Modelling Future Land Surface Temperature: A Comparative Analysis between Parametric and Non-Parametric Methods

Yukun Gao, Nan Li, Minyi Gao, Ming Hao and Xue Liu ()
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Yukun Gao: School of Computer Engineering, Suzhou Vocational University, Suzhou 215000, China
Nan Li: Northeast Asia Ecosystem Carbon Sink Research Center (NACC), Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, School of Ecology, Northeast Forestry University, Harbin 150040, China
Minyi Gao: School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Ming Hao: School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Xue Liu: School of Geographic Sciences, East China Normal University, Shanghai 200241, China

Sustainability, 2024, vol. 16, issue 18, 1-17

Abstract: As urban expansion continues, the intensifying land surface temperature (LST) underscores the critical need for accurate predictions of future thermal environments. However, no study has investigated which method can most effectively and consistently predict the future LST. To address these gaps, our study employed four methods—the multiple linear regression (MLR), geographically weighted regression (GWR), random forest (RF), and artificial neural network (ANN) approach—to establish relationships between land use/cover and LST. Subsequently, we utilized these relationships established in 2006 to predict the LST for the years 2012 and 2018, validating these predictions against the observed data. Our results indicate that, in terms of fitting performance (R 2 and RMSE), the methods rank as follows: RF > GWR > ANN > MLR. However, in terms of temporal stability, we observed a significant variation in predictive accuracy, with MLR > GWR > RF > ANN for the years 2012 and 2018. The predictions using MLR indicate that the future LST in 2050, under the SSP2 and SSP5 scenarios, is expected to increase by 1.8 ± 1.4 K and 2.1 ± 1.6 K, respectively, compared to 2018. This study emphasizes the importance of the MLR method in predicting the future LST and provides potential instructions for future heat mitigation.

Keywords: land surface temperature; land use/cover; multiple linear regression; random forest approach; future projections (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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