Author:
Li Anqi,Zhang Zhenkai,Hong Zenglin,Liu Lingyi,Liu Yuanmin
Abstract
AbstractWith the increasing global population and escalating ecological and farmland degradation, challenges to the environment and livelihoods have become prominent. Coordinating urban development, food security, and ecological conservation is crucial for fostering sustainable development. This study focuses on assessing the "Ecology-Agriculture-Urban" (E-A-U) space in Yulin City, China, as a representative case. Following the framework proposed by Chinese named "environmental capacity and national space development suitability evaluation" (hereinafter referred to as "Double Evaluation"), we developed a Self-Attention Residual Neural Network (SARes-NET) model to assess the E-U-A space. Spatially, the northwest region is dominated by agriculture, while the southeast is characterized by urban and ecological areas, aligning with regional development patterns. Comparative validations with five other models, including Logistic Regression (LR), Naive Bayes (NB), Gradient Boosting Decision Trees (GBDT), Random Forest (RF) and Artificial Neural Network (ANN), reveal that the SARes-NET model exhibits superior simulation performance, highlighting it’s ability to capture intricate non-linear relationships and reduce human errors in data processing. This study establishes deep learning-guided E-A-U spatial evaluation as an innovative approach for national spatial planning, holding broader implications for national-level territorial assessments.
Publisher
Springer Science and Business Media LLC
Reference50 articles.
1. Li, Y. B., Deng, F. R. & Luo, X. The application of “double evaluations” in Changsha national land use and space plan. Planners 36, 33 (2020).
2. Du, H. E., Li, Z. & Zheng, Y. Research progress on assessment of resources and environmentbearing capacity and suitability of land space development. China Min. Mag. 28, 159 (2019).
3. Tao, J. et al. The spatial pattern of agricultural ecosystem services from the production-living-ecology perspective: A case study of the Huaihai Economic Zone, China. Land Use Policy 122, 106355 (2022).
4. Zhou, D., Xu, J. & Lin, Z. Conflict or coordination? Assessing land use multi-functionalization using production-living-ecology analysis. Sci. Total Environ. 577, 136 (2017).
5. Chaturvedi, V. & de Vries, W. T. Machine learning algorithms for urban land use planning: A review. Urban Sci. 5, 68 (2021).