Multi‐step‐ahead flood forecasting using an improved BiLSTM‐S2S model-Reference-Cited by-同舟云学术

Multi‐step‐ahead flood forecasting using an improved BiLSTM‐S2S model

Published:2022-06-14 Issue:4 Volume:15 Page:
ISSN:1753-318X
Container-title:Journal of Flood Risk Management
language:en
Short-container-title:J Flood Risk Management

Author:

Cao Qing¹²³^ORCID,Zhang Hanchen⁴⁵⁶,Zhu Feilin³,Hao Zhenchun³,Yuan Feifei³

Affiliation:

1. Key Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resources/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science and Technology Nanjing China

2. School of Hydrology and Water Resources Nanjing University of Information Science and Technology Nanjing Jiangsu China

3. State Key Laboratory of Hydrology Water Resources and Hydraulic Engineering Hohai University Nanjing China

4. Institute of Environmental Engineering Ningxia University Yinchuan China

5. Ningxia Key Laboratory of Resource Assessment and Environment Regulation in Arid Region Yinchuan China

6. China‐Arab Joint International Research Laboratory for Featured Resources and Environmental Governance in Arid Region Yinchuan China

Publisher

Wiley

Subject

Water Science and Technology,Safety, Risk, Reliability and Quality,Geography, Planning and Development,Environmental Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/jfr3.12827

Reference46 articles.

1. Abadi M. Agarwal A. Barham P. Brevdo E. Chen Z. Citro C. Corrado G.S. Davis A. Dean J. Devin M. 2016.Tensorflow: Large‐scale machine learning on heterogeneous distributed systems. ArXiv preprint arXiv:1603.04467.

2. Neural Network Modelling

3. Adikari Y. Yoshitani J. 2009.Global trends in water‐related disasters: An insight for policymakers. World Water Assessment Programme Side Publication Series Insights. The United Nations UNESCO. International Centre for Water Hazard and Risk Management (ICHARM).

4. SWAT: Model Use, Calibration, and Validation

5. End-to-End ASR-Free Keyword Search From Speech

Cited by 12 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Deep Learning Models for Small Rivers Stream-Flow Forecasting and Flood Prediction;2024-08-13

2. A novel method of nonuniform phase space reconstruction for multivariate prediction of daily runoff;Journal of Hydrology;2024-07

3. A novel strategy for flood flow Prediction: Integrating Spatio-Temporal information through a Two-Dimensional hidden layer structure;Journal of Hydrology;2024-07

4. Multimodal deep learning water level forecasting model for multiscale drought alert in Feiyun River basin;Expert Systems with Applications;2024-06

5. Multi-lead-time short-term runoff forecasting based on Ensemble Attention Temporal Convolutional Network;Expert Systems with Applications;2024-06