Long-Term Streamflow Prediction Using Hybrid SVR-ANN Based on Bayesian Model Averaging

Abbasi, Mahdi; Dehban, Hossein; Farokhnia, Ashkan; Roozbahani, Reza; Bahreinimotlagh, Masoud

doi:10.1061/(asce)he.1943-5584.0002218

Cited by 14 publications

(2 citation statements)

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“…CBMA exhibited narrower uncertainty intervals and higher reliability than BMA when merging monthly runoff series forecasts from Random Forest, ANN, and SVM models [87]. Additionally, applying BMA with Boruta variable preprocessing enhanced monthly runoff predictions from SVR, ANN, and MLR models in terms of continuous ranked probability skill score (CRPSS) and Kling-Gupta efficiency (KGE) metrics [109]. Studies combining statistical, dynamic, and machine learning models for seasonal forecasting revealed that techniques like CCR, BMA, and QMA provided unbiased and relatively smaller forecast errors compared to individual models, demonstrating their effectiveness in improving seasonal ensemble forecasts [31,66].…”

Section: Referencesmentioning

confidence: 99%

Review of Recent Developments in Hydrologic Forecast Merging Techniques

Sheikh,

Coulibaly

2024

Water

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Accurate forecasting in hydrologic modeling is crucial for sustainable water resource management across various sectors, where predicting extreme flow phases holds particular significance due to their severe impact on the territory. Due to the inherent uncertainties in hydrologic forecasting, relying solely on a single rainfall–runoff model may not provide reliable predictions. To address this challenge, over the years, researchers have developed and applied hydrologic forecast merging (HFM) techniques that combine multiple models or ensembles to enhance forecast accuracy, reduce uncertainty, and increase confidence in the forecast. This review summarizes the progress in HFM techniques since the early 1990s and covers developments and applications in flow simulation, uncertainty analysis, monthly and seasonal streamflow predictions, ensemble forecasts, flood forecasting, and climate change analysis. The findings indicate that while HFM techniques outperform individual models regarding forecasting efficiency, their performance across applications is not uniform. Among the different methods, Bayesian model averaging (BMA) is the most popular due to its ability to reduce uncertainty and provide accurate and reliable forecasts in deterministic and probabilistic simulations. With their application simplicity, regression techniques are also robust and efficient as they perform competitively well across different model-merging applications. While specific techniques, such as model-dependent weighted averaging and neural network methods, effectively reduce forecast uncertainty, there is still room for improving forecast accuracy across different lead times. Future research can focus on advanced HFM techniques for estimating optimal weights in time-varying domains and overcoming limitations, like simulating low flows in seasonally dry catchments.

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Section: Referencesmentioning

confidence: 99%

Review of Recent Developments in Hydrologic Forecast Merging Techniques

Sheikh,

Coulibaly

2024

Water

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“…The BMA algorithm was used as a single model to manage saltwater intrusion in the "1,500-foot" sand aquifer in the Baton Rouge area, Louisiana [8], or to build top-soil salinity maps for three coastal districts of Ben Tre province in Vietnam [9]. BMA was also combined with chance-constrained (CC) programming to build a BMA-CC framework to design a hydraulic barrier to protect public supply wells of the Government St. pump station from saltwater intrusion in the "1500-foot" sand and the "1700-foot" sand of the Baton Rouge area, southeastern Louisiana [10], or it also was employed using the combination of the Boruta-artificial neural network (B-ANN) and the Boruta-support vector regression (B-SVR) models to predict long-term streamflow of the Volga River [11]. Most of these studies have indicated the number of input variables having a crucial role in assessing the salinity forecast models, and the used ML algorithms in these studies have been applied based on scientists'experiences.…”

Section: Introductionmentioning

confidence: 99%

Application of the bayesian model averaging algorithm in evaluating and selecting optimal salinity prediction models

Quynh,

Hang,

Hieu

et al. 2023

JSTCE

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Salinity intrusion poses significant challenges to coastal regions worldwide. Reliable salinity prediction models can provide valuable information to mitigate the impact and influence of salinity intrusion. However, their accuracy relies mainly on the selected input variables and used optimal models. This study employs the BayesianModel Averaging (BMA) algorithm to evaluate input variable importance and select the most reliable salinityprediction model. Based on an analysis of observed salinity data and climate data extracted from Landsat 8 OLI in the Google Earth Engine platform, the BMA algorithm identifies the significance of critical variables andoptimal salinity prediction models. Various statistical metrics, including R-squared, Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) from the Random Forest method, were used to verify the performance of these optimal salinity prediction models. These obtained results offer foundational knowledge and valuable insights for future studies in determining appropriate input variables and selecting the best optimalsalinity prediction model.

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