基于人工神经网络工具的城市演化格局时空分析与预测

IF 1.5 Q3 URBAN STUDIES
Deshbhushan Patil, Rajiv Gupta
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引用次数: 0

摘要

土地利用和土地覆被(LULC)的精确量化在保持可持续性方面发挥着至关重要的作用,而这正受到日益增长的城市化的影响。本研究提出了综合地理信息系统(GIS)与人工神经网络(ANN)相结合的方法来分析城市过去的发展模式,以预测未来的土地变化。在本研究中,以印度斋浦尔市为例,利用分类地图分析了过去三十年(1990年、2000年、2010年、2015年和2020年)的土地变化,结果表明,该城市的建设用地增加了46.55%。随后,利用多层感知器(MLP)和元胞自动机(CA)模拟了2030年的土地转型图,预计通过减少9.44%和3.24%的荒地和植被,建设用地将增加12.68%。模拟提供了强有力的证据,表明大多数中等建设用地密度的直辖市在未来十年将转变为高密度的建设用地密度区,并通过专门开发的逐区建设用地密度图将其可视化。本文提出的模拟地图利用方法有助于编制综合微观层面的城市发展规划,将自然资源保护和土地利用规划相结合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spatiotemporal analysis and prediction of urban evolution patterns using Artificial Neural Network tool
The precise quantification of Land Use Land Cover (LULC) plays a vital role in preserving sustainability, which is being affected by growing urbanization. The study proposes the comprehensive Geographical Information System (GIS) approach in integration with Artificial Neural Network (ANN) to analyse the past development patterns of the city for predicting future land transformations. In the present study, land transformations over the past three decades (for years 1990, 2000, 2010, 2015, and 2020) were analysed using classified maps for Jaipur city, India, as a case study, which reveals that the built-up land was increased by 46.55%. Subsequently, the simulated land transformation map for the year 2030 using the Multi-Layer Perceptron (MLP) and Cellular Automata (CA) anticipates that the built-up land would be increased by 12.68% by cutting down the barren land and vegetation by 9.44% and 3.24%, respectively. The simulation offers strong evidence that most of the medium-built-up land density municipality wards transform into high-density built-up land density wards during the next decade, which is visualized through the exclusively developed ward-by-ward built-up land density maps. The utilization of the simulated map in a proposed way helps to prepare the comprehensive micro-level urban development plan by incorporating natural resource conservation and land use planning.
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来源期刊
CiteScore
3.00
自引率
30.00%
发文量
20
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