Research on Runoff Management of Sponge Cities under Urban Expansion DOI Open Access
Hongliang Sun,

Shangkun Wu,

Qiyu Dong

и другие.

Water, Год журнала: 2024, Номер 16(15), С. 2103 - 2103

Опубликована: Июль 25, 2024

To integrate the sponge city concept into urban development, we propose an analytical approach for runoff volume control, considering expansion. Using Changchun City as a case study and historical land-use data, simulated prediction of City’s structure 2035 change with GeoSOS-FLUS platform. We calculated storage volumes Low Impact Development (LID) designs using 2019 land surface data. The objective is 80% control rate by 2035. Through Monte Carlo simulation sensitivity analysis, assessed impact various types on LID calculations. Findings show that industrial significantly influences volumes. This highlights need precise surveys properties composition in planning more accurate analysis City. results indicate based current data may not meet long-term goals due to increased impervious surfaces coefficients during urbanization.

Язык: Английский

Assessing the scale effect of urban vertical patterns on urban waterlogging: An empirical study in Shenzhen DOI

Yuqin Huang,

Jinyao Lin, Xiaoyu He

и другие.

Environmental Impact Assessment Review, Год журнала: 2024, Номер 106, С. 107486 - 107486

Опубликована: Март 8, 2024

Язык: Английский

Процитировано

41

A novel flood risk management approach based on future climate and land use change scenarios DOI
Huu Duy Nguyen, Quoc‐Huy Nguyen, Dinh Kha Dang

и другие.

The Science of The Total Environment, Год журнала: 2024, Номер 921, С. 171204 - 171204

Опубликована: Фев. 23, 2024

Язык: Английский

Процитировано

25

Effective or useless? Assessing the impact of park entrance addition policy on green space services from the 15-min city perspective DOI
Qinyu Cui, Lin Tan, Haoran Ma

и другие.

Journal of Cleaner Production, Год журнала: 2024, Номер 467, С. 142951 - 142951

Опубликована: Июнь 19, 2024

Язык: Английский

Процитировано

10

Attribution analysis of urban social resilience differences under rainstorm disaster impact: Insights from interpretable spatial machine learning framework DOI

Tianshun Gu,

Hongbo Zhao, Yue Li

и другие.

Sustainable Cities and Society, Год журнала: 2024, Номер unknown, С. 106029 - 106029

Опубликована: Дек. 1, 2024

Язык: Английский

Процитировано

7

Geographic heterogeneity of activation functions in urban real-time flood forecasting: Based on seasonal trend decomposition using Loess-Temporal Convolutional Network-Gated Recurrent Unit model DOI

Songhua Huan

Journal of Hydrology, Год журнала: 2024, Номер 636, С. 131279 - 131279

Опубликована: Май 7, 2024

Язык: Английский

Процитировано

6

Understanding the key factors and future trends of ecosystem service value to support the decision management in the cluster cities around the Yellow River floodplain area DOI Creative Commons
Hongbo Zhao,

Xiaoman Xu,

Junqing Tang

и другие.

Ecological Indicators, Год журнала: 2023, Номер 154, С. 110544 - 110544

Опубликована: Июнь 26, 2023

The ecosystem services value (ESV) is an important basis for measuring ecological environment quality and efficient management of ecosystems. Although there have been many studies devoted to the measurement ESV, research on key influencing factors ESV prediction future development scenarios still limited. This study coupled Deep Forest model Patch-generating Land Use Simulation (PLUS) identify simulated change trend under Shared Socioeconomic Pathways (SSPs). Taking cluster cities around Yellow River floodplain area as object, this quantitatively analyzed spatiotemporal evolution characteristics its from 2000 2020, identified affecting using model. results showed that: (1) overall upward with strong spatial heterogeneity; (2) were construction land ratio, distance railway, SHDI, etc.; (3) best pathway in 2025, 2030 2035 would be SSPs5, SSPs2 SSPs4 respectively. can provide theoretical support maximizing benefits area.

Язык: Английский

Процитировано

11

Investigating the influence of nonlinear spatial heterogeneity in urban flooding factors using geographic explainable artificial intelligence DOI
Entong Ke, Juchao Zhao, Yaolong Zhao

и другие.

Journal of Hydrology, Год журнала: 2024, Номер unknown, С. 132398 - 132398

Опубликована: Ноя. 1, 2024

Язык: Английский

Процитировано

4

Assessing the impact of urbanization on flood patterns in Varanasi, India using Google Earth Engine DOI Creative Commons

Vikas Yadav,

Ashutosh Kainthola,

Gaurav Kushwaha

и другие.

Deleted Journal, Год журнала: 2025, Номер 2(1)

Опубликована: Фев. 25, 2025

Язык: Английский

Процитировано

0

Exploring the dynamic impact of future land use changes on urban flood disasters: A case study in Zhengzhou City, China DOI Creative Commons

Yuanyuan Bai,

Shao Sun,

Yingjun Xu

и другие.

Geography and sustainability, Год журнала: 2025, Номер unknown, С. 100287 - 100287

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0

The application of integrating comprehensive evaluation and clustering algorithms weighted by maximal information coefficient for urban flood susceptibility DOI
Hongfa Wang, Yu Meng, Huiliang Wang

и другие.

Journal of Environmental Management, Год журнала: 2023, Номер 344, С. 118846 - 118846

Опубликована: Сен. 2, 2023

Язык: Английский

Процитировано

10