Uncertainty Forecasting Model for Mountain Flood Based on Bayesian Deep Learning DOI Creative Commons
Songsong Wang, Ouguan Xu

IEEE Access, Год журнала: 2024, Номер 12, С. 47830 - 47841

Опубликована: Янв. 1, 2024

Due to the characteristics of strong suddenness, high harmfulness, and frequent occurrence mountain flood disasters in small watersheds, accuracy reliability forecasting are insufficient watersheds. This paper studies key theories technologies, that is uncertainty model based on hydrologic physical mechanism. We design Bayesian Deep Learning (DL) models, it suitable for transfer spatiotemporal factors caused by floods disaster probability. The models include Linear Long Short-Term Memory (LSTM) model, we hope achieve an acceptable balance between (uncertainty confidence coverage) (confidence interval width). Meanwhile, extract effective information from multi-source multi-dimensional hazard factors' big data. experiment shows differences DL have long-term probability ability at both, but LSTM superior terms reliability, computational consumption.

Язык: Английский

Spatio-temporal deep learning model for accurate streamflow prediction with multi-source data fusion DOI
Zhaocai Wang, Nannan Xu, Xiaoguang Bao

и другие.

Environmental Modelling & Software, Год журнала: 2024, Номер 178, С. 106091 - 106091

Опубликована: Май 28, 2024

Язык: Английский

Процитировано

42

An interpretable hybrid deep learning model for flood forecasting based on Transformer and LSTM DOI Creative Commons
Wenzhong Li,

Chengshuai Liu,

Yingying Xu

и другие.

Journal of Hydrology Regional Studies, Год журнала: 2024, Номер 54, С. 101873 - 101873

Опубликована: Июнь 27, 2024

Язык: Английский

Процитировано

22

Enhancing carbon price point-interval multi-step-ahead prediction using a hybrid framework of autoformer and extreme learning machine with multi-factors DOI

Baoli Wang,

Zhaocai Wang, Zhiyuan Yao

и другие.

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 126467 - 126467

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

8

Long-term streamflow forecasting in data-scarce regions: Insightful investigation for leveraging satellite-derived data, Informer architecture, and concurrent fine-tuning transfer learning DOI
Fatemeh Ghobadi, Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬,

Doosun Kang

и другие.

Journal of Hydrology, Год журнала: 2024, Номер 631, С. 130772 - 130772

Опубликована: Фев. 2, 2024

Язык: Английский

Процитировано

17

DTTR: Encoding and decoding monthly runoff prediction model based on deep temporal attention convolution and multimodal fusion DOI
Wenchuan Wang,

Wei-can Tian,

Xiao-xue Hu

и другие.

Journal of Hydrology, Год журнала: 2024, Номер 643, С. 131996 - 131996

Опубликована: Сен. 16, 2024

Язык: Английский

Процитировано

17

A novel additive regression model for streamflow forecasting in German rivers DOI Creative Commons
Francesco Granata, Fabio Di Nunno, Quoc Bao Pham

и другие.

Results in Engineering, Год журнала: 2024, Номер 22, С. 102104 - 102104

Опубликована: Апрель 10, 2024

Forecasting streamflows, essential for flood mitigation and the efficient management of water resources drinking, agriculture hydroelectric power generation, presents a formidable challenge in most real-world scenarios. In this study, two models, first based on Additive Regression Radial Basis Function Neural Networks (AR-RBF) second stacking with Pace Multilayer Perceptron Random Forest (MLP-RF-PR), were compared prediction short-term (1–3 days ahead) medium-term (7 daily streamflow rates three different rivers Germany: Elbe River at Wittenberge, Leine Herrenhausen, Saale Hof The lagged values rate, precipitation temperature considered modeling. Moreover, Bayesian Optimization (BO) algorithm was used to assess optimal number hyperparameters. Both models showed accurate predictions forecasting, R2 1-day ahead ranging from 0.939 0.998 AR-RBF 0.930 0.996 MLP-RF-PR, while MAPE ranged 2.02 % 8.99 2.14 9.68 when exogeneous variables included. As forecast horizon increased, reduction forecasting accuracy observed. However, both could still predict overall flow pattern, even 7-day-ahead predictions, 0.772 0.871 0.703 0.840 10.60 20.45 10.44 19.65 MLP-RF-PR. Overall, outcomes study suggest that MLP-RF-PR can be reliable tools short- rate prediction, requiring short parameters optimized, making them easy implement reducing calculation time required.

Язык: Английский

Процитировано

14

Advanced streamflow forecasting for Central European Rivers: The Cutting-Edge Kolmogorov-Arnold networks compared to Transformers DOI
Francesco Granata, Senlin Zhu, Fabio Di Nunno

и другие.

Journal of Hydrology, Год журнала: 2024, Номер unknown, С. 132175 - 132175

Опубликована: Окт. 1, 2024

Язык: Английский

Процитировано

13

River discharge prediction based multivariate climatological variables using hybridized long short-term memory with nature inspired algorithm DOI
Sandeep Samantaray, Abinash Sahoo, Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

и другие.

Journal of Hydrology, Год журнала: 2024, Номер unknown, С. 132453 - 132453

Опубликована: Дек. 1, 2024

Язык: Английский

Процитировано

13

In-depth simulation of rainfall–runoff relationships using machine learning methods DOI Creative Commons
Mehdi Fuladipanah,

Alireza Shahhosseini,

Namal Rathnayake

и другие.

Water Practice & Technology, Год журнала: 2024, Номер 19(6), С. 2442 - 2459

Опубликована: Июнь 1, 2024

ABSTRACT Measurement inaccuracies and the absence of precise parameters value in conceptual analytical models pose challenges simulating rainfall–runoff modeling (RRM). Accurate prediction water resources, especially scarcity conditions, plays a distinctive pivotal role decision-making within resource management. The significance machine learning (MLMs) has become pronounced addressing these issues. In this context, forthcoming research endeavors to model RRM utilizing four MLMs: Support Vector Machine, Gene Expression Programming (GEP), Multilayer Perceptron, Multivariate Adaptive Regression Splines (MARS). simulation was conducted Malwathu Oya watershed, employing dataset comprising 4,765 daily observations spanning from July 18, 2005, September 30, 2018, gathered rainfall stations, Kappachichiya hydrometric station. Of all input combinations, incorporating Qt−1, Qt−2, R̄t identified as optimal configuration among considered alternatives. models' performance assessed through root mean square error (RMSE), average (MAE), coefficient determination (R2), developed discrepancy ratio (DDR). GEP emerged superior choice, with corresponding index values (RMSE, MAE, R2, DDRmax) (43.028, 9.991, 0.909, 0.736) during training process (40.561, 10.565, 0.832, 1.038) testing process.

Язык: Английский

Процитировано

12

A state-of-the-art review of long short-term memory models with applications in hydrology and water resources DOI
Zhong-kai Feng, J. Zhang, Wen-jing Niu

и другие.

Applied Soft Computing, Год журнала: 2024, Номер unknown, С. 112352 - 112352

Опубликована: Окт. 1, 2024

Язык: Английский

Процитировано

12