJ is licensed under CC BY-SA 1.0
| Taylor & Francis Verlag | 2023
| DOAJ | 2023
| Taylor & Francis Verlag | 2021
Land-Use and Land-Cover Changes and Urban Expansion in Central Vietnam: A Case Study in Hue City
| DOAJ | 2024