Analysis and Prediction of Land Use in Beijing-Tianjin-Hebei Region: A Study Based on the Improved Convolutional Neural Network Model

Haojie Liu, Jinyue Liu, Weixin Yang, Jianing Chen and Mingyang Zhu
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Haojie Liu: University of Shanghai for Science and Technology, Shanghai 200093, China
Jinyue Liu: Business School, University of Shanghai for Science and Technology, Shanghai 200093, China
Weixin Yang: Business School, University of Shanghai for Science and Technology, Shanghai 200093, China
Jianing Chen: University of Shanghai for Science and Technology, Shanghai 200093, China
Mingyang Zhu: Jiangxi University of Technology High School, Nanchang, Jiangxi 330029, China

Sustainability, 2020, vol. 12, issue 7, 1-25

Abstract: During the rapid economic development of China, there are certain blind decisions made in the use of land resources, which poses a significant threat to sustainable development. With the help of the improved convolutional neural network model, this paper analyzes the land use of the Beijing-Tianjin-Hebei region of China from 1995 to 2018, and provides a prediction for 2023. The research results show that: (1) There is still much room for improvement in the land use of the Beijing-Tianjin-Hebei region, with dry land taking up the largest proportion of land in these three locations; (2) Beijing’s development has been well protected in terms of land use. It is predicted that by 2023, the proportions of its woodland, grassland, and rivers, lakes, reservoirs and ponds would increase by 0.26%, 0.30%, and 0.61%, respectively, compared with their proportion in 2018; (3) the land use type in Tianjin during the research period was generally stable. In 2018, the proportion of its woodland and grassland had increased by 1.04% and 0.61%, respectively, compared with that of 1995; and (4) many ecological and environmental problems were exposed during the construction of highways in Hebei province. The area of sand land, saline-alkali land, marshland, bare land, and bare rock areas have all increased, and their total proportion is predicted to reach 1.48% by 2023.

Keywords: Beijing-Tianjin-Hebei; land use; Convolutional Neural Network Model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

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