Time series forecasting of river flow using an integrated approach of wavelet multi-resolution analysis and evolutionary data-driven models. A case study: Sebaou River (Algeria)

Zakhrouf, Mousaab; Bouchelkia, Hamid; Stamboul, Madani; Kim, Sung-Won; Heddam, Salim

doi:10.1080/02723646.2018.1429245

Cited by 12 publications

(9 citation statements)

References 42 publications

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“…S f or − S obs (14) where S obs and S f or are the observed and forecasted streamflow values; S obs and S f or are the observed and forecasted mean streamflow values; and n is total number of employed data.…”

Section: Mean Absolute Error (Mae)mentioning

confidence: 99%

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Bayesian Model Averaging: A Unique Model Enhancing Forecasting Accuracy for Daily Streamflow Based on Different Antecedent Time Series

et al. 2020

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Streamflow forecasting is a vital task for hydrology and water resources engineering, and the different artificial intelligence (AI) approaches have been employed for this purposes until now. Additionally, the forecasting accuracy and uncertainty estimation are the meaningful assignments that need to be recognized. The addressed research investigates the potential of novel ensemble approach, Bayesian model averaging (BMA), in streamflow forecasting using daily time series data from two stations (i.e., Hongcheon and Jucheon), South Korea. Six categories (i.e., M1–M6) of input combination using different antecedent times were employed for streamflow forecasting. The outcomes of BMA model were compared with those of multivariate adaptive regression spline (MARS), M5 model tree (M5Tree), and Kernel extreme learning machines (KELM) models considering four assessment indexes, root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), and mean absolute error (MAE). The results revealed the superior accuracy of BMA model over three machine learning models in daily streamflow forecasting. Considering RMSE values among the best models during testing phase, the best BMA model (i.e., BMA2) enhanced the forecasting accuracy of MARS1, M5Tree4, and KELM3 models by 5.2%, 5.8%, and 3.4% in Hongcheon station. Additionally, the best BMA model (i.e., BMA1) improved the forecasting accuracy of MARS1, M5Tree1, and KELM1 models by 6.7%, 9.5%, and 3.7% in Jucheon station. In addition, the best BMA models in both stations allowed the uncertainty estimation, and produced higher uncertainty of peak flows compared to that of low flows. As one of the most robust and effective tools, therefore, the BMA model can be successfully employed for streamflow forecasting with different antecedent times.

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Section: Mean Absolute Error (Mae)mentioning

confidence: 99%

“…Increasing issue complications often depend on long antecedent times (or lead times) such as days and months [8][9][10][11]. Therefore, streamflow forecasting using different Sustainability 2020, 12, 9720 2 of 22 antecedent times can be categorized as universal assignment for hydrology and water resources researches [12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Model Averaging: A Unique Model Enhancing Forecasting Accuracy for Daily Streamflow Based on Different Antecedent Time Series

et al. 2020

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“…e accelerated hydrological cycle enhances the intensity of precipitation events which boost variation in streamflow, and such events are responsible for frequent floods and droughts [2]. Researchers have applied many models to predict stream inflow including autoregressive (AR), autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), regression-based models, neurofuzzy models, conceptual models, and more complex models based on artificial neural network (ANN) [3][4][5][6][7]. Soft computing models such as ANN, adaptive neuro-fuzzy inference system (ANFIS), and random forest are proposed by Seo et al [8] for river stage modeling.…”

Section: Introductionmentioning

confidence: 99%

Reservoir Inflow Prediction by Employing Response Surface-Based Models Conjunction with Wavelet and Bootstrap Techniques

Shehzad

Bashir

Noor‐ul‐Amin

et al. 2021

Mathematical Problems in Engineering

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Reservoir inflow prediction is a vital subject in the field of hydrology because it determines the flood event. The negative impact of the floods could be minimized greatly if the flood frequency is predicted accurately in advance. In the present study, a novel hybrid model, bootstrap quadratic response surface is developed to test daily streamflow prediction. The developed bootstrap quadratic response surface model is compared with multiple linear regression model, first-order response surface model, quadratic response surface model, wavelet first-order response surface model, wavelet quadratic response surface model, and bootstrap first-order response surface model. Time series data of monsoon season (1 July to 30 September) for the year 2010 of the Chenab river basin are analyzed. The studied models are tested by using performance indices: Nash–Sutcliffe coefficient of efficiency, mean absolute error, persistence index, and root mean square error. Results reveal that the proposed model, i.e., bootstrap quadratic response surface shows good performance and produces optimum results for daily reservoir inflow prediction than other models used in the study.

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“…Nevertheless, various investigations have been accomplished for estimating solar radiation using empirical and conventional methods, but there is an essential challenge to develop a method to overcome non-stationary time series. To address non-stationary problems, several pre-processing approaches (e.g., the principal component analysis (PCA) [24,25], continuous wavelet transform (CWT) [26][27][28], moving average (MA) [29], wavelet multi-resolution analysis (WMRA) [30], maximum entropy spectral analysis (MESA) [31], singular spectrum analysis (SSA) [32,33], and empirical mode decomposition (EMD) [34]) have been used to decompose input/output variables. These techniques are useful tools to resolve the frequency components of input/output time series data by decomposing original datasets into several sub-series, before such datasets are applied in time series estimations.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Daily Solar Radiation Using CEEMDAN Decomposition-Based MARS Model Trained by Crow Search Algorithm

Rezaie-Balf

Maleki²,

Kim

et al. 2019

Energies

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The precise forecasting of daily solar radiation (DSR) is receiving prominent attention among thriving solar energy studies. In this study, three standalone models, including gene expression programing (GEP), multivariate adaptive regression splines (MARS), and self-adaptive MARS (SaMARS), were evaluated to forecast DSR. A SaMARS model was classified as MARS model when using the crow search algorithm (CSA). In addition, to overcome the limitations of the standalone models, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was employed to enhance the accuracy of DSR forecasting. Therefore, three hybrid models including CEEMDAN-GEP, CEEMDAN-MARS, and CEEMDAN-SaMARS were proposed to forecast DSR in Busan and Incheon stations in South Korea. The performance of proposed models were evaluated and affirmed that the accuracy of the CEEMDAN-SaMARS model (NSE = 0.878–0.883) outperformed CEEMDAN-MARS (NSE = 0.819–0.818), CEEMDAN-GEP (NSE = 0.873–0.789), SaMARS (NSE = 0.846–0.769), MARS (NSE = 0.819–0.758), and GEP (NSE = 0.814–0.755) models at both stations. Therefore, it can be concluded that the optimized CEEMDAN-SaMARS model significantly enhanced the accuracy of DSR forecasting compared to that of standalone models.

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Time series forecasting of river flow using an integrated approach of wavelet multi-resolution analysis and evolutionary data-driven models. A case study: Sebaou River (Algeria)

Cited by 12 publications

References 42 publications

Bayesian Model Averaging: A Unique Model Enhancing Forecasting Accuracy for Daily Streamflow Based on Different Antecedent Time Series

Bayesian Model Averaging: A Unique Model Enhancing Forecasting Accuracy for Daily Streamflow Based on Different Antecedent Time Series

Reservoir Inflow Prediction by Employing Response Surface-Based Models Conjunction with Wavelet and Bootstrap Techniques

Forecasting Daily Solar Radiation Using CEEMDAN Decomposition-Based MARS Model Trained by Crow Search Algorithm

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