Suitability assessment of urban land use in Dalian, China using PNN and GIS

Ziqian Kang, Shuo Wang (), Ling Xu, Fenglin Yang and Shushen Zhang
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Ziqian Kang: Jilin University
Shuo Wang: Jilin University
Ling Xu: Dalian University of Technology
Fenglin Yang: Dalian University of Technology
Shushen Zhang: Dalian University of Technology

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2021, vol. 106, issue 1, No 40, 913-936

Abstract: Abstract The suitability assessment of land use is crucial to avoid wasting land resources. However, the traditional methods with subjective weights are prone to reduce the reasonability and reliability of assessment. For filling this knowledge gap, the probability neural network (PNN) coupled with GIS was adopted to evaluate the land use suitability in this paper. According to the applications of the urban land resource, the land use was divided into three types (resident, industry and ecological reserve). Thus, the three different assessment criteria systems were built for the three land use types. The result of residential land use indicated that the most suitable, suitable and normal suitable residential land were 401, 272 and 12,406 km2 and mainly located in Changhai, Lvshun and Pulandian accordingly. The most suitable land for industry was in Ganjingzi, Jinzhou and Wafangdian and accounted for 22% of the total area. While the most suitable land for ecological reserve was in Pulandian and Zhuanghe with the area of 1967 km2. The results indicated that the south of Dalian was suitable for the residential land use, north of Dalian was suitable for the ecological land use and the central was suitable for industrial land use. The results were coincided to the actual spatial distribution of land use. The proposed PNN coupled with GIS assessment method in suitability of land use is conducted to provide a more reasonable assessment result that can be used by managers and regulators.

Keywords: Suitability; Land use; PNN; GIS; Dalian (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s11069-020-04500-z

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