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

Load Forecasting Techniques for Power System: Research Challenges and Survey DOI Creative Commons

Naqash Ahmad,

Yazeed Yasin Ghadi,

Muhammad Adnan

et al.

IEEE Access, Journal Year: 2022, Volume and Issue: 10, P. 71054 - 71090

Published: Jan. 1, 2022

The main and pivot part of electric companies is the load forecasting. Decision-makers think tank power sectors should forecast future need electricity with large accuracy small error to give uninterrupted free shedding consumers. demand can be forecasted amicably by many Machine Learning (ML), Deep (DL) Artificial Intelligence (AI) techniques among which hybrid methods are most popular. present technologies forecasting work regarding combination various ML, DL AI algorithms reviewed in this paper. comprehensive review single models functions; advantages disadvantages discussed comparison between performance terms Mean Absolute Error (MAE), Root Squared (RMSE), Percentage (MAPE) values compared literature different support researchers select best model for prediction. This validates fact that will provide a more optimal solution.

Language: Английский

Citations

138
Application of Machine Learning in Water Resources Management: A Systematic Literature Review DOI Open Access
Fatemeh Ghobadi,

Doosun Kang

Water, Journal Year: 2023, Volume and Issue: 15(4), P. 620 - 620

Published: Feb. 5, 2023

In accordance with the rapid proliferation of machine learning (ML) and data management, ML applications have evolved to encompass all engineering disciplines. Owing importance world’s water supply throughout rest this century, much research has been concentrated on application strategies integrated resources management (WRM). Thus, a thorough well-organized review that is required. To accommodate underlying knowledge interests both artificial intelligence (AI) unresolved issues in WRM, overview divides core fundamentals, major applications, ongoing into two sections. First, basic are categorized three main groups, prediction, clustering, reinforcement learning. Moreover, literature organized each field according new perspectives, patterns indicated so attention can be directed toward where headed. second part, less investigated WRM addressed provide grounds for future studies. The widespread tools projected accelerate formation sustainable plans over next decade.

Language: Английский

Citations

75
Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research directions DOI
Tao Hai, Sani I. Abba, Ahmed M. Al‐Areeq

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 129, P. 107559 - 107559

Published: Dec. 3, 2023

Language: Английский

Citations

61
Ensemble learning using multivariate variational mode decomposition based on the Transformer for multi-step-ahead streamflow forecasting DOI

Jinjie Fang,

Linshan Yang,

Xiaohu Wen

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 636, P. 131275 - 131275

Published: May 7, 2024

Language: Английский

Citations

17
Improving long-term streamflow prediction in a poorly gauged basin using geo-spatiotemporal mesoscale data and attention-based deep learning: A comparative study DOI
Fatemeh Ghobadi,

Doosun Kang

Journal of Hydrology, Journal Year: 2022, Volume and Issue: 615, P. 128608 - 128608

Published: Nov. 3, 2022

Language: Английский

Citations

48
Forecasting vapor pressure deficit for agricultural water management using machine learning in semi-arid environments DOI Creative Commons
Ahmed Elbeltagi, Aman Srivastava,

Jinsong Deng

et al.

Agricultural Water Management, Journal Year: 2023, Volume and Issue: 283, P. 108302 - 108302

Published: April 14, 2023

Precise evapotranspiration (ET) estimation is critical for agricultural water management, particularly in water-stressed developing countries. Vapor Pressure Deficit one of the ET parameters that has a significant impact on its calculation (VPD). This paper forecasts VPD using ensemble learning-based modeling eight different regions (Dakahliyah, Gharbiyah, Kafr Elsheikh, Dumyat, Port Said, Ismailia, Sharqiyah, and Qalubiyah) Egypt. In this study, six machine learning algorithms were used: Linear Regression (LR), Additive regression trees (ART), Random SubSpace (RSS), Forest (RF), Reduced Error Pruning Tree (REPTree), Quinlan's M5 algorithm (M5P). Monthly vapor pressure data obtained from Japanese 55-year Reanalysis JRA-55 1958 to 2021. The dateset been divided into two segments: training stage (1958–2005) testing (2006–2021). Five statistical measures used evaluate model performances: Correlation Coefficient (CC), Mean Absolute (MAE), Root Square (RMSE), Relative absolute error (RAE), Squared (RRSE), across both stages. RF outperformed rest models [CC = 0.9694; MAE 0.0967; RMSE 0.1252; RAE (%) 21.7297 RRSE 24.0356], followed closely by REPTree RSS models. On other hand, M5P performance remained moderate LR AR worst. During stage, terms (which statistic), study recommended future hydro-climatological studies general, deficit prediction particular. enables magnitudes be predicted, alerting authorities administrators involved focus their policy-making more specific pathways toward climate adaptation.

Language: Английский

Citations

38
New-generation machine learning models as prediction tools for modeling interfacial tension of hydrogen-brine system DOI
Afeez Gbadamosi, Haruna Adamu, Jamilu Usman

et al.

International Journal of Hydrogen Energy, Journal Year: 2023, Volume and Issue: 50, P. 1326 - 1337

Published: Oct. 4, 2023

Language: Английский

Citations

27
Forecasting actual evapotranspiration without climate data based on stacked integration of DNN and meta-heuristic models across China from 1958 to 2021 DOI
Ahmed Elbeltagi, Aman Srivastava, Penghan Li

et al.

Journal of Environmental Management, Journal Year: 2023, Volume and Issue: 345, P. 118697 - 118697

Published: Sept. 7, 2023

Language: Английский

Citations

24
A Comprehensive Survey on Load Forecasting Hybrid Models: Navigating the Futuristic Demand Response Patterns through Experts and Intelligent Systems DOI Creative Commons

Kinza Fida,

Usman Abbasi,

Muhammad Adnan

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: 23, P. 102773 - 102773

Published: Aug. 24, 2024

Language: Английский

Citations

9
Evaluating machine learning models comprehensively for predicting maximum power from photovoltaic systems DOI Creative Commons
Samir A. Hamad,

Mohamed A. Ghalib,

Amr Munshi

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 28, 2025

This paper presents a machine learning (ML) model designed to track the maximum power point of standalone Photovoltaic (PV) systems. Due nonlinear nature generation in PV systems, influenced by fluctuating weather conditions, managing this data effectively remains challenge. As result, use ML techniques optimize systems at their MPP is highly beneficial. To achieve this, research explores various algorithms, such as Linear Regression (LR), Ridge (RR), Lasso (Lasso R), Bayesian (BR), Decision Tree (DTR), Gradient Boosting (GBR), and Artificial Neural Networks (ANN), predict The utilizes from unit's technical specifications, allowing algorithms forecast power, current, voltage based on given irradiance temperature inputs. Predicted also used determine boost converter's duty cycle. simulation was conducted 100 kW solar panel with an open-circuit 64.2 V short-circuit current 5.96 A. Model performance evaluated using metrics Root Mean Square Error (RMSE), Coefficient Determination (R2), Absolute (MAE). Additionally, study assessed correlation feature importance evaluate compatibility factors impacting predictive accuracy models. Results showed that DTR algorithm outperformed others like LR, RR, R, BR, GBR, ANN predicting (Im), (Vm), (Pm) system. achieved RMSE, MAE, R2 values 0.006, 0.004, 0.99999 for Im, 0.015, 0.0036, Vm, 2.36, 0.871, Pm. Factors size training dataset, operating conditions system, type, preprocessing were found significantly influence prediction accuracy.

Language: Английский

Citations

1