Harnessing Machine Learning Algorithms to Model the Association between Land Use/Land Cover Change and Heatwave Dynamics for Enhanced Environmental Management

Kumar Ashwini, Briti Sundar Sil, Abdulla Al Kafy (), Hamad Ahmed Altuwaijri, Hrithik Nath and Zullyadini A. Rahaman
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Kumar Ashwini: Department of Civil Engineering Chaibasa Engineering College, Jhinkpani 833215, Jharkhand, India
Briti Sundar Sil: Department of Civil Engineering, National Institute of Technology, Silchar 788010, Assam, India
Abdulla Al Kafy: Department of Geography & the Environment, The University of Texas at Austin, 305 E 23rd St, Austin, TX 78712, USA
Hamad Ahmed Altuwaijri: Department of Geography, College of Humanities and Social Sciences, King Saud University, Riyadh 11451, Saudi Arabia
Hrithik Nath: Department of Civil Engineering, Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh
Zullyadini A. Rahaman: Department of Geography & Environment, Faculty of Human Sciences, Sultan Idris Education University, Tanjung Malim 35900, Malaysia

Land, 2024, vol. 13, issue 8, 1-30

Abstract: As we navigate the fast-paced era of urban expansion, the integration of machine learning (ML) and remote sensing (RS) has become a cornerstone in environmental management. This research, focusing on Silchar City, a non-attainment city under the National Clean Air Program (NCAP), leverages these advanced technologies to understand the urban microclimate and its implications on the health, resilience, and sustainability of the built environment. The rise in land surface temperature (LST) and changes in land use and land cover (LULC) have been identified as key contributors to thermal dynamics, particularly focusing on the development of urban heat islands (UHIs). The Urban Thermal Field Variance Index (UTFVI) can assess the influence of UHIs, which is considered a parameter for ecological quality assessment. This research examines the interlinkages among urban expansion, LST, and thermal dynamics in Silchar City due to a substantial rise in air temperature, poor air quality, and particulate matter PM 2.5 . Using Landsat satellite imagery, LULC maps were derived for 2000, 2010, and 2020 by applying a supervised classification approach. LST was calculated by converting thermal band spectral radiance into brightness temperature. We utilized Cellular Automata (CA) and Artificial Neural Networks (ANNs) to project potential scenarios up to the year 2040. Over the two-decade period from 2000 to 2020, we observed a 21% expansion in built-up areas, primarily at the expense of vegetation and agricultural lands. This land transformation contributed to increased LST, with over 10% of the area exceeding 25 °C in 2020 compared with just 1% in 2000. The CA model predicts built-up areas will grow by an additional 26% by 2040, causing LST to rise by 4 °C. The UTFVI analysis reveals declining thermal comfort, with the worst affected zone projected to expand by 7 km 2 . The increase in PM 2.5 and aerosol optical depth over the past two decades further indicates deteriorating air quality. This study underscores the potential of ML and RS in environmental management, providing valuable insights into urban expansion, thermal dynamics, and air quality that can guide policy formulation for sustainable urban planning.

Keywords: machine learning; urban microclimate; heatwave; thermal comfort; artificial neural network; environmental management (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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