Grading surface urban heat island and investigating factor weight based on interpretable deep learning model across global cities
Significant urbanization resulted in increasing surface urban heat island (SUHI) that caused negative impacts on urban ecological environment, and residential comfort. Accurately monitoring the spatiotemporal variations and understanding controls of SUHI were essential to propose effective mitigatio...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-10-01
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| Series: | Environment International |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0160412023004695 |
| _version_ | 1797659932522184704 |
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| author | Kangning Li Yunhao Chen Jinbao Jiang |
| author_facet | Kangning Li Yunhao Chen Jinbao Jiang |
| author_sort | Kangning Li |
| collection | DOAJ |
| description | Significant urbanization resulted in increasing surface urban heat island (SUHI) that caused negative impacts on urban ecological environment, and residential comfort. Accurately monitoring the spatiotemporal variations and understanding controls of SUHI were essential to propose effective mitigation measurements. However, SUHI grades across global cities remained unknown, which cloud greatly support for global mitigations. Additionally, quantitative evaluating factor weights for different SUHI indicators and grades worldwide remained further investigations. Therefore, this paper proposed SUHI grading based on agglomerative hierarchical clustering, and further quantified factor weights for different indicators and grades based on an interoperable machine learning named TabNet. There were three major findings. (1) Global cities were grouped into five grades, including SUCI (surface urban cool island), insignificant, low-value, medium-value, and high-value SUHI grades, indicating significant differences among different grades. SUHI grades showed significant climate-based variations, wherein the arid climate was dominated by the SUCI grade at daytime but the high-value grade at nighttime. (2) Vegetation difference was an important factor for daytime SUHII accounting for 27%. Daytime frequency of SUHI was controlled by vegetation difference, temperature, evaporation and nighttime light, accounting for 78%. The major factors for nighttime frequency were albedo differences and nighttime light, accounting for 45%. (3) Related factors contributed differently to various SUHI grades. The weight of △EVI for daytime SUHII gradually increased with grades, while it for daytime frequency and maximum duration of SUHI decreased with grades. The nighttime SUHII of the low-value grade was greatly affected by the background climate, while that of the medium-value and high-value grades were strongly impacted by anthropogenic heat flux. The diurnal contrast of grades and coupling effects with heat wave were further discussed. This paper aimed to provide information on grades and controls of SUHI for further mitigation proposal. |
| first_indexed | 2024-03-11T18:23:21Z |
| format | Article |
| id | doaj.art-b43440d3ab424861a471fc1e2051a0e2 |
| institution | Directory Open Access Journal |
| issn | 0160-4120 |
| language | English |
| last_indexed | 2024-03-11T18:23:21Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Environment International |
| spelling | doaj.art-b43440d3ab424861a471fc1e2051a0e22023-10-15T04:36:25ZengElsevierEnvironment International0160-41202023-10-01180108196Grading surface urban heat island and investigating factor weight based on interpretable deep learning model across global citiesKangning Li0Yunhao Chen1Jinbao Jiang2College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, ChinaState Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; Corresponding author.College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, ChinaSignificant urbanization resulted in increasing surface urban heat island (SUHI) that caused negative impacts on urban ecological environment, and residential comfort. Accurately monitoring the spatiotemporal variations and understanding controls of SUHI were essential to propose effective mitigation measurements. However, SUHI grades across global cities remained unknown, which cloud greatly support for global mitigations. Additionally, quantitative evaluating factor weights for different SUHI indicators and grades worldwide remained further investigations. Therefore, this paper proposed SUHI grading based on agglomerative hierarchical clustering, and further quantified factor weights for different indicators and grades based on an interoperable machine learning named TabNet. There were three major findings. (1) Global cities were grouped into five grades, including SUCI (surface urban cool island), insignificant, low-value, medium-value, and high-value SUHI grades, indicating significant differences among different grades. SUHI grades showed significant climate-based variations, wherein the arid climate was dominated by the SUCI grade at daytime but the high-value grade at nighttime. (2) Vegetation difference was an important factor for daytime SUHII accounting for 27%. Daytime frequency of SUHI was controlled by vegetation difference, temperature, evaporation and nighttime light, accounting for 78%. The major factors for nighttime frequency were albedo differences and nighttime light, accounting for 45%. (3) Related factors contributed differently to various SUHI grades. The weight of △EVI for daytime SUHII gradually increased with grades, while it for daytime frequency and maximum duration of SUHI decreased with grades. The nighttime SUHII of the low-value grade was greatly affected by the background climate, while that of the medium-value and high-value grades were strongly impacted by anthropogenic heat flux. The diurnal contrast of grades and coupling effects with heat wave were further discussed. This paper aimed to provide information on grades and controls of SUHI for further mitigation proposal.http://www.sciencedirect.com/science/article/pii/S0160412023004695Surface urban heat islandCity gradesFactor weightDifferent indicatorsInterpretable deep learning modelGlobal cities |
| spellingShingle | Kangning Li Yunhao Chen Jinbao Jiang Grading surface urban heat island and investigating factor weight based on interpretable deep learning model across global cities Environment International Surface urban heat island City grades Factor weight Different indicators Interpretable deep learning model Global cities |
| title | Grading surface urban heat island and investigating factor weight based on interpretable deep learning model across global cities |
| title_full | Grading surface urban heat island and investigating factor weight based on interpretable deep learning model across global cities |
| title_fullStr | Grading surface urban heat island and investigating factor weight based on interpretable deep learning model across global cities |
| title_full_unstemmed | Grading surface urban heat island and investigating factor weight based on interpretable deep learning model across global cities |
| title_short | Grading surface urban heat island and investigating factor weight based on interpretable deep learning model across global cities |
| title_sort | grading surface urban heat island and investigating factor weight based on interpretable deep learning model across global cities |
| topic | Surface urban heat island City grades Factor weight Different indicators Interpretable deep learning model Global cities |
| url | http://www.sciencedirect.com/science/article/pii/S0160412023004695 |
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