.png){kind=icon}
返回
A stochastic deep-learning-based approach for improved streamflow simulation
.png){kind=icon}
PRE
AI
DOI：10.1007/s00477-023-02567-1.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Post-processing using deep learning algorithms can be conducted to improve accuracy of hydrologic predictions and quantify their uncertainty. In this paper, a revised version of the Local uncertainty estimation model (LUEM-R) and the circular block bootstrap (CBB) method have been used to improve the accuracy of the Variable Infiltration Capacity's (VIC) streamflow simulation and quantify its uncertainty. We used the simulated and observed streamflow at the gauge located in North CAPE Fear basin, USA from the Dayflow dataset. In the LUEM-R method, a combination of Gaussian Mixture Model and Long Short-Term Memory (LSTM) networks, which are able to capture the dependencies in time series, were used to construct the upper and lower prediction limits (PLs) for the 90% confidence level. In the CBB method, a circular block resampling technique was used to account for the dependencies in the time series (CBB-LSTM). The improved streamflow, calculated as the mean of the LSTM outputs for 200 bootstrap realizations, showed a very high correlation with observations, with coefficient of determination values of 0.97 and 0.87 for the training and testing periods, compared to the 0.77 and 0.74 values for the initial VIC simulations. For the CBB-LSTM method, the 90% PLs were constructed by fitting the best distribution at each time step, accordingly. The PLs bracketed 90% and 70% of observations in the training and testing periods, while being significantly narrower than the LUEM-R bands, which contained 92 and 91 percent of observations in the training and testing periods. Ordinary bootstrapping was also conducted using the Random Forest model (OB-RF). Comparison of the results indicates the superiority of CBB-LSTM method to the OB-RF in improving the accuracy and quantifying the uncertainties in hydrological model simulations. Overall, the CBB-LSTM was successful in improving the deterministic accuracy of VIC model simulations and quantifying its uncertainty. Also, the LUEM-R method could be efficiently utilized in quantification of uncertainty of VIC model simulations.
Keyword:
LSTM
Uncertainty
Post-process
Bootstrap
VIC model
期刊
IF:
3.6
论文数:
3.5K
被引数:
6.9K
机构
引用论文
Simulating glacier mass balance and its contribution to runoff in Northern Sweden瑞典北部冰川物质平衡模拟及其对径流的贡献.png)
JOURNAL OF HYDROLOGY
OAAI
JOURNAL OF HYDROLOGYIF6.3
OAAI分享收藏A novel attention-based LSTM cell post-processor coupled with bayesian optimization for streamflow predictionJOURNAL OF HYDROLOGY
JOURNAL OF HYDROLOGYIF6.3
PREAI
分享收藏分享收藏Hydrological modelling of karst catchment using lumped conceptual and data mining modelsJOURNAL OF HYDROLOGY
JOURNAL OF HYDROLOGYIF6.3
PREAI
分享收藏
查看更多内容