Introducing entropy-based Bayesian model averaging for streamflow forecast

Darbandsari, Pedram; Coulibaly, Paulin

doi:10.1016/j.jhydrol.2020.125577

Cited by 24 publications

(15 citation statements)

References 47 publications

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“…While it was obvious that the BMA model yielded accurate stream ow forecasts at different day-ahead timescales, its forecasting accuracy decreased as the forecasting lead time increased. This is consistent with similar forecasting hydrological modeling studies undertaken at multiple timescales(Wagena et al, 2020;Darbandsari et al, 2020;Guo et al, 2021). This phenomenon is attributable to the greater time dependency of the shorter (vs. longer) time steps(Barzegar et al, 2017;Barzegar et al, 2021).…”

supporting

confidence: 88%

Ensemble learning of decomposition-based machine learning and deep learning models for multi-time step ahead streamflow forecasting in an arid region

Yang

Feng

et al. 2023

Preprint

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As much as accurate streamflow forecasts are important and significant for arid regions, they remain deficient and challenging. An ensemble learning strategy of decomposition-based machine learning and deep learning models was proposed to forecast multi-time-step ahead streamflow for northwest China’s Dunhuang Oasis. The efficiency and reliability of a Bayesian Model Averaging (BMA) ensemble strategy for 1-, 2-, and 3-day ahead streamflow forecasting was evaluated in comparison with decomposition-based machine learning and deep learning models: (i), a variational-mode-decomposition model coupled with a deep-belief-network model (VMD-DBN), (ii) a variational-mode-decomposition model coupled with a gradient-boosted-regression-tree model (VMD-GBRT), (iii) a complete ensemble empirical mode decomposition with adaptive noise model coupled with a deep belief network model (CEEMDAN-DBN), and (iv) a complete ensemble empirical mode decomposition with adaptive noise model with a gradient boosted regression tree coupled model (CEEMDAN-GBRT). Satisfactory forecasts were achieved with all proposed models at all lead times; however, based on Nash-Sutcliffe coefficient (NSE) values of 0.976, 0.967, and 0.957, the BMA model achieved the greatest accuracy for 1-, 2-, and 3-day ahead streamflow forecasts, respectively. Uncertainty analysis confirmed the reliability of the BMA model in yielding consistently accurate streamflow forecasts. Thus, the BMA ensemble strategy could provide an efficient alternative approach to multi-time-step ahead streamflow forecasting for areas where physically-based models cannot be used due to a lack of land surface data. The application of the BMA model was particularly valuable when the ensemble members gave equivalent satisfactory performances, making it difficult to choose amongst them.

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supporting

confidence: 88%

Ensemble learning of decomposition-based machine learning and deep learning models for multi-time step ahead streamflow forecasting in an arid region

Yang

Feng

et al. 2023

Preprint

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“…The range is between 0 and 1, and the best effect is 0.90. DI is the ratio of the average width of the 90% prediction interval to the observed value, with the lower the value, the better the prediction [60].…”

Section: Evaluation Measuresmentioning

confidence: 99%

A C-Vine Copula-Based Quantile Regression Method for Streamflow Forecasting in Xiangxi River Basin, China

Huang

et al. 2021

Sustainability

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In this study, a C-vine copula-based quantile regression (CVQR) model is proposed for forecasting monthly streamflow. The CVQR model integrates techniques for vine copulas and quantile regression into a framework that can effectively establish relationships between the multidimensional response-independent variables as well as capture the upper tail or asymmetric dependence (i.e., upper extreme values). The CVQR model is applied to the Xiangxi River basin that is located in the Three Gorges Reservoir area in China for monthly streamflow forecasting. Multiple linear regression (MLR) and artificial neural network (ANN) are also compared to illustrate the applicability of CVQR. The results show that the CVQR model performs best in the calibration period for monthly streamflow prediction. The results also indicate that MLR has the worst effects in extreme quantile (flood events) and confidence interval predictions. Moreover, the performance of ANN tends to be overestimated in the process of peak prediction. Notably, CVQR is the most effective at capturing upper tail dependences among the hydrometeorological variables (i.e., floods). These findings are very helpful to decision-makers in hydrological process identification and water resource management practices.

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“…The Bayesian Model Averaging (BMA) approach offers the best alternative solution to this problem [8], [9]. Bayesian model averaging has been applied in other fields such as economic and financial [10]- [13], hydrology [14]- [17], engineering [18], behavioral research [19], environmental [20], agriculture [21], climate and meteorological [22]- [24] and many others.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Model Averaging (BMA) Based on Logistic Regression for Gene Selection and Classification of Animal Tumor Disease on Microarray Data

Kuswanto¹,

Fitriana²

2022

Int. J. Adv. Sci. Eng. Inf. Technol.

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Tumor is one of the deadly diseases which is frequently to be found in animals. However, identifying whether an animal has a tumor still becomes a big challenge. Classification of tumor disease can be done through gene expression, which consists of hundreds of genes, but only a small number of samples is taken. This data structure is called microarray data having the characteristic of highdimensional data. The choice of a single model can be a problem for high-dimensional data because it ignores model uncertainty. This research proposed to use Bayesian Model Averaging (BMA) to model the uncertainty model by averaging the posterior distribution of all best models, weighted by their posterior model probabilities. Selecting relevant genes to diagnose animal tumors is very important; hence, variable selection needs to be carried out. The selection of predictor variables is carried out by using the iterative BMA algorithm.The BMA results showed that from 335 gene expressions, 12 genes were selected to be relevant genes for classifying whether the animals have a tumor or normal. Moreover, from 2 335 possible models formed, 12 of the best models are selected. The accuracy of BMA results is assessed using the Brier Score, resulting from a value indicating that the BMA model is good enough to classify animals, whether they have a tumor or not. This research has proven that BMA with logistic performance has very good predictability; hence, the method can be applied to classify other diseases.

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Introducing entropy-based Bayesian model averaging for streamflow forecast

Cited by 24 publications

References 47 publications

Ensemble learning of decomposition-based machine learning and deep learning models for multi-time step ahead streamflow forecasting in an arid region

Ensemble learning of decomposition-based machine learning and deep learning models for multi-time step ahead streamflow forecasting in an arid region

A C-Vine Copula-Based Quantile Regression Method for Streamflow Forecasting in Xiangxi River Basin, China

Bayesian Model Averaging (BMA) Based on Logistic Regression for Gene Selection and Classification of Animal Tumor Disease on Microarray Data

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