文献类型：期刊文献

英文题名：Impact assessment of land-use alteration on land surface temperature in Kabul using machine learning algorithm

作者：Ullah, Sajid[1,2];Abbas, Mohsin[3];Qiao, Xiuchen[1]

机构：[1]East China Univ Sci & Technol, Sch Resources & Environm Engn, Shanghai, Peoples R China;[2]Nangarhar Univ, Dept Water Resources & Environm Engn, Jalalabad, Afghanistan;[3]Tsinghua Univ, Dept Hydraul Engn, Beijing, Peoples R China

年份：2024

外文期刊名：JOURNAL OF SPATIAL SCIENCE

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001258068000001)】；

基金：We would like to express our heartfelt appreciation to all anonymous reviewers for their valuable suggestions.

语种：英文

外文关键词：Urbanization; LST; CA-Markov; Random Forest; Machine Learning; ANN; Kabul

摘要：This research evaluates the impact of LULC changes on LST of Kabul City, Afghanistan using Landsat data and Machine Learning Algorithm. The Cellular Automata Markov (CA-Markov) and Artificial Neural Network (ANN) models were used for future predictions. Results showed a significant increase in built-up areas, such as 8.54%. However, the vegetation and bare soil were reduced by approximately 6.97% and 2.18% between 1990 and 2020, respectively. The maximum annual mean LST was found in built-up areas, followed by bare soil and vegetation, while the mean annual LST increased by about 3.52 degrees C. According to seasonal analysis, LST was reported higher during the summer, followed by autumn, spring, and winter. Future predictions showed that built-up areas, which were 14% in 2020, are likely to increase to 18% and 20% in 2030 and 2040, respectively. The regions with higher annual mean LST class (>30 degrees C) are expected to grow by about 58% and 70% in 2030 and 2040, respectively. This research would improve the urban planning to avoid any possible effects of Urban Heat Islands (UHIs).