Suppr超能文献

运用集成模糊逻辑和耦合协调度模型来模拟建设用地扩张概率。

Modelling built-up land expansion probability using the integrated fuzzy logic and coupling coordination degree model.

机构信息

Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, Jamia Nagar, New Delhi, 110025, India.

出版信息

J Environ Manage. 2023 Jan 1;325(Pt A):116441. doi: 10.1016/j.jenvman.2022.116441. Epub 2022 Oct 12.

Abstract

The expansion of built-up area is the most noticeable form of urbanization-induced land use/land cover (LULC) change. In the global cities of south, the urban sprawl is increasing rapidly with even higher probabilities of future built-up expansion. These cities are witnessing unsustainable urban growth with no consideration of eco-friendly environmental condition and quality of life due to rapid expansion in built-up area. Indian cities too have been witnessing rapid urban growth and built-up expansion especially in the large metropolitan cities like Delhi. Therefore, the main objective of this study is to model the built-up expansion probabilities in Delhi National Capital Region (Delhi NCR) using remote sensing datasets and an integrated fuzzy logic and coupling coordination degree model (CCDM). For this, initially, the LULC classification was done using random forest (RF) classifier to extract the built-up area. Further, analytical hierarchy process (AHP) integrated fuzzy sets were applied using the extracted built-up area along with a set of economic, demographic, proximity parameters, topographic, and utility services. Five zones of built-up expansion probabilities were made namely very high, high, medium, low and very low. The result shows that the probability of built-up expansion in Delhi NCR is maximum under very high and high probability zones, whereas minimum expansion probabilities come in the very low probability zone for both base year i.e., 2018 and future years. Moreover, between base year and future years, the probability of built-up expansion has increased maximum (5.72%) under the very high zone while it declined by 14.06% in low probability zone. The validation of built-up probability using CCDM shows that the AHP integrated fuzzy logic-based probability model is robust while predicting built-up probability. The results of this study may provide useful insights for the urban planning department and policy makers to mitigate the adverse impacts of built-up expansion. Similar approach may be utilized in the analyzing the built-up urban expansion of other major cities of the world similar geographical conditions.

摘要

建成区的扩张是城市化导致土地利用/土地覆盖(LULC)变化最显著的形式。在全球南方的城市中,城市扩张正在迅速增长,未来建成区扩张的可能性更高。由于建成区的快速扩张,这些城市正在见证不可持续的城市增长,而没有考虑到生态友好的环境条件和生活质量。印度的城市也一直在见证快速的城市增长和建成区扩张,特别是在德里等大城市。因此,本研究的主要目的是使用遥感数据集和综合模糊逻辑和耦合协调度模型(CCDM)来模拟德里国家首都地区(德里 NCR)的建成区扩张概率。为此,最初使用随机森林(RF)分类器对 LULC 进行分类,以提取建成区。进一步,应用基于层次分析法(AHP)的模糊集,结合一套经济、人口、临近参数、地形和公共服务设施,提取建成区。制作了五个建成区扩张概率区,分别为极高、高、中、低和极低。结果表明,在极高和高概率区,德里 NCR 的建成区扩张概率最大,而在极低概率区,无论是基年还是未来年份,扩张概率最小。此外,在基年和未来年份之间,极高区的建成区扩张概率增加最大(5.72%),而低概率区的概率下降了 14.06%。使用 CCDM 对建成区概率进行验证表明,基于 AHP 集成模糊逻辑的概率模型在预测建成区概率时是稳健的。本研究的结果可为城市规划部门和决策者提供有用的见解,以减轻建成区扩张的不利影响。类似的方法可以用于分析世界上其他具有类似地理条件的主要城市的建成区城市扩张。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验