Improved annual rainfall-runoff forecasting using PSO–SVM model based on EEMD

Wang, Wenchuan; Xu, Dianxiang; Chau, Kwok Wing; Chen, Shouyu

doi:10.2166/hydro.2013.134

Cited by 246 publications

(86 citation statements)

References 64 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%

“…It has been reported that these hybrid MLMs, which consists of time series decomposition and sub-time series modeling, were able to achieve better performance compared with the single MLMs. Finally, the hybrid MLMs, combined with more than two methods, have been developed for rainfall-runoff and streamflow modeling including DWT, PSO, and SVMs [45]; DWT, GA, and adaptive neuro-fuzzy inference system (ANFIS) [46]; EEMD, PSO, and SVMs [47]; EEMD, SOM, and linear genetic programming [48]; wavelet transform, singular spectrum, chaotic approach, and SVR [49]; and Hermite-projection pursuit regression, social spider optimization, and least square algorithm [50].…”

Section: Introductionmentioning

confidence: 99%

“…They found that the advantages of data preprocessing were dependent on the characteristics of intrinsic modes. Wang et al [47] proposed a coupling of EEMD, PSO and SVMs for forecasting annual rainfall-runoff and concluded that the hybrid approach could improve the accuracy of annual runoff forecasting significantly. Huang et al [42] assessed the performance of a modified EMD-SVMs model to forecast monthly streamflow and confirmed that the hybrid model provided high prediction accuracy and reliable stability.…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2018

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“…The PSO algorithm is derived from the migration mechanism of birds during foraging, which has advantages of fast convergence, efficient parallel computing and strong universality which is able to efficiently avoid local optimum [23,24]. Moreover, the iteration velocity of particle is influenced by the sum of current velocity, historical particle value, current global optimal value and random interferences, which avoids local optima to a large extent and improves search coverage and effectiveness.…”

Section: Pso Parameter Calibration Methodsmentioning

confidence: 99%

Comparative Analysis of ANN and SVM Models Combined with Wavelet Preprocess for Groundwater Depth Prediction

Zhou

Wang

Yang

2017

Water

112

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Reliable prediction of groundwater depth fluctuations has been an important component in sustainable water resources management. In this study, a data-driven prediction model combining discrete wavelet transform (DWT) preprocess and support vector machine (SVM) was proposed for groundwater depth forecasting. Regular artificial neural networks (ANN), regular SVM, and wavelet preprocessed artificial neural networks (WANN) models were also developed for comparison. These methods were applied to the monthly groundwater depth records over a period of 37 years from ten wells in the Mengcheng County, China. Relative absolute error (RAE), Pearson correlation coefficient (r), root mean square error (RMSE), and Nash-Sutcliffe efficiency (NSE) were adopted for model evaluation. The results indicate that wavelet preprocess extremely improved the training and test performance of ANN and SVM models. The WSVM model provided the most precise and reliable groundwater depth prediction compared with ANN, SVM, and WSVM models. The criterion of RAE, r, RMSE, and NSE values for proposed WSVM model are 0.20, 0.97, 0.18 and 0.94, respectively. Comprehensive comparisons and discussion revealed that wavelet preprocess extremely improves the prediction precision and reliability for both SVM and ANN models. The prediction result of SVM model is superior to ANN model in generalization ability and precision. Nevertheless, the performance of WANN is superior to SVM model, which further validates the power of data preprocess in data-driven prediction models. Finally, the optimal model, WSVM, is discussed by comparing its subseries performances as well as model performance stability, revealing the efficiency and universality of WSVM model in data driven prediction field.

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“…In the late 1980s, research on artificial neural network (ANN) applications advanced after the introduction of backpropagation training algorithms for feedforward ANNs [3]. ANNs, which simulate the biological nervous system and brain activity, have become the preferred forecasting approach in hydrology and hydrometeorology (e.g., [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]). ANNs are advantageous because feedforward networks are universal approximators capable of learning continuous functions with any desired degree of accuracy.…”

Section: Introductionmentioning

confidence: 99%

Parameter Automatic Calibration Approach for Neural-Network-Based Cyclonic Precipitation Forecast Models

Wei

Tsai

2015

Water

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This paper presents artificial neural network (ANN)-based models for forecasting precipitation, in which the training parameters are adjusted using a parameter automatic calibration (PAC) approach. A classical ANN-based model, the multilayer perceptron (MLP) neural network, was used to verify the utility of the proposed ANN-PAC approach. The MLP-based ANN used the learning rate, momentum, and number of neurons in the hidden layer as its major parameters. The Dawu gauge station in Taitung, Taiwan, was the study site, and observed typhoon characteristics and ground weather data were the study data. The traditional multiple linear regression model was selected as the benchmark for comparing the accuracy of the ANN-PAC model. In addition, two MLP ANN models based on a trial-and-error calibration method, ANN-TRI1 and ANN-TRI2, were realized by manually tuning the parameters. We found the results yielded by the ANN-PAC model were more reliable than those yielded by the ANN-TRI1, ANN-TRI2, and traditional regression models. In addition, the computing efficiency of the ANN-PAC model decreased with an increase in the number of increments within the parameter ranges because of the considerably increased computational time, whereas the prediction errors decreased because of the model's increased capability of identifying optimal solutions. OPEN ACCESSWater 2015, 7 3964

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Improved annual rainfall-runoff forecasting using PSO–SVM model based on EEMD

Cited by 246 publications

References 64 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

Comparative Analysis of ANN and SVM Models Combined with Wavelet Preprocess for Groundwater Depth Prediction

Parameter Automatic Calibration Approach for Neural-Network-Based Cyclonic Precipitation Forecast Models

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