A Hybrid Model of Ensemble Empirical Mode Decomposition and Sparrow Search Algorithm-Based Long Short-Term Memory Neural Networks for Monthly Runoff Forecasting DOI Creative Commons

Baojian Li,

Jingxin Yang, Qingyuan Luo

et al.

Frontiers in Environmental Science, Journal Year: 2022, Volume and Issue: 10

Published: July 19, 2022

Monthly runoff forecasting plays a vital role in reservoir ecological operation, which can reduce the negative impact of dam construction and operation on river ecosystem. Numerous studies have been conducted to improve monthly forecast accuracy, machine learning methods paid much attention due their unique advantages. In this study, conjunction model, EEMD-SSA-LSTM for short, comprises ensemble empirical mode decomposition (EEMD) sparrow search algorithm (SSA)–based long short-term neural networks (LSTM), has proposed forecasting. The model is mainly carried out three steps. First, original time series data decomposed into several sub-sequences. Second, each sub-sequence simulated by LSTM, hyperparameters are optimized SSA. Finally, results summarized as final results. obtained from two reservoirs located China used validate performance. Meanwhile, four commonly statistical evaluation indexes utilized evaluate demonstrate that compared benchmark models, yield satisfactory be conducive improving accuracy.

Language: Английский

Using Adaptive Chaotic Grey Wolf Optimization for the daily streamflow prediction DOI
Jing Liang, Yukun Du, Yi‐Peng Xu

et al.

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 237, P. 121113 - 121113

Published: Aug. 10, 2023

Language: Английский

Citations

15
DTTM: A deep temporal transfer model for ultra-short-term online wind power forecasting DOI
Mingwei Zhong,

Cancheng Xu,

Zikang Xian

et al.

Energy, Journal Year: 2023, Volume and Issue: 286, P. 129588 - 129588

Published: Nov. 6, 2023

Language: Английский

Citations

14
Improving multi-month hydrological drought forecasting in a tropical region using hybridized extreme learning machine model with Beluga Whale Optimization algorithm DOI
Mohammed Majeed Hameed, Siti Fatin Mohd Razali, Wan Hanna Melini Wan Mohtar

et al.

Stochastic Environmental Research and Risk Assessment, Journal Year: 2023, Volume and Issue: 37(12), P. 4963 - 4989

Published: Sept. 9, 2023

Language: Английский

Citations

13
Review of Recent Trends in the Hybridisation of Preprocessing-Based and Parameter Optimisation-Based Hybrid Models to Forecast Univariate Streamflow DOI Open Access

Baydaa Abdul Kareem,

Salah L. Zubaidi, Nadhir Al‐Ansari

et al.

Computer Modeling in Engineering & Sciences, Journal Year: 2023, Volume and Issue: 138(1), P. 1 - 41

Published: Sept. 19, 2023

Forecasting river flow is crucial for optimal planning, management, and sustainability using freshwater resources. Many machine learning (ML) approaches have been enhanced to improve streamflow prediction. Hybrid techniques viewed as a viable method enhancing the accuracy of univariate estimation when compared standalone approaches. Current researchers also emphasised hybrid models forecast accuracy. Accordingly, this paper conducts an updated literature review applications in estimating over last five years, summarising data preprocessing, modelling strategy, advantages disadvantages ML techniques, models, performance metrics. This study focuses on two types models: parameter optimisation-based (OBH) hybridisation preprocessing-based (HOPH). Overall, research supports idea that meta-heuristic precisely techniques. It's one first efforts comprehensively examine efficiency various (classified into four primary classes) hybridised with revealed previous applied swarm, evolutionary, physics, metaheuristics 77%, 61%, 12%, respectively. Finally, there still room improving OBH HOPH by examining different pre-processing metaheuristic algorithms.

Language: Английский

Citations

11
An evaluation of random forest based input variable selection methods for one month ahead streamflow forecasting DOI Creative Commons
Wei Fang, Kun Ren, Tie-Jun Liu

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Nov. 30, 2024

In the development of data-driven models for streamflow forecasting, choosing appropriate input variables is crucial. Although random forest (RF) has been successfully applied to forecasting variable selection (IVS), comparative analysis different forest-based IVS (RF-IVS) methods yet absent. Here, we investigate performance five RF-IVS in four (RF, support vector regression (SVR), Gaussian process (GP), and long short-term memory (LSTM)). A case study implemented contiguous United States one-month-ahead forecasting. Results indicate that enable acquire enhanced comparison widely used partial Pearson correlation conditional mutual information. Meanwhile, performance-based appear be superior test-based methods, tend select redundant variables. The RF with a forward strategy finally recommended connect GP model as promising combination having potential yield favorable performance.

Language: Английский

Citations

4
Bayesian-optimized recursive machine learning for predicting human-induced changes in suspended sediment transport DOI

Soumya Kundu,

Somil Swarnkar,

Akshay Agarwal

et al.

Environmental Monitoring and Assessment, Journal Year: 2025, Volume and Issue: 197(5)

Published: April 26, 2025

Language: Английский

Citations

0
Monthly River Discharge Forecasting Using Hybrid Models Based on Extreme Gradient Boosting Coupled with Wavelet Theory and Lévy–Jaya Optimization Algorithm DOI
Jincheng Zhou, Dan Wang, Shahab S. Band

et al.

Water Resources Management, Journal Year: 2023, Volume and Issue: 37(10), P. 3953 - 3972

Published: June 9, 2023

Language: Английский

Citations

9
Deep neural network-based discharge prediction for upstream hydrological stations: a comparative study DOI
Xuan-Hien Le, Duc Hai Nguyen, Sungho Jung

et al.

Earth Science Informatics, Journal Year: 2023, Volume and Issue: 16(4), P. 3113 - 3124

Published: Aug. 21, 2023

Language: Английский

Citations

9
Towards an efficient streamflow forecasting method for event-scales in Ca River basin, Vietnam DOI Creative Commons
Xuan-Hien Le, Linh Nguyen Van, Giang V. Nguyen

et al.

Journal of Hydrology Regional Studies, Journal Year: 2023, Volume and Issue: 46, P. 101328 - 101328

Published: Feb. 1, 2023

The Ca River basin is located in the North Central Coast area of Vietnam This study aims to develop a deep learning framework that both effective and straightforward order forecast water levels advance multiple time steps for event scales. We have thoroughly studied assessed two models (DLMs), long-short term memory (LSTM) gated recurrent unit (GRU), their capacity levels, focusing on various aspects such as influence sequence length or impact hyperparameter selection. Besides, data scenarios were established using hydrological from eight severe floods between 2007 2019 examine effect input variables model performance. Water level was employed (S1 S2), whereas precipitation used only S2. cross-validation technique dynamically address issue limited data. inputs reformatted tensors then randomly divided into subsets. flexible tuning preserved sequential nature while enabling DLMs be trained efficiently. findings revealed exhibited equally excellent performances. NSE LSTM varies 0.999–0.971 compared 0.998–0.974 GRU model, corresponding cases one four-time ahead. indicated use multiple-input types (S2) contrary date type (S1) does not necessarily improve forecasting LSTM/GRU with hidden layer are adequate delivering high performance minimizing processing time.

Language: Английский

Citations

8
Multi-step-ahead water level forecasting for operating sluice gates in Hai Duong, Vietnam DOI

Hung Viet Ho,

Duc Hai Nguyen, Xuan-Hien Le

et al.

Environmental Monitoring and Assessment, Journal Year: 2022, Volume and Issue: 194(6)

Published: May 21, 2022

Language: Английский

Citations

13