Coupling and Comparison of Physical Mechanism and Machine Learning Models for Water Level Simulation in Plain River Network Area DOI Creative Commons
Xiaoqing Gao, Yunzhu Liu, Cheng Gao

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(24), P. 12008 - 12008

Published: Dec. 22, 2024

In this study, the JiaoGang Basin in Yangtze River Delta plains of river network area was research object. A basin water level simulation model constructed based on physical mechanism and Mike software, parameters were calibrated validated. Based dataset produced by model, three types ML models, Support Vector Machine (SVM), random forest (RF), gradient boosting decision tree (GBDT), constructed, trained, validated, compared with model. The results showed that met accuracy requirements at most stations. training validation periods, RF GBDT models had root mean square errors (RMSEs) all stations less than 0.25 Nash–Sutcliffe coefficient (NSE) greater 0.7. can simulate better. considerably outperform terms peak present time errors, fluctuations (RMSE NSE) are minor to those

Language: Английский

Improving urban flood prediction using LSTM-DeepLabv3+ and Bayesian optimization with spatiotemporal feature fusion DOI
Zuxiang Situ, Qi Wang, Shuai Teng

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 630, P. 130743 - 130743

Published: Jan. 26, 2024

Language: Английский

Citations

17

Stakeholder analysis in the application of cutting-edge digital visualisation technologies for urban flood risk management: A critical review DOI Creative Commons

Vahid Bakhtiari,

Farzad Piadeh, Albert Chen

et al.

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 236, P. 121426 - 121426

Published: Sept. 4, 2023

Cutting-edge flood visualisation technologies are becoming increasingly important in managing urban risks, particularly from the perspective of stakeholders who play a crucial role controlling and reducing risks associated with events. This review study provides comprehensive overview stakeholder analysis this context, highlighting gaps current research paving way for future investigations. For purpose, scientific literature critical conducted based on identified relevant works to map mutual context. categorises cutting-edge into four groups - virtual reality, augmented mixed digital twin explores their adoption engaging various across five key stages risk management: prevention, mitigation, preparation, response, recovery. Results show that existing has primarily concentrated support water utilities communication general public. However, there is noticeable gap regarding engagement such as policy-makers, researchers, insurance providers. Furthermore, highlights disparities involvement damage assessment studies, lack representation policy-makers researchers. Finally, introduces concept overlooked interconnected impacts they have, which received relatively little attention previous research.

Language: Английский

Citations

33

Flood risk assessment of urban metro system using random forest algorithm and triangular fuzzy number based analytical hierarchy process approach DOI

Xinjian Guan,

Fengjiao Yu,

Hongshi Xu

et al.

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 109, P. 105546 - 105546

Published: May 21, 2024

Language: Английский

Citations

14

Prediction of flood risk levels of urban flooded points though using machine learning with unbalanced data DOI
Hongfa Wang,

Yu Meng,

Hongshi Xu

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 630, P. 130742 - 130742

Published: Jan. 25, 2024

Language: Английский

Citations

9

Threshold and real-time initiation mechanism of urban flood emergency response under combined disaster scenarios DOI
Yihong Zhou, Zening Wu, Qiuhua Liang

et al.

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 108, P. 105512 - 105512

Published: May 8, 2024

Language: Английский

Citations

9

A Novel Multi-Scenario Mitigation Model for Rainstorm Flood Disasters DOI

Lei Wen,

Xiaoyi Miao, Ting Wang

et al.

International Journal of Disaster Risk Reduction, Journal Year: 2025, Volume and Issue: 119, P. 105321 - 105321

Published: Feb. 18, 2025

Language: Английский

Citations

1

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, Journal Year: 2024, Volume and Issue: 636, P. 131279 - 131279

Published: May 7, 2024

Language: Английский

Citations

6

Enhancing transparency in data-driven urban pluvial flood prediction using an explainable CNN model DOI

Weizhi Gao,

Yaoxing Liao,

Yuhong Chen

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: unknown, P. 132228 - 132228

Published: Oct. 1, 2024

Language: Английский

Citations

6

A framework for amplification flood risk assessment and threshold determination of combined rainfall and river level in an inland city DOI
Wanjie Xue, Zening Wu,

Hongshi Xu

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 640, P. 131725 - 131725

Published: July 28, 2024

Language: Английский

Citations

5

Integrating Machine Learning Models with Comprehensive Data Strategies and Optimization Techniques to Enhance Flood Prediction Accuracy: A Review DOI

Adisa Hammed Akinsoji,

Bashir Adelodun, Qudus Adeyi

et al.

Water Resources Management, Journal Year: 2024, Volume and Issue: 38(12), P. 4735 - 4761

Published: June 3, 2024

Language: Английский

Citations

4