Bibliometrics-based Research Landscape of Artificial Intelligence in Flood Prediction DOI Open Access
Wenling Guan,

Haodong Cen

Journal of intelligence and knowledge engineering., Год журнала: 2024, Номер 2(1), С. 37 - None

Опубликована: Март 1, 2024

Climate change has caused an increasing threat of flood disasters, and using artificial intelligence methods to predict floods is now a hot subject in the field prediction. To find out current situation prediction research based on methods, it essential summarise main focus direction at present. 612 references forecasting AI were selected from Web Science Core Collection database. The collected articles analysed visually CiteSpace VOSviewer. results study indicate that overall trend publications AI-based studies increasing. In particular, China, United States India are contributors this area. analysis collaborating institutions shows Chinese have high activity field. keywords term show mainly focuses three aspects, which risk assessment, hydrological information simulation, integration improvement algorithms. recent years, (AI) algorithms became as new focal point research. incorporation multiple machine learning or deep well additional improve quality models received attention future, important for efforts explore these avenues further, order strengthen China's scientific efficient response capabilities face disasters. This study's can be reference researchers understand landscape emerging frontiers AI-driven It will help guide future directions strategies promote continued development

Язык: Английский

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

и другие.

Expert Systems with Applications, Год журнала: 2023, Номер 236, С. 121426 - 121426

Опубликована: Сен. 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.

Язык: Английский

Процитировано

48

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

и другие.

Journal of Hydrology, Год журнала: 2024, Номер 630, С. 130743 - 130743

Опубликована: Янв. 26, 2024

Язык: Английский

Процитировано

17

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

и другие.

Sustainable Cities and Society, Год журнала: 2024, Номер 109, С. 105546 - 105546

Опубликована: Май 21, 2024

Язык: Английский

Процитировано

16

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

Yu Meng,

Hongshi Xu

и другие.

Journal of Hydrology, Год журнала: 2024, Номер 630, С. 130742 - 130742

Опубликована: Янв. 25, 2024

Язык: Английский

Процитировано

9

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

и другие.

Sustainable Cities and Society, Год журнала: 2024, Номер 108, С. 105512 - 105512

Опубликована: Май 8, 2024

Язык: Английский

Процитировано

9

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

Lei Wen,

Xiaoyi Miao, Ting Wang

и другие.

International Journal of Disaster Risk Reduction, Год журнала: 2025, Номер 119, С. 105321 - 105321

Опубликована: Фев. 18, 2025

Язык: Английский

Процитировано

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, Год журнала: 2024, Номер 636, С. 131279 - 131279

Опубликована: Май 7, 2024

Язык: Английский

Процитировано

7

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

Weizhi Gao,

Yaoxing Liao,

Yuhong Chen

и другие.

Journal of Hydrology, Год журнала: 2024, Номер unknown, С. 132228 - 132228

Опубликована: Окт. 1, 2024

Язык: Английский

Процитировано

7

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

и другие.

Journal of Hydrology, Год журнала: 2024, Номер 640, С. 131725 - 131725

Опубликована: Июль 28, 2024

Язык: Английский

Процитировано

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

и другие.

Water Resources Management, Год журнала: 2024, Номер 38(12), С. 4735 - 4761

Опубликована: Июнь 3, 2024

Язык: Английский

Процитировано

4