Real-time error correction method combined with combination flood forecasting technique for improving the accuracy of flood forecasting

Chen, Lu; Zhang, Yongchuan; Zhou, Jianzhong; Singh, Vijay P.; Guo, Shenglian; Zhang, Mengjie

doi:10.1016/j.jhydrol.2014.11.053

Cited by 60 publications

(23 citation statements)

References 25 publications

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“…We notice that the upper Yangtze River' tributaries have notoriously complex hydro-geological characteristics. Plenty of studies were devoted to developing reliable and accurate short-term (less than 24-h ahead) flood forecast models for the Yangtze River due to the extremely non-linear relationship between rainfall and runoff over this basin during storm events [50,51]. Besides, a small improvement in the reliability and accuracy of short-term flood forecasts could be critical and beneficial to flood prevention as well as the dynamic management of the TGR.…”

Section: Evaluation Indicatorsmentioning

confidence: 99%

Improving the Reliability of Probabilistic Multi-Step-Ahead Flood Forecasting by Fusing Unscented Kalman Filter with Recurrent Neural Network

Zhou

Guo

et al. 2020

Water

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It is fundamentally challenging to quantify the uncertainty of data-driven flood forecasting. This study introduces a general framework for probabilistic flood forecasting conditional on point forecasts. We adopt an unscented Kalman filter (UKF) post-processing technique to model the point forecasts made by a recurrent neural network and their corresponding observations. The methodology is tested by using a long-term 6-h timescale inflow series of the Three Gorges Reservoir in China. The main merits of the proposed approach lie in: first, overcoming the under-prediction phenomena in data-driven flood forecasting; second, alleviating the uncertainty encountered in data-driven flood forecasting. Two commonly used artificial neural networks, a recurrent and a static neural network, were used to make the point forecasts. Then the UKF approach driven by the point forecasts demonstrated its competency in increasing the reliability of probabilistic flood forecasts significantly, where predictive distributions encountered in multi-step-ahead flood forecasts were effectively reduced to small ranges. The results demonstrated that the UKF plus recurrent neural network approach could suitably extract the complex non-linear dependence structure between the model’s outputs and observed inflows and overcome the systematic error so that model reliability as well as forecast accuracy for future horizons could be significantly improved.

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Section: Evaluation Indicatorsmentioning

confidence: 99%

Improving the Reliability of Probabilistic Multi-Step-Ahead Flood Forecasting by Fusing Unscented Kalman Filter with Recurrent Neural Network

Zhou

Guo

et al. 2020

Water

Self Cite

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“…Therefore, we coupled these criterions to examine the descriptive strength of PD S and hence choose the most reliable marginal distribution. RMSE and AIC are defined by the following equations [37,38]…”

Section: Selection Of Marginal Distributionmentioning

confidence: 99%

Deriving Design Flood Hydrographs Based on Copula Function: A Case Study in Pakistan

Rizwan

Guo

Yin

et al. 2019

Water

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Flood events are characterized by flood peaks and volumes that can be mutually constructed using a copula function. The Indus basin system of Pakistan is periodically threatened by floods during monsoon seasons and thus causes huge losses to infrastructure as well as the community and economy. The design flood hydrograph (DFH) of suitable magnitude and degree is imperative for sheltering dams against the flood risk. The hydrological pair of flood peak and volume is required to be defined using a multivariate analysis method. In this paper, the joint probability function of the hydrological pair is employed to derive the DFH in the Indus basin system of Pakistan. Firstly, we compared the fitting performance of different probability distributions (PDs) as a marginal distribution. Next, we compared the Archimedean family of copulas to construct the bivariate joint distribution of flood peak and volume. Later, the equal frequency combination (EFC) method and most likely combination (MLC) method using "OR" joint return period (JRP or ), was involved to derive the design flood quantiles. Finally, we derived the DFH using the two combination methods based on Gumbel-Hougaard copula for different return periods. We presented the combination methods for updating the shape of the DFH in Pakistan. Our study will contribute towards the improvement of design standards of dams and environmental recovery in Pakistan.

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“…The output/ error assimilation methods treat a streamflow forecast as a pure model output and update it by adding errors calculated with another independent procedure or model. Such procedures/models could either be nonlinear, such as artificial neural networks (ANNs; Anctil et al 2003), or linear, such as autoregressive-moving-average models (Broersen 2007, Chen et al 2015. Output/error assimilation of streamflow is relatively simple to implement, as there is no feedback to the original RR model from the manipulation of model outputs.…”

Section: Hydrological Da Targetsmentioning

confidence: 99%

Review of the Kalman-type hydrological data assimilation

Sun

Seidou

Nistor

et al. 2016

Hydrological Sciences Journal

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There is great potential in Data Assimilation (DA) for the purposes of uncertainty identification, reduction and real-time correction of hydrological models. This paper reviews the latest developments in Kalman filters (KFs), particularly the Extended KF (EKF) and the Ensemble KF (EnKF) in hydrological DA. The hydrological DA targets, methodologies and their applicability are examined. The recent applications of the EKF and EnKF in hydrological DA are summarized and assessed critically. Furthermore, this review highlights the existing challenges in the implementation of the EKF and EnKF, especially error determination and joint parameter estimation. A detailed review of these issues would benefit not only the Kalmantype DA but also provide an important reference to other hydrological DA types.

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Real-time error correction method combined with combination flood forecasting technique for improving the accuracy of flood forecasting

Cited by 60 publications

References 25 publications

Improving the Reliability of Probabilistic Multi-Step-Ahead Flood Forecasting by Fusing Unscented Kalman Filter with Recurrent Neural Network

Improving the Reliability of Probabilistic Multi-Step-Ahead Flood Forecasting by Fusing Unscented Kalman Filter with Recurrent Neural Network

Deriving Design Flood Hydrographs Based on Copula Function: A Case Study in Pakistan

Review of the Kalman-type hydrological data assimilation

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