Tropical cyclone track prediction model for multidimensional features and time differences series observation DOI Creative Commons
Peihao Yang, Guodong Ye

Alexandria Engineering Journal, Journal Year: 2024, Volume and Issue: 111, P. 432 - 445

Published: Oct. 30, 2024

Language: Английский

Comparison of strategies for multistep-ahead lake water level forecasting using deep learning models DOI
Gang Li, Zhangkang Shu,

Miaoli Lin

et al.

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 444, P. 141228 - 141228

Published: Feb. 13, 2024

Language: Английский

Citations

13
Improving Long-Term Flood Forecasting Accuracy Using Ensemble Deep Learning Models and an Attention Mechanism DOI
Marjan Kordani, Mohammad Reza Nikoo, Mahmood Fooladi

et al.

Journal of Hydrologic Engineering, Journal Year: 2024, Volume and Issue: 29(6)

Published: Sept. 14, 2024

Language: Английский

Citations

11
Optimizing complementary operation of mega cascade reservoirs for boosting hydropower sustainability DOI
Yuxin Zhu, Yanlai Zhou, Chong‐Yu Xu

et al.

Sustainable Energy Technologies and Assessments, Journal Year: 2024, Volume and Issue: 64, P. 103719 - 103719

Published: March 2, 2024

Language: Английский

Citations

6
Advancing real-time error correction of flood forecasting based on the hydrologic similarity theory and machine learning techniques DOI
Peng Shi,

Hongshi Wu,

Simin Qu

et al.

Environmental Research, Journal Year: 2024, Volume and Issue: 246, P. 118533 - 118533

Published: Feb. 26, 2024

Language: Английский

Citations

5
Enhancing physically-based flood forecasts through fusion of long short-term memory neural network with unscented Kalman filter DOI
Yuxuan Luo, Yanlai Zhou,

Hanbing Xu

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 641, P. 131819 - 131819

Published: Aug. 13, 2024

Language: Английский

Citations

4
Probabilistic runoff forecasting by integrating improved conceptual hydrological model with interpretable deep learning approach in a typical karst basin, Southwest China DOI
Shufeng Lai,

Chongxun Mo,

Xingbi Lei

et al.

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 132950 - 132950

Published: Feb. 1, 2025

Language: Английский

Citations

0
A New Custom Deep Learning Model Coupled with a Flood Index for Multi-Step-Ahead Flood Forecasting DOI Creative Commons
Jianming Shen, Moyuan Yang, Juan Zhang

et al.

Hydrology, Journal Year: 2025, Volume and Issue: 12(5), P. 104 - 104

Published: April 26, 2025

Accurate and prompt flood forecasting is essential for effective decision making in control to help minimize or prevent damage. We propose a new custom deep learning model, IF-CNN-GRU, multi-step-ahead that incorporates the index (IF) improve prediction accuracy. The model integrates convolutional neural networks (CNNs) gated recurrent (GRUs) analyze spatiotemporal characteristics of hydrological data, while using recursive network adjusts unit output at each moment based on index. IF-CNN-GRU was applied forecast floods with lead time 1–5 d Baihe station middle reaches Han River, China, accompanied by an in-depth investigation uncertainty. results showed incorporating IF improved precision up 20%. analysis uncertainty revealed contributions modeling factors, such as datasets, structures, their interactions, varied across periods. interaction factors contributed 17–36% uncertainty, contribution datasets increased period (32–53%) structure decreased (32–28%). experiment also demonstrated data samples play critical role improving accuracy, offering actionable insights reduce predictive providing scientific basis early warning systems water resource management.

Language: Английский

Citations

0
A hydrologic similarity-based parameters dynamic matching framework: Application to enhance the real-time flood forecasting DOI

Hongshi Wu,

Peng Shi, Simin Qu

et al.

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 907, P. 167767 - 167767

Published: Oct. 11, 2023

Language: Английский

Citations

8
Study on runoff forecasting and error correction driven by atmosphere–ocean-land dataset DOI
Xinyu Chang, Jun Guo, Yi Liu

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 263, P. 125744 - 125744

Published: Nov. 8, 2024

Language: Английский

Citations

2
Research on Runoff Prediction Based on Time2Vec-TCN-Transformer Driven by Multi-Source Data DOI Open Access
Yang Liu, Yize Wang, Xuemei Liu

et al.

Electronics, Journal Year: 2024, Volume and Issue: 13(14), P. 2681 - 2681

Published: July 9, 2024

Due to the frequent occurrence of extreme weather in recent years, accurate runoff prediction is crucial for rational planning and management water resources. Addressing high uncertainty multiple influencing factors prediction, this paper proposes a method driven by multi-source data. Based on multivariate observed data runoff, level, temperature, precipitation, Time2Vec-TCN-Transformer model proposed research compared with LSTM, TCN, TCN-Transformer models. The results show that outperforms other models metrics including MAE, RRMSE, MAPE, NSE, demonstrating higher accuracy reliability. By effectively combining Time2Vec, Transformer, improves MAPE forecasting 1–4 days future approximately 7% traditional LSTM 4% standalone TCN model, while maintaining NSE consistently between 0.9 1. This can better capture periodicity, long-term scale information, relationships among variables data, providing reliable predictive support flood resources management.

Language: Английский

Citations

1