Investigating socio-ecological vulnerability to climate change via remote sensing and a data-driven ranking algorithm DOI
Harrison Odion Ikhumhen, Qinhua Fang, Shanlong Lu

и другие.

Journal of Environmental Management, Год журнала: 2023, Номер 347, С. 119254 - 119254

Опубликована: Окт. 6, 2023

Язык: Английский

Assessment of urban flood susceptibility based on a novel integrated machine learning method DOI
Haidong Yang, Ting Zou, Biyu Liu

и другие.

Environmental Monitoring and Assessment, Год журнала: 2024, Номер 197(1)

Опубликована: Дек. 5, 2024

Язык: Английский

Процитировано

1

Projecting Socio-Economic Exposure due to Future Hydro-Meteorological Extremes in Large Transboundary River Basin under Global Warming Targets DOI
Rishi Gupta, Vinay Chembolu

Water Resources Management, Год журнала: 2024, Номер unknown

Опубликована: Дек. 14, 2024

Язык: Английский

Процитировано

1

Study on the Spatiotemporal Evolution of Habitat Quality in Highly Urbanized Areas Based on Bayesian Networks: A Case Study from Shenzhen, China DOI Open Access
Wei Zhang, Xiaodong Lü,

Zhuangxiu Xie

и другие.

Sustainability, Год журнала: 2024, Номер 16(24), С. 10993 - 10993

Опубликована: Дек. 15, 2024

Rapid urbanization presents significant challenges to biodiversity through habitat degradation, fragmentation, and loss. This study focuses on Shenzhen, China, a highly urbanized region experiencing substantial land use changes facing considerable risk of decline, investigate the dynamics quality over two critical periods: 2010–2015 2015–2020. Using InVEST (Integrated Valuation Ecosystem Services Trade-offs) model for assessment Bayesian networks analyze causal relationships, this research offers an innovative comparison between recovery degradation across these phases. Results indicate that from 2010 2015, localized was achieved 0.53% area due restoration policies, yet overall trend remained negative. During 2015–2020 period, intensified (7.19%) compared (5.7%); notably, 70.6% areas had been previously restored are now once again. re-degradation highlights instability earlier efforts under ongoing urban pressure. By integrating spatial analysis with network modeling, provides nuanced understanding where why initial were unsuccessful, identifying susceptible persistent degradation. The emphasizes expansion—particularly development construction land, primary driver while ecological sensitivity played crucial role in determining long-term success efforts. approach valuable insights designing more effective, sustainable conservation strategies rapidly urbanizing regions.

Язык: Английский

Процитировано

1

Algorithm for community security risk assessment and influencing factors analysis by back propagation neural network DOI Creative Commons
Shuang Zhou,

Meiling Du,

Xiaoyu Liu

и другие.

Heliyon, Год журнала: 2024, Номер 10(9), С. e30185 - e30185

Опубликована: Апрель 24, 2024

Язык: Английский

Процитировано

1

Investigating socio-ecological vulnerability to climate change via remote sensing and a data-driven ranking algorithm DOI
Harrison Odion Ikhumhen, Qinhua Fang, Shanlong Lu

и другие.

Journal of Environmental Management, Год журнала: 2023, Номер 347, С. 119254 - 119254

Опубликована: Окт. 6, 2023

Язык: Английский

Процитировано

3