Monthly streamflow forecasting based on hidden Markov model and Gaussian Mixture Regression

Liu, Yongqi; Ye, Lei; Hui, Qing; Xia, Hong; Ye, Jiajun; Yin, Xingli

doi:10.1016/j.jhydrol.2018.03.057

Cited by 76 publications

(25 citation statements)

References 40 publications

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“…Considering the fair and simple reasons in determining the weight of the four SEB models, the uniform model prior was employed before this study for examination. The BMA method assembles four SEB models (i.e., four explanatory variables) thus 2 4…”

Section: Effect Of Model Priorsmentioning

confidence: 99%

“…It is a sophisticated process that links water transport from the land surface to the atmosphere. ET plays an important part in the global hydrological cycle, accounting for about two-thirds of the terrestrial precipitation [2][3][4]. Therefore, the accurate estimation of ET provides an effective approach in understanding the hydrological cycle process [5,6], planning cropland water consumption [7], and environmental evaluation, especially in arid and semiarid areas [8], as well as exploring the laws of crop growth progress and predicting the tendency of global climate change [9].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improving Estimation of Cropland Evapotranspiration by the Bayesian Model Averaging Method with Surface Energy Balance Models

Sun

Yang

et al. 2019

Atmosphere

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Evapotranspiration (ET) is one of the key components of the global hydrological cycle. Many models have been established to obtain an accurate estimation of ET, but the uncertainty of each model has not been satisfactorily addressed, and the weight determination in multi-model simulation methods remains unclear. In this study, the Bayesian model averaging (BMA) method was adopted to tackle this issue. We explored the combination of four surface energy balance (SEB) models (SEBAL, SSEB, S-SEBI and SEBS) with the BMA method by using Landsat 8 images over two study areas in China, the Huailai flux station (semiarid region) and the Sidaoqiao flux station (arid/semiarid region), and the data from two stations were used as validation for this method. The performances of SEB models and different BMA methods is revealed by three statistical parameters (i.e., the coefficient of determination (R2), root mean squared error (RMSE), and the Nash-Sutcliffe efficiency coefficient (NSE)). We found the best performing SEB model was SEBAL, with an R2 of 0.609 (0.672), RMSE of 1.345 (0.876) mm/day, and NSE of 0.407 (0.563) at Huailai (Sidaoqiao) station. Compared with the four individual SEB models, each of the BMA methods (fixed, posterior inclusion probability, or random) can provide a more accurate and reliable simulation result. Similarly, in Huailai (Sidaoqiao) station, the best performing BMA random model provided an R2 of 0.750 (0.796), RMSE of 0.902 (0.602) mm/day, and NSE of 0.746 (0.793). We conclude that the BMA method outperformed the four SEB models alone and obtained a more accurate prediction of ET in two cropland areas, which provides important guidance for water resource allocation and management in arid and semiarid regions.

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Section: Effect Of Model Priorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving Estimation of Cropland Evapotranspiration by the Bayesian Model Averaging Method with Surface Energy Balance Models

Sun

Yang

et al. 2019

Atmosphere

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“…In some areas, the reservoir water storage capacity is even greater than the total water resources outside the reservoir (such lakes, groundwater, etc.). In addition, large reservoirs may even affect meteorological conditions in the surrounding areas [24,25]. Therefore, this paper proposes that when analyzing the carrying capacity of water resources in areas with abundant water resources and many reservoirs, the water volume of the reservoir should be taken as a single index factor in calculations to improve the reliability of the calculation results.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Water Resources Carrying Capacity and Its Obstruction Factor Analysis: A Case Study of Hubei Province, China

Zhu

Bai

et al. 2019

Water

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The carrying capacity of water resources can provide a reference index for regional economic construction and development. Hubei produces 13.2% of China’s hydroelectric energy and 4% of China’s water resources, highlighting that the reservoir group in Hubei province is relatively developed. In the current research on water resources carrying capacity, only the amount of water resources was considered; the benign mutual feeding effect of regional reservoirs on regional water resources carrying capacity was not reflected upon. In order to guide social and economic activities better, this paper proposes the addition of reservoir water storage to the calculation of water resources carrying capacity as a separate indicator. In this paper, the cloud model method was used to calculate the water resources carrying capacity of Hubei province and the Dematel method was used to determine the degree of importance of reservoir water storage. Finally, the degree of obstacles was also considered to discuss the main factors affecting the water resources carrying capacity of Hubei province. In the system discussed in this paper, the degree of influence and the affected degree of reservoir water storage were found to be 1.2915 and 0.5759, respectively. The calculation results showed that Hubei province’s water resources carrying capacity has been increasing every year and the amount of water resources per unit area was the main restricting factor, with the obstacle degree reaching 19.24% of the average annual level.

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“…In addition to the proposed VBNN, a deterministic prediction model NN and two probabilistic prediction models were developed for comparison, including Gaussian process regression (GPR) and hidden Markov model (HMM) [28].…”

Section: Model Developmentmentioning

confidence: 99%

Variational Bayesian Neural Network for Ensemble Flood Forecasting

Zhan

Hui

Liu

et al. 2020

Water

Self Cite

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Disastrous floods are destructive and likely to cause widespread economic losses. An understanding of flood forecasting and its potential forecast uncertainty is essential for water resource managers. Reliable forecasting may provide future streamflow information to assist in an assessment of the benefits of reservoirs and the risk of flood disasters. However, deterministic forecasting models are not able to provide forecast uncertainty information. To quantify the forecast uncertainty, a variational Bayesian neural network (VBNN) model for ensemble flood forecasting is proposed in this study. In VBNN, the posterior distribution is approximated by the variational distribution, which can avoid the heavy computational costs in the traditional Bayesian neural network. To transform the model parameters’ uncertainty into the model output uncertainty, a Monte Carlo sample is applied to give ensemble forecast results. The proposed method is verified by a flood forecasting case study on the upper Yangtze River. A point forecasting model neural network and two probabilistic forecasting models, including hidden Markov Model and Gaussian process regression, are also applied to compare with the proposed model. The experimental results show that the VBNN performs better than other comparable models in terms of both accuracy and reliability. Finally, the result of uncertainty estimation shows that the VBNN can effectively handle heteroscedastic flood streamflow data.

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Monthly streamflow forecasting based on hidden Markov model and Gaussian Mixture Regression

Cited by 76 publications

References 40 publications

Improving Estimation of Cropland Evapotranspiration by the Bayesian Model Averaging Method with Surface Energy Balance Models

Improving Estimation of Cropland Evapotranspiration by the Bayesian Model Averaging Method with Surface Energy Balance Models

Evaluation of Water Resources Carrying Capacity and Its Obstruction Factor Analysis: A Case Study of Hubei Province, China

Variational Bayesian Neural Network for Ensemble Flood Forecasting

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