An enhanced monthly runoff time series prediction using extreme learning machine optimized by salp swarm algorithm based on time varying filtering based empirical mode decomposition

Wang, Wenchuan; Cheng, Qi; Chau, Kwok-wing; Hu, Hao; Zang, Hongfei; Xu, Dongmei

doi:10.1016/j.jhydrol.2023.129460

Cited by 22 publications

(5 citation statements)

References 90 publications

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“…Another problem that can benefit from reliable forecasts is the monthly runoff prediction in hydropower plants. For this purpose, for the enhancement of the accuracy of the predictions, hybrid prediction models can be formulated through the combination of empirical mode decomposition (EMD), time varying filtering (TVF), extreme learning machines (ELM), and salp swarm algorithm (SSA) [150]. ELM algorithms can be integrated with the Monte Carlo method in order to provide reliable predictions of hydropower production and energy saving [151].…”

Section: Forecastmentioning

confidence: 99%

Digitalization, Industry 4.0, Data, KPIs, Modelization and Forecast for Energy Production in Hydroelectric Power Plants: A Review

Pepe,

Zanoli

2024

Energies

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Intelligent water usage is required in order to target the challenging goals for 2030 and 2050. Hydroelectric power plants represent processes wherein water is exploited as a renewable resource and a source for energy production. Hydroelectric power plants usually include reservoirs, valves, gates, and energy production devices, e.g., turbines. In this context, monitoring and maintenance policies together with control and optimization strategies, at the different levels of the automation hierarchy, may represent strategic tools and drivers for energy efficiency improvement. Nowadays, these strategies rely on different basic concepts and elements, which must be assessed and investigated in order to provide a reliable background. This paper focuses on a review of the state of the art associated with these basic concepts and elements, i.e., digitalization, Industry 4.0, data, KPIs, modelization, and forecast.

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Section: Forecastmentioning

confidence: 99%

Digitalization, Industry 4.0, Data, KPIs, Modelization and Forecast for Energy Production in Hydroelectric Power Plants: A Review

Pepe,

Zanoli

2024

Energies

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“…Both basic and hybrid forms of LSTM have found extensive application in runoff simulation due to their ability to accurately capture underlying patterns in time series data [6]. Recent studies have indicated that gated recurrent units (GRU), a variant of LSTM, achieve comparable performance with a simpler structure and lower computational burden compared to LSTM, extreme learning machines [7], and support vector machines (SVM) in monthly runoff prediction [8].…”

Section: Introductionmentioning

confidence: 99%

“…Wavelet transform (WT), empirical mode decomposition (EMD) [9], and variational mode decomposition (VMD) [10,11] are commonly employed to identify and separate fluctuation components (referred to as noise) in time series data to achieve satisfactory simulations [7,12,13]. WT serves as an effective data processing technique to enhance the efficiency of any network by reducing noise in model input data.…”

Section: Introductionmentioning

confidence: 99%

Monthly Runoff Prediction for Xijiang River via Gated Recurrent Unit, Discrete Wavelet Transform, and Variational Modal Decomposition

Yang,

Li,

Liu

2024

Water

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Neural networks have become widely employed in streamflow forecasting due to their ability to capture complex hydrological processes and provide accurate predictions. In this study, we propose a framework for monthly runoff prediction using antecedent monthly runoff, water level, and precipitation. This framework integrates the discrete wavelet transform (DWT) for denoising, variational modal decomposition (VMD) for sub-sequence extraction, and gated recurrent unit (GRU) networks for modeling individual sub-sequences. Our findings demonstrate that the DWT–VMD–GRU model, utilizing runoff and rainfall time series as inputs, outperforms other models such as GRU, long short-term memory (LSTM), DWT–GRU, and DWT–LSTM, consistently exhibiting superior performance across various evaluation metrics. During the testing phase, the DWT–VMD–GRU model yielded RMSE, MAE, MAPE, NSE, and KGE values of 245.5 m3/s, 200.5 m3/s, 0.033, 0.997, and 0.978, respectively. Additionally, optimal sliding window durations for different input combinations typically range from 1 to 3 months, with the DWT–VMD–GRU model (using runoff and rainfall) achieving optimal performance with a one-month sliding window. The model’s superior accuracy enhances water resource management, flood control, and reservoir operation, supporting better-informed decisions and efficient resource allocation.

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“…Yaseen 31 developed an enhanced version of ELM (EELM) for river flow predicting, demonstrating its superiority over Support Vector Regression (SVR) and ELM across multiple metrics including Nash–Sutcliffe Efficiency (NSE), Willmott’s Index (WI), Pearson correlation coefficient (R), Mean Absolute Error (MAE), and RMSE. Wang 32 proposed a model named TVF-EMD-SSA-ELM, which combines time-varying filtering (TVF) based empirical mode decomposition (EMD), the salp swarm algorithm (SSA), and extreme learning machine (ELM). This model was applied to the monthly runoff prediction for Manwan Hydropower, Hongjiadu Hydropower, and Yingluoxia Hydrological Station.…”

Section: Introductionmentioning

confidence: 99%

Integrated metaheuristic algorithms with extreme learning machine models for river streamflow prediction

Van Thieu,

Nguyen,

Sherif

et al. 2024

Sci Rep

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Accurate river streamflow prediction is pivotal for effective resource planning and flood risk management. Traditional river streamflow forecasting models encounter challenges such as nonlinearity, stochastic behavior, and convergence reliability. To overcome these, we introduce novel hybrid models that combine extreme learning machines (ELM) with cutting-edge mathematical inspired metaheuristic optimization algorithms, including Pareto-like sequential sampling (PSS), weighted mean of vectors (INFO), and the Runge–Kutta optimizer (RUN). Our comparative assessment includes 20 hybrid models across eight metaheuristic categories, using streamflow data from the Aswan High Dam on the Nile River. Our findings highlight the superior performance of mathematically based models, which demonstrate enhanced predictive accuracy, robust convergence, and sustained stability. Specifically, the PSS-ELM model achieves superior performance with a root mean square error of 2.0667, a Pearson’s correlation index (R) of 0.9374, and a Nash–Sutcliffe efficiency (NSE) of 0.8642. Additionally, INFO-ELM and RUN-ELM models exhibit robust convergence with mean absolute percentage errors of 15.21% and 15.28% respectively, a mean absolute errors of 1.2145 and 1.2105, and high Kling-Gupta efficiencies values of 0.9113 and 0.9124, respectively. These findings suggest that the adoption of our proposed models significantly enhances water management strategies and reduces any risks.

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An enhanced monthly runoff time series prediction using extreme learning machine optimized by salp swarm algorithm based on time varying filtering based empirical mode decomposition

Cited by 22 publications

References 90 publications

Digitalization, Industry 4.0, Data, KPIs, Modelization and Forecast for Energy Production in Hydroelectric Power Plants: A Review

Digitalization, Industry 4.0, Data, KPIs, Modelization and Forecast for Energy Production in Hydroelectric Power Plants: A Review

Monthly Runoff Prediction for Xijiang River via Gated Recurrent Unit, Discrete Wavelet Transform, and Variational Modal Decomposition

Integrated metaheuristic algorithms with extreme learning machine models for river streamflow prediction

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