River Stage Forecasting Using Wavelet Packet Decomposition and Machine Learning Models

Seo, Youngmin; Kim, Sungwon; Kisi, Özgür; Singh, Vijay P.; Parasuraman, Kamban

doi:10.1007/s11269-016-1409-4

Cited by 48 publications

(21 citation statements)

References 51 publications

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“…These results are consistent with those of previous studies dealing with hybrid modeling for rainfall-runoff process [6,47,[99][100][101]. Single MLMs (e.g., ANN, ELM, and LSSVR) represent a natural limitation in terms of nonstationary hydrologic time series modeling even if they can model nonlinear hydrologic time series effectively [102,103].…”

Section: Performance Assessmentsupporting

confidence: 91%

Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

2018

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Accurate modeling for nonlinear and nonstationary rainfall-runoff processes is essential for performing hydrologic practices effectively. This paper proposes two hybrid machine learning models (MLMs) coupled with variational mode decomposition (VMD) to enhance the accuracy for daily rainfall-runoff modeling. These hybrid MLMs consist of VMD-based extreme learning machine (VMD-ELM) and VMD-based least squares support vector regression (VMD-LSSVR). The VMD is employed to decompose original input and target time series into sub-time series called intrinsic mode functions (IMFs). The ELM and LSSVR models are selected for developing daily rainfall-runoff models utilizing the IMFs as inputs. The performances of VMD-ELM and VMD-LSSVR models are evaluated utilizing efficiency and effectiveness indices. Their performances are also compared with those of VMD-based artificial neural network (VMD-ANN), discrete wavelet transform (DWT)-based MLMs (DWT-ELM, DWT-LSSVR, and DWT-ANN) and single MLMs (ELM, LSSVR, and ANN). As a result, the VMD-based MLMs provide better accuracy compared with the single MLMs and yield slightly better performance than the DWT-based MLMs. Among all models, the VMD-ELM and VMD-LSSVR models achieve the best performance in daily rainfall-runoff modeling with respect to efficiency and effectiveness. Therefore, the VMD-ELM and VMD-LSSVR models can be an alternative tool for reliable and accurate daily rainfall-runoff modeling.

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Section: Performance Assessmentsupporting

confidence: 91%

Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

2018

Self Cite

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“…As can be seen in Figure 1, a typical three-layer feed-forward ANN usually consists of three layers including input, hidden and output layers [36]. Each layer contains nodes that are connected to nodes of the other layer(s).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Streamflow Forecasting Using Empirical Wavelet Transform and Artificial Neural Networks

Tian

Zhou

Zhang

et al. 2017

Water

103

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Accurate and reliable streamflow forecasting plays an important role in various aspects of water resources management such as reservoir scheduling and water supply. This paper shows the development of a novel hybrid model for streamflow forecasting and demonstrates its efficiency. In the proposed hybrid model for streamflow forecasting, the empirical wavelet transform (EWT) is firstly employed to eliminate the redundant noises from the original streamflow series. Secondly, the partial autocorrelation function (PACF) values are explored to identify the inputs for the artificial neural network (ANN) models. Thirdly, the weights and biases of the ANN architecture are tuned and optimized by the multi-verse optimizer (MVO) algorithm. Finally, the simulated streamflow is obtained using the well-trained MVO-ANN model. The proposed hybrid model has been applied to annual streamflow observations from four hydrological stations in the upper reaches of the Yangtze River, China. Parallel experiments using non-denoising models, the back propagation neural network (BPNN) and the ANN optimized by the particle swarm optimization algorithm (PSO-ANN) have been designed and conducted to compare with the proposed model. Results obtained from this study indicate that the proposed hybrid model can capture the nonlinear characteristics of the streamflow time series and thus provides more accurate forecasting results.

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“…Consequently, it is necessary to adopt a method for scale decomposition before regression. Several approaches, including the empirical mode decomposition, wavelet decomposition (WD), and wavelet packet decomposition (WPD), are employed to facilitate the models . Among these methods, WPD has attracted increasing attention due to its ability to provide more flexible time‐frequency decomposition, especially for the high‐frequency region.…”

Section: Introductionmentioning

confidence: 99%

Multi‐Scale Fuzzy Inference System for Influent Characteristic Prediction of Wastewater Treatment

Cheng

Li²,

Bai

et al. 2018

CLEAN Soil Air Water

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Accurate influent characteristic prediction is vital to maintain the stable performance of wastewater treatment processes. In this work, an associated approach based on the wavelet packet decomposition (WPD) and adaptive network‐based fuzzy inference system (ANFIS) is proposed to address this issue. In this method, the WPD is first adopted to decompose the historical data of the influent characteristic into wavelet coefficients in different scales. The time sub‐series, which are obtained with a single branch reconstruction of the wavelet coefficients in each scale, are then utilized to build the ANFIS regression model. The predicted sub‐results in each scale are finally summarized into an eventual predicted result. Moreover, a particle swarm optimization (PSO) algorithm is employed to acquire the optimal parameters of the multi‐scale ANFIS, and chaos theory is utilized to determine the input variables of the multi‐scale ANFIS. The reported approach is investigated by the influent characteristic data, including the chemical oxygen and biochemical oxygen demands from a wastewater treatment plant (WTP) in southwest of China. Two peer models are introduced for a comparison study. The results show that the developed approach has superior performance in terms of the mean absolute error (3.346 and 1.384), mean absolute percentage error (1.804% and 1.800%), root mean square error (3.988 and 1.788), and correlation coefficient (0.960 and 0.964), and can accurately predict the influent characteristic of the WTP.

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River Stage Forecasting Using Wavelet Packet Decomposition and Machine Learning Models

Cited by 48 publications

References 51 publications

Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

Streamflow Forecasting Using Empirical Wavelet Transform and Artificial Neural Networks

Multi‐Scale Fuzzy Inference System for Influent Characteristic Prediction of Wastewater Treatment

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