A Comparative Assessment of Metaheuristic Optimized Extreme Learning Machine and Deep Neural Network in Multi-Step-Ahead Long-term Rainfall Prediction for All-Indian Regions

Kumar, Radhikesh; Singh, M. P.; Roy, Bappadittya; Shahid, Afzal Hussain

doi:10.1007/s11269-021-02822-6

Cited by 35 publications

(12 citation statements)

References 28 publications

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“…Selecting appropriate input variables has an important influence on the forecast result. In previous studies, several methods have been tried to determine input variables, and PACF has been frequently used as an efficient tool (Feng et al, 2020;Kumar et al, 2021)…”

Section: Input Determinationmentioning

confidence: 99%

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

Yang

Luo³

et al. 2022

Front. Environ. Sci.

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

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Section: Input Determinationmentioning

confidence: 99%

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

Yang

Luo³

et al. 2022

Front. Environ. Sci.

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“…Kumar et al optimized the parameters of ELMs used for long-term rainfall prediction using biogeography, genetic algorithms (GAs), and particle swarm optimization (PSO), respectively. 15 Jiang et al proposed an enhanced PSO algorithm for optimizing the parameters of ELMs and developing a model for monthly runoff prediction. 16 Zhang et al decomposed the data series using complete ensemble empirical mode decomposition (CEEMD), which generated an ELM prediction model for each component 17 and then optimized the weights and deviations of network nodes using the ant lion optimization (ALO) algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…Numerous studies have been conducted on WA, based on data decomposition and time-series prediction. 15,[21][22][23][24][25][26] In 1998, Huang et al proposed empirical mode decomposition (EMD). 27 The purpose of EMD is to decompose the original signal into several intrinsic mode functions (IMFs), and to determine the intrinsic properties of the effective signal in the data based on experience, that is, to smooth the time series.…”

Section: Related Workmentioning

confidence: 99%

“…Li et al proposed a multi‐objective imperialist competitive algorithm for ELM optimization, 14 which proposed a fuzzy data preprocessing scheme of fuzzy time series to predict wind speed series. Kumar et al optimized the parameters of ELMs used for long‐term rainfall prediction using biogeography, genetic algorithms (GAs), and particle swarm optimization (PSO), respectively 15 . Jiang et al proposed an enhanced PSO algorithm for optimizing the parameters of ELMs and developing a model for monthly runoff prediction 16 .…”

Section: Introductionmentioning

confidence: 99%

“…As a result, these characteristics can be used to analyze the signal. Numerous studies have been conducted on WA, based on data decomposition and time‐series prediction 15,21–26 . In 1998, Huang et al proposed empirical mode decomposition (EMD) 27 .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

SAVE‐AS: Accelerating convergence in network traffic prediction through adaptive optimized variational mode decomposition and an integrated extreme learning machine

Shi

Leau

2022

Int J Communication

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Nonlinearity and nonstationary data affect the prediction accuracy of network traffic. A practical solution is to use an integrated modeling method based on time-series decomposition and an extreme learning machine (ELM). The original network traffic data series are decomposed in this paper using variational mode decomposition (VMD), and then each subdata series is subjected to phase space reconstruction (PSR). Finally, ELM trains a model for predicting network traffic. We used scalable artificial bee colony (SABC) algorithms to optimize parameter selection throughout the modeling process. However, several processes, such as VMD decomposition, PSR, and ELM training, will be repeated during iterative model optimization, resulting in a significant increase in computational complexity. This article proposes an adaptive selection mechanism for decomposing time series, which named is SABC-VMD-ELM prediction method with an adaptive selection (SAVE-AS) method. From gray correlation degree and approximate entropy, an adaptive selection operator is constructed that can adaptively select the number of data sets after VMD optimization decomposition. The ELM model is trained using several subdata sequences that satisfy the conditions rather than all subdata sequences to minimize computational complexity in modeling. Two publicly available data sets of Mackey-Glass and Lorenz chaotic time series were used; in addition, actual network traffic data from the MAWI Working Group's WIDE Backbone was simulated. It is demonstrated that the prediction model can reduce computational complexity and accelerate convergence speed while maintaining the model's predictive accuracy. In three data sets, training time was reduced by 25.25%, 23.87%, and 41.36%, respectively.

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Human Stress Detection from SWCT EEG Data Using Optimised Stacked Deep Learning Model

Jadhav

Malviya

Shandilya

et al. 2023

Springer Tracts in Human-Centered Computing

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A Comparative Assessment of Metaheuristic Optimized Extreme Learning Machine and Deep Neural Network in Multi-Step-Ahead Long-term Rainfall Prediction for All-Indian Regions

Cited by 35 publications

References 28 publications

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

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

SAVE‐AS: Accelerating convergence in network traffic prediction through adaptive optimized variational mode decomposition and an integrated extreme learning machine

Human Stress Detection from SWCT EEG Data Using Optimised Stacked Deep Learning Model

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