Spatial assessment of utility-scale solar photovoltaic siting potential using machine learning approaches: A case study in Aichi prefecture, Japan

Applied Energy, Journal Year: 2025, Volume and Issue: 383, P. 125329 - 125329

Published: Jan. 16, 2025

Language: Английский

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From Data to Insights: Modeling Urban Land Surface Temperature Using Geospatial Analysis and Interpretable Machine Learning

Sensors, Journal Year: 2025, Volume and Issue: 25(4), P. 1169 - 1169

Published: Feb. 14, 2025

This study introduces an innovative machine learning method to model the spatial variation of land surface temperature (LST) with a focus on urban center Da Nang, Vietnam. Light Gradient Boosting Machine (LightGBM), support vector machine, random forest, and Deep Neural Network are employed establish functional relationships between LST its influencing factors. The approaches trained validated using remote sensing data from 2014, 2019, 2024. Various explanatory variables representing topographical characteristics, as well landscapes, used. Experimental results show that LightGBM outperforms other benchmark methods. In addition, Shapley Additive Explanations utilized clarify impact factors affecting LST. analysis outcomes indicate while importance these changes over time, density greenspace consistently emerge most influential attained R2 values 0.85, 0.92, 0.91 for years 2024, respectively. findings this work can be helpful deeper understanding heat stress dynamics facilitate planning.

Language: Английский

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Spatiotemporal Footprints of Surface Urban Heat Islands in the Urban Agglomeration of Yangtze River Delta During 2000–2022

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(5), P. 892 - 892

Published: March 3, 2025

Compared with atmospheric urban heat islands, surface islands (SUHIs) are easily monitored by the thermal sensors on satellites and have a more stable spatial pattern resembling built-up lands across single cities, large metropolitans, agglomerations; hence, they gaining attention from scholars planners worldwide in search for reasonable patterns scales to guide future development. Traditional urban–rural dichotomies, being sensitive representative rural areas diurnal seasonal variations land temperature (LST), obtain inflated varying SUHI footprints of approximately 1.0–6.5 times size different satellite-retrieved LST datasets many cities metropolitan areas, which not conducive developing strategies mitigate SUHIs. Taking Yangtze River Delta agglomeration China as an example, we proposed improved structural similarity index quantify SUHIs multiple at annual interval. We identified gridded anomalies (LSTAs) related urbanization adopting random forest models climate, urbanization, geographical, biophysical, topographical parameters. Using LSTA cycle grid point relative reference terms average values, variances, shapes characterize SUHIs, cross-validated ~1.06–2.45 × 104 km2 smaller than clear transition zones between zone were obtained 2000–2022. Hence, can balance urbanization’s benefits adverse effects enhancing design. Considering that rapidly transformed into ratio extent increasing 0.43 0.62 during 2000–2022, should also take measures prevent rapid expansion high-density ISA density above 65%

Language: Английский

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Investigating the Quantitative Impact of the Vegetation Indices on the Urban Thermal Comfort Based on Machine Learning: A Case Study of the Qinhuai River Basin, China

Sustainable Cities and Society, Journal Year: 2025, Volume and Issue: unknown, P. 106357 - 106357

Published: April 1, 2025

Language: Английский

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Carbon saving potential of urban parks due to heat mitigation in Yangtze River Economic Belt

Journal of Cleaner Production, Journal Year: 2022, Volume and Issue: 385, P. 135713 - 135713

Published: Dec. 20, 2022

Language: Английский

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