Harnessing machine learning algorithms to model the association between land use/land cover change and heatwave dynamics for enhanced environmental management

Zullyadini A. Rahaman

QR Code Link :
Type :	Article
Subject :	SB Plant culture
ISBN :	2073-445X
Main Author :	Zullyadini A. Rahaman
Title :	Harnessing machine learning algorithms to model the association between land use/land cover change and heatwave dynamics for enhanced environmental management
Hits :	28

Place of Production :	Tanjung Malim
Publisher :	Fakulti Sains Kemanusiaan
Year of Publication :	2024
Notes :	Land
Corporate Name :	Universiti Pendidikan Sultan Idris
HTTP Link :	Click to view web link
PDF Full Text :	You have no permission to view this item.

Abstract : Universiti Pendidikan Sultan Idris

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 PM2.5. 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 km2. The increase in PM2.5 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. © 2024 by the authors.

References

Nguyen, H.H.; Recknagel, F.; Meyer, W. Effects of Projected Urbanization and Climate Change on Flow and Nutrient Loads of a Mediterranean Catchment in South Australia. Ecohydrol. Hydrobiol. 2019, 19, 279–288.

PCC. Climate Change 2001. Synthesis Report; IPCC Fourth Assessment Report (TAR); Cambridge University Press: Cambridge, UK; New York, NY, USA; Port Melbourne, VIC, Australia; Madrid, Spain; Cape Town, South Africa, 2007.

Hunt, J.C.R.; Aktas, Y.D.; Mahalov, A.; Moustaoui, M.; Salamanca, F.; Georgescu, M. Climate Change and Growing Megacities: Hazards and Vulnerability. Eng. Sustain. 2017, 171, 314–326.

Wang, H.; Zhang, Y.; Tsou, J.Y.; Li, Y. Surface Urban Heat Island Analysis of Shanghai (China) Based on the Change of Land Use and Land Cover. Sustainability 2017, 9, 1538.

Van Schendel,W. A History of Bangladesh; Cambridge University Press: Cambridge, UK; New York, NY, USA; Port Melbourne, VIC, Australia; Madrid, Spain; Cape Town, South Africa, 2020.

Mishra, V.N.; Rai, P.K. A Remote Sensing Aided Multi-Layer Perceptron-Markov Chain Analysis for Land Use and Land Cover Change Prediction in Patna District (Bihar), India. Arab. J. Geosci. 2016, 9, 249.

Ashwini, K.; Sil, B.S. Impacts of Land Use and Land Cover Changes on Land Surface Temperature over Cachar Region, Northeast India—A Case Study. Sustainability 2022, 14, 14087.

Pathan, S.A.; Ashwini, K.; Sil, B.S. Spatio-Temporal Variation in Land Use/Land Cover Pattern and Channel Migration in Majuli River Island, India. Environ. Monit. Assess. 2021, 193, 811.

Thakur, S.; Maity, D.; Mondal, I.; Basumatary, G.; Ghosh, P.B.; Das, P.; De, T.K. Assessment of Changes in Land Use, Land Cover, and Land Surface Temperature in the Mangrove Forest of Sundarbans, Northeast Coast of India. Environ. Dev. Sustain. 2021, 23, 1917–1943.

Kafy, A.A.; Rahman, M.S.; Faisal, A.A.; Hasan, M.M.; Islam, M. Modelling Future Land Use Land Cover Changes and Their Impacts on Land Surface Temperatures in Rajshahi, Bangladesh. Remote Sens. Appl. Soc. Environ. 2020, 18, 100314.

Ellwanger, J.H.; Kulmann-Leal, B.; Kaminski, V.L.; Valverde-Villegas, J.M.; Veiga, A.B.G.; Spilki, F.R.; Fearnside, P.M.; Caesar, L.; Giatti, L.L.;Wallau, G.L.; et al. Beyond Diversity Loss and Climate Change: Impacts of Amazon Deforestation on Infectious Diseases and Public Health. An. Acad. Bras. Cienc. 2020, 92, e20191375.

Ganaie, T.A.; Jamal, S.; Ahmad, W.S. Changing Land Use/Land Cover Patterns and Growing Human Population in Wular Catchment of Kashmir Valley, India. GeoJournal 2021, 86, 1589–1606.

Weber, C.; Puissant, A. Urbanization Pressure and Modeling of Urban Growth: Example of the Tunis Metropolitan Area. Remote Sens. Environ. 2003, 86, 341–352.

Yamagata, Y.; Seya, H. Simulating a Future Smart City: An Integrated Land Use-Energy Model. Appl. Energy 2013, 112, 1466–1474.

Tang, J.; Di, L.; Rahman, M.S.; Yu, Z. Spatial--Temporal Landscape Pattern Change under Rapid Urbanization. J. Appl. Remote Sens. 2019, 13, 024503.

Shahfahad, S.T.; Mohd, R.; Hang, H.T.; Bhaskaran, S.; Rahman, A. Modelling Urban Heat Island (UHI) and Thermal Field Variation and Their Relationship with Land Use Indices over Delhi and Mumbai Metro Cities. Environ. Dev. Sustain. 2022, 24, 3762–3790.

Rahaman, Z.A.; Kafy, A.-A.; Faisal, A.-A.; Al Rakib, A.; Jahir, D.M.A.; Fattah, M.A.; Kalaivani, S.; Rathi, R.; Mallik, S.; Rahman, M.T. Predicting Microscale Land Use/Land Cover Changes Using Cellular Automata Algorithm on the Northwest Coast of Peninsular Malaysia. Earth Syst. Environ. 2022, 6, 817–835.

Soltani, A.; Sharifi, E. Daily Variation of Urban Heat Island Effect and Its Correlations to Urban Greenery: A Case Study of Adelaide. Front. Archit. Res. 2017, 6, 529–538.

Li, X.-X.; Koh, T.-Y.; Entekhabi, D.; Roth, M.; Panda, J.; Norford, L.K. A Multi-Resolution Ensemble Study of a Tropical Urban Environment and Its Interactions with the Background Regional Atmosphere. J. Geophys. Res. Atmos. 2013, 118, 9804–9818.

Lim, T.K.; Rajabifard, A.; Khoo, V.; Sabri, S.; Chen, Y. The Smart City in Singapore: How Environmental and Geospatial Innovation Lead to Urban Livability and Environmental Sustainability. In Smart Cities for Technological and Social Innovation; Elsevier: Amsterdam, The Netherlands, 2021; pp. 29–49.

Coates, L.; Haynes, K.; O’Brien, J.; McAneney, J.; de Oliveira, F.D. Exploring 167 Years of Vulnerability: An Examination of Extreme Heat Events in Australia 1844–2010. Environ. Sci. Policy 2014, 42, 33–44.

Kleerekoper, L.; van Esch, M.; Salcedo, T.B. How to Make a City Climate-Proof, Addressing the Urban Heat Island Effect. Resour. Conserv. Recycl. 2012, 64, 30–38.

Santamouris, M.; Haddad, S.; Saliari, M.; Vasilakopoulou, K.; Synnefa, A.; Paolini, R.; Ulpiani, G.; Garshasbi, S.; Fiorito, F. On the Energy Impact of Urban Heat Island in Sydney: Climate and Energy Potential of Mitigation Technologies. Energy Build. 2018, 166, 154–164.

This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials.
You may use the digitized material for private study, scholarship, or research.

Back to search page