Intermittent stream flow forecasting and modelling with hybrid wavelet neuro-fuzzy model

Badrzadeh, Honey; Sarukkalige, Ranjan; Jayawardena, A. W.

doi:10.2166/nh.2017.163

Cited by 22 publications

(11 citation statements)

References 20 publications

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“…The standard deviation reduction (SDR) can influence to build the tree of M5Tree model. Additionally, it can reinforce the expected error reduction for specific points using Equation (5).…”

Section: M5 Model Tree (M5tree)mentioning

confidence: 69%

“…Implementing a stable model to forecast streamflow can be influential for the fields of hydrology and water resources researches [1][2][3][4]. Streamflow forecasting, however, is an intricate project because of nonstationary time series and reliance on temporal and spatial parameters which have unclear and complicated components [5][6][7]. Increasing issue complications often depend on long antecedent times (or lead times) such as days and months [8][9][10][11].…”

Section: Introductionmentioning

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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“…The standard deviation reduction (SDR) can influence to build the tree of M5Tree model. Additionally, it can reinforce the expected error reduction for specific points using Equation (5).…”

Section: M5 Model Tree (M5tree)mentioning

confidence: 69%

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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“…To compare the hybrid wavelet neuro-fuzzy model [38] with the proposed new model, the old model is first analyzed in this section as it has the same input so that the results can be compared with those of the proposed new model. In this scenario, the number of input data is equal to the input data of [38] and the results are tabulated in Table 2 Section 4.2. From the comparison between the results, it is initially clear that the proposed method performs well and also has a higher speed and accuracy than the previous method.…”

Section: Scenariomentioning

confidence: 99%

Data-Driven Forecasting and Modeling of Runoff Flow to Reduce Flood Risk Using a Novel Hybrid Wavelet-Neural Network Based on Feature Extraction

et al. 2021

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The reliable forecasting of river flow plays a key role in reducing the risk of floods. Regarding nonlinear and variable characteristics of hydraulic processes, the use of data-driven and hybrid methods has become more noticeable. Thus, this paper proposes a novel hybrid wavelet-neural network (WNN) method with feature extraction to forecast river flow. To do this, initially, the collected data are analyzed by the wavelet method. Then, the number of inputs to the ANN is determined using feature extraction, which is based on energy, standard deviation, and maximum values of the analyzed data. The proposed method has been analyzed by different input and various structures for daily, weekly, and monthly flow forecasting at Ellen Brook river station, western Australia. Furthermore, the mean squares error (MSE), root mean square error (RMSE), and the Nash-Sutcliffe efficiency (NSE) is used to evaluate the performance of the suggested method. Furthermore, the obtained findings were compared to those of other models and methods in order to examine the performance and efficiency of the feature extraction process. It was discovered that the proposed feature extraction model outperformed their counterparts, especially when it came to long-term forecasting.

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“…Lagged values of observed streamflows have also been used as inputs to explicitly capture streamflow persistence in many streamflow forecasting studies ( Badrzadeh et al, 2017;Hadi and Tombul, 2018;Danandeh Mehr, 2018;Rahmani-Rezaeieh et al, 2020). However, the use of lagged streamflows (which is an output of the model) is not suitable when multi-step ahead https://doi.org/10.5194/hess-2021-176 Preprint.…”

Section: How Well Does the Model Predict Non-zero Flowrates?mentioning

confidence: 99%

Deep, Wide, or Shallow? Artificial Neural Network Topologies for Predicting Intermittent Flows

Forghanparast

Hernandez

Uddameri

2021

Preprint

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Abstract. Intermittent Rivers and Ephemeral Streams (IRES) comprise 60 % of all streams in the US and about 50 % of the streams worldwide. Furthermore, climate-driven changes are expected to force a shift towards intermittency in currently perennial streams. Most modeling studies have treated intermittent streamflows as a continuum. However, it is better to envision flow data of IRES as a “mixture-type”, comprised of both flow and no-flow regimes. It is therefore hypothesized that data-driven models with both classification and regression cells can improve the streamflow forecasting abilities in these streams. Deep and wide Artificial Neural Networks (ANNs) comprising of classification and regression cells were developed here by stacking them in series and parallel configurations. These deep and wide network architectures were compared against the commonly used single hidden layer ANNs (shallow), as a baseline, for modeling IRES flow series under the continuum assumption. New metrics focused on no-flow persistence and transitions between flow and no-flow states were formulated using contingency tables and Markov chain analysis. Nine IRES across the state of Texas, US, were used as a wide range of testbeds with different hydro-climatic characteristics. Model overfitting and the curse-of-dimensionality were reduced using extreme learning machines (ELM), and balancing training data using the synthetic minority oversampling technique (SMOTE), greedy learning and Least Absolute Shrinkage and Selection Operator (LASSO). The addition of classifier cells greatly improved the ability to distinguish between no-flow and flow states, in turn, improving the ability to capture no-flow persistence (dryness) and transitions to and from flow states (dryness initiation and cessation). The wide network topology provided better results when the focus was on capturing low flows and the deep topology did well in capturing extreme flows (zero and > 75th percentile).

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Intermittent stream flow forecasting and modelling with hybrid wavelet neuro-fuzzy model

Cited by 22 publications

References 20 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

Data-Driven Forecasting and Modeling of Runoff Flow to Reduce Flood Risk Using a Novel Hybrid Wavelet-Neural Network Based on Feature Extraction

Deep, Wide, or Shallow? Artificial Neural Network Topologies for Predicting Intermittent Flows

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