A variable parameter bidirectional stage routing model for tidal rivers with lateral inflow

Zhang, Xiaoqin; Bao, Weimin; Wenqing, Liang; Shen, Dandan

doi:10.1016/j.jhydrol.2018.07.065

Cited by 5 publications

(3 citation statements)

References 45 publications

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“…14) is applied to rectify the output so that the output is rectified to between 0 and 1 before passing to the next layer. However, LSTM is a unidirectional model, and in order to allow the model to learn bidirectionally (Zhang et al 2018;Chen et al 2014), bidirectional long short term memory (BLSTM) was developed to improve the learning ability of the model. At present, data-driven models are usually single model or single coupling model.…”

Section: Bidirectional Ensemble Learning Long Short Term Memory (Bellstm)mentioning

confidence: 99%

Multi-Model Coupling Water Demand Prediction Optimization Method for Megacities Based on Time Series Decomposition

Liu

Sang

Chang

et al. 2021

Preprint

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The fluctuation of water supply is affected by the living habits and population mobility, so the daily water supply is significantly non-stationarity, which presents a great challenge to the water demand prediction based on data-driven model. To solve this problem, the Hodrick-Prescott (HP) and wavelet transform (WT) time series decomposition methods, and ensemble learning (EL) were introduced, coupling model bidirectional long short term memory (BLSTM), seasonal autoregressive integrated moving average (SARIMA) and Gaussian radial basis function neural network (GRBFNN) were developed, and interval prediction was carried out based on student's t-test (T-test). This research method was applied to the daily water demand prediction in Shenzhen and cross-validation was performed. It is found that the decomposed subseries has obvious law, and WT is superior to HP decomposition method. However, the maximum decomposition level (MDL) of WT should not be set too high, otherwise the trend characteristics of subseries will be weakened. The results show that the potential characteristics and quantitative relationships of historical data can be learned accurately based on WT and coupling model. Although the corona virus disease 2019 (COVID-19) outbreak in 2020 caused a variation in water supply law, this variation is still within the interval prediction. The WT and coupling model satisfactorily predicted water demand and provided the lowest mean square error (0.17%), mean relative error (0.1) and mean absolute error (3.32%) and the highest Nash-Sutcliffe efficiency (97.21%) and correlation coefficient (0.99) in testing set.

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Section: Bidirectional Ensemble Learning Long Short Term Memory (Bellstm)mentioning

confidence: 99%

Multi-Model Coupling Water Demand Prediction Optimization Method for Megacities Based on Time Series Decomposition

Liu

Sang

Chang

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…However, LSTM is a unidirectional model, and the unidirectional model cannot learn the bidirectional knowledge. In order to make the model learn bidirectionally (Chen et al 2014;Zhang et al 2018), the BLSTM model is developed to improve the learning ability of the model. The BLSTM model is constructed through 5 lag time, the model structure includes bidirectional LSTM with eight-layer network structure, one-layer feedforward neural network, and one-layer rectification neural network which is used to restore the dimensionality magnified by the bidirectional propagation.…”

Section: Bidirectional Ensemble Learning Long Short Term Memory (Bellstm)mentioning

confidence: 99%

Multi-Model Coupling Water Demand Prediction Optimization Method for Megacities Based on Time Series Decomposition

Liu

Sang

Chang

et al. 2021

Water Resour Manage

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The water supply in megacities can be affected by the living habits and population mobility, so the fluctuation degree of daily water supply data is acute, which presents a great challenge to the water demand prediction. This is because that non-stationarity of daily data can have a large influence on the generalization ability of models. In this study, the Hodrick-Prescott (HP) and wavelet transform (WT) methods were used to carry out decomposition of daily data to solve the non-stationarity problem. The bidirectional long short term memory (BLSTM), seasonal autoregressive integrated moving average (SARIMA) and Gaussian radial basis function neural network (GRBFNN) were developed to carry out prediction of different subseries. The ensemble learning was introduced to improve the generalization ability of models, and prediction interval was generated based on student's t-test to cope with the variation of water supply laws. This study method was applied to the daily water demand prediction in Shenzhen and cross-validation was performed. The results show that WT is superior to HP decomposition method, but maximum decomposition level of WT should not be set too high, otherwise the trend characteristics of subseries will be weakened. Although the corona virus disease 2019 (COVID-19) outbreak caused a variation in water supply laws, this variation is still within the prediction interval. The WT and coupling models accurately predict water demand and provide the optimal mean square error (0.17%), Nash-Sutcliffe efficiency (97.21%), mean relative error (0.1), mean absolute error (3.32%), and correlation coefficient (0.99).

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“…This study explored and compared the generalization ability of different deep learning models in water demand prediction. The regular bidirectional models (Chen et al, 2014;Zhang et al, 2018) are developed to compare the influence of unidirectional and bidirectional propagation on the generalization ability of models. The zero-sum game (ZSG) (Aviram et al, 2014) is proposed to guide the model more effectively to find the optimal solution, and ensemble learning is introduced to enhance the randomness of the model so as to further enhance the generalization ability of models.…”

Section: Introductionmentioning

confidence: 99%

Water demand prediction optimization method in Shenzhen based on the zero-sum game model and rolling revisions

et al. 2021

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In this study, a deep learning model based on zero-sum game (ZSG) was proposed for accurate water demand prediction. The ensemble learning was introduced to enhance the generalization ability of models, and the sliding average was designed to solve the non-stationarity problem of time series. To solve the problem that the deep learning model could not predict water supply fluctuations caused by emergencies, a hypothesis testing method combining Student's t-test and discrete wavelet transform was proposed to generate the envelope interval of the predicted values to carry out rolling revisions. The research methods were applied to Shenzhen, a megacity with extremely short water resources. The research results showed that the regular bidirectional models were superior to the unidirectional model, and the ZSG-based bidirectional models were superior to the regular bidirectional models. The bidirectional propagation was conducive to improving the generalization ability of the model, and ZSG could better guide the model to find the optimal solution. The fluctuations in water supply were mainly caused by the floating population, but the fluctuation was still within the envelope interval of the predicted values. The predicted values after rolling revisions were very close to the measured values.

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A variable parameter bidirectional stage routing model for tidal rivers with lateral inflow

Cited by 5 publications

References 45 publications

Multi-Model Coupling Water Demand Prediction Optimization Method for Megacities Based on Time Series Decomposition

Multi-Model Coupling Water Demand Prediction Optimization Method for Megacities Based on Time Series Decomposition

Multi-Model Coupling Water Demand Prediction Optimization Method for Megacities Based on Time Series Decomposition

Water demand prediction optimization method in Shenzhen based on the zero-sum game model and rolling revisions

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