A novel model for the dynamics and evaporation of water droplets with deformation considerations DOI

Xiaowang Zhao,

Yulong Li, Han Zhang

et al.

International Journal of Thermal Sciences, Journal Year: 2024, Volume and Issue: 210, P. 109555 - 109555

Published: Nov. 20, 2024

Language: Английский

Assessing Wildfire Susceptibility in Iran: Leveraging Machine Learning for Geospatial Analysis of Climatic and Anthropogenic Factors DOI Creative Commons

Ehsan Masoudian,

Ali Mirzaei, Hossein Bagheri

et al.

Trees Forests and People, Journal Year: 2025, Volume and Issue: unknown, P. 100774 - 100774

Published: Jan. 1, 2025

Language: Английский

Citations

1
Forest fire risk assessment model optimized by stochastic average gradient descent DOI Creative Commons
Zexin Fu, Adu Gong, Jia Wan

et al.

Ecological Indicators, Journal Year: 2025, Volume and Issue: 170, P. 113006 - 113006

Published: Jan. 1, 2025

Language: Английский

Citations

1
Mapping the probability of forest fire hazard across the European Alps under climate change scenarios DOI Creative Commons

Kilian Gerberding,

Uta Schirpke

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 377, P. 124600 - 124600

Published: Feb. 22, 2025

Forest fires are increasing in frequency and intensity worldwide due to the anthropogenic climate change, threatening people's lives causing huge economic environmental damages. Recent forest fire events suggest that also an urgent issue European Alps, but studies assessing hazard under future scenarios still rare. Thus, this study aims analyse impacts of change on probability across Alps surrounding areas. In specific, we (1) explain current based a set parameters, (2) map conditions area using geographically weighted regression. Our results mainly depends lightning strikes, annual mean temperature, precipitation seasonality. Overall, our indicate increase hazard, which is already significant SSP126 (+15.5%), while highest increases occur SSP370 (30.6%) SSP585 (35.4%). However, less pronounced fire-prone regions southwestern France, will greatly Northern Eastern regions. findings emphasize need address these climate-related challenges by decision-making management through fire-smart management. Nevertheless, further efforts needed overcome limitations related data availability uncertainties scenarios.

Language: Английский

Citations

1
PyLST: a remote sensing application for retrieving land surface temperature (LST) from Landsat data DOI
Zahra Parvar, Abdolrassoul Salmanmahiny

Environmental Earth Sciences, Journal Year: 2024, Volume and Issue: 83(12)

Published: May 31, 2024

Language: Английский

Citations

7
Unveiling the thermal impact of land cover transformations in Khuzestan province through MODIS satellite remote sensing products DOI

Iraj Baronian,

Reza Borna,

Kamran Jafarpour Ghalehteimouri

et al.

Paddy and Water Environment, Journal Year: 2024, Volume and Issue: 22(4), P. 503 - 520

Published: June 5, 2024

Language: Английский

Citations

5
Enhancing forest fire susceptibility mapping in Xichang City, China using DBSCAN-based non-fire point selection integrated with deep neural network DOI Creative Commons
Lingxiao Xie, Rui Zhang, Jichao Lv

et al.

Geomatics Natural Hazards and Risk, Journal Year: 2025, Volume and Issue: 16(1)

Published: Jan. 2, 2025

Forest fire susceptibility mapping plays a crucial role in forest management and disaster prevention. However, existing research often neglects the selection of non-fire data during model construction, resulting limited prediction accuracy. To address this issue, we propose an innovative DBSCAN-DNN that optimizes to enhance precision. Using VIIRS GLC_FCS30D datasets, created spatial database for Xichang's dry seasons from 2012 2022, incorporating topography, meteorology, vegetation, human activities. Based on this, employed DBSCAN algorithm cluster points accurately delineated affected areas. Subsequently, selected samples outside these regions training DNN model. Through comparative experiments, found exhibited excellent performance predicting Xichang City, with AUC value 0.925 significant improvements accuracy (0.834), precision (0.800), recall (0.891), F1-score (0.843), Kappa coefficient (0.669). Additionally, conducted SHAP analysis delve into contributions interactions various factors influencing susceptibility. This finding offers valuable insights selecting sample

Language: Английский

Citations

0
U3UNet: An accurate and reliable segmentation model for forest fire monitoring based on UAV vision DOI
Hailin Feng, Jiefan Qiu, Long Wen

et al.

Neural Networks, Journal Year: 2025, Volume and Issue: 185, P. 107207 - 107207

Published: Jan. 30, 2025

Language: Английский

Citations

0
Integrating spatial analysis of land surface temperature and green space suitability: an advanced approach to urban and peri-urban planning DOI
Zahra Parvar, Marjan Mohammadzadeh, Sepideh Saeidi

et al.

GeoJournal, Journal Year: 2025, Volume and Issue: 90(2)

Published: March 18, 2025

Language: Английский

Citations

0
Assessing and predicting habitat quality under urbanization and climate pressures DOI
Zahra Parvar, Abdolrassoul Salmanmahiny

Journal for Nature Conservation, Journal Year: 2025, Volume and Issue: unknown, P. 126903 - 126903

Published: March 1, 2025

Language: Английский

Citations

0
Assessing Hyrcanian forest fire vulnerability: socioeconomic and environmental perspectives DOI Creative Commons

Elnaz Nejatiyanpour,

Omid Ghorbanzadeh, Josef Strobl

et al.

Journal of Forestry Research, Journal Year: 2025, Volume and Issue: 36(1)

Published: Feb. 22, 2025

Language: Английский

Citations

0