Mid- to long-term runoff prediction by combining the deep belief network and partial least-squares regression

Yue, Zhaoxin; Ai, Ping; Xiong, Chuansheng; Hong, Min; Song, Yanhong

doi:10.2166/hydro.2020.022

Cited by 17 publications

(10 citation statements)

References 28 publications

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“…But there is no single standard to determine which evaluation metric is the most accurate assessment method. Therefore, to estimate the performance of the proposed model, the evaluation metrics, such as bias index (BIAS) (Najafzadeh & Sattar 2015;Barzkar et al 2021), scatter index (SI) (Najafzadeh et al 2020), mean absolute percentage error (MAPE), root-mean-square error (RMSE), mean absolute error (MAE) and deterministic coefficient (DC) (Yue et al 2020a(Yue et al , 2020b, are applied. Among them, four common evaluation metrics are adopted in this paper, including MAE, MAPE, RMSE and DC.…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…With the development of national economy and the adjustment of national water control policy, the gap between the existing hydrologic medium-and long-term runoff forecasting methods and the demand for production and application have been further widened. In addition, due to the increasing amount of hydrological data, it can introduce redundant and noisy information to the prediction feature or factor, which may deteriorate the performance of the mid-to long-term runoff prediction (Yue et al 2020a). Therefore, runoff prediction is not only a management issue but also a scientific problem, and accurate runoff forecasts will be of vital importance to the policymakers.…”

Section: Graphical Abstract Introductionmentioning

confidence: 99%

“…However, the determination of a suitable set of inputs is challenging due to the time lag of runoff response to the influence of large-scale atmospheric circulation (Cheng et al 2019). The existing studies on the lag selection of runoff factors are mainly carried out for 1 or 2 years (Gao 2006;Yang et al 2013;Li et al 2019;Yue et al 2020a). It is generally known that it is difficult to accurately determine the lag period since runoff flows through different field conditions to reach a point (He et al 2011).…”

Section: Graphical Abstract Introductionmentioning

confidence: 99%

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A novel medium- and long-term runoff combined forecasting model based on different lag periods

Song

Xiong

et al. 2022

Journal of Hydroinformatics

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The accuracy of medium- and long-term runoff forecasting plays a signiﬁcant role in several applications involving the management of hydrological resources, such as power generation, water supply and ﬂood mitigation. Numerous studies that adopted combined forecasting models to enhance runoff forecasting accuracy have been proposed. Nevertheless, some models do not take into account the effects of different lag periods on the selection of input factors. Based on this, this paper proposed a novel medium- and long-term runoff combined forecasting model based on different lag periods. In this approach, the factors are initially selected by the time-delay correlation analysis method of different lag periods and further screened with stepwise regression analysis. Next, an extreme learning machine (ELM) is adopted to integrate each result obtained from the three single models, including multiple linear regression (MLR), feed-forward back propagation-neural network (FFBP-NN) and support vector regression (SVR), which is optimized by particle swarm optimization (PSO). To verify the effectiveness and versatility of the proposed combined model, the Lianghekou and Jinping hydrological stations from the Yalong River basin, China, are utilized as case studies. The experimental results indicate that compared with MLR, FFBP-NN, SVR and ridge regression (RR), the proposed combined model can better improve the accuracy of medium- and long-term runoff forecasting in the statistical indices of MAE, MAPE, RMSE, DC, U95 and reliability.

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Section: Evaluation Metricsmentioning

confidence: 99%

Section: Graphical Abstract Introductionmentioning

confidence: 99%

Section: Graphical Abstract Introductionmentioning

confidence: 99%

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A novel medium- and long-term runoff combined forecasting model based on different lag periods

Song

Xiong

et al. 2022

Journal of Hydroinformatics

Self Cite

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“…While many hydrologic models have been developed over the past 50 years, the challenge of providing streamflow forecasts accurately, efficiently and everywhere at all times remains. Several studies have applied deep learning in water resources fields, including surface water quality (Hu et al, 2019;Zhou, 2020), streamflow forecasting (Feng et al, 2020;Li et al, 2020;Qian et al, 2020;Sarkar et al, 2020;Van et al, 2020;Yue et al, 2020), soil moisture (Fang and Shen, 2020), groundwater (Wang et al, 2020;Yu et al, 2020), hydrometeorology (Chen et al, 2020;Lee et al, 2020), and water management (Liu et al, 2019). Recent studies (Chang et al, 2015;Granata et al, 2016;Faruk, 2010;Sit and Demir, 2019) have shown that many machine learning and deep learning models could be valuable in streamflow forecasting.…”

Section: Introductionmentioning

confidence: 99%

A Regional Semi-Distributed Streamflow Model Using Deep Learning

Xiang¹,

Demir²,

Mantilla³

et al. 2021

Preprint

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Recent studies have shown that deep learning models in hydrological applications significantly improved streamflow predictions at multiple stations compared to traditional machine learning approaches. However, most studies lack generalization; i.e. researchers are training separate models for each location. The spatial and temporal generalization ability of deep learning models in hydrology that can be gained by training a single model for multiple stations is evaluated in this study. We developed a generalized model with a multi-site structure for hourly streamflow hindcasts on 125 USGS gauges. Considering watershed-scale features including drainage area, time of concentration, slope, and soil types, the proposed models have acceptable performance and slightly higher median NSE value than training individual models for each USGS station. Furthermore, we showed that the trained generalized model can be applied to any new gauge in the state of Iowa that was not used in the training set with acceptable accuracy. This study demonstrates the potential of deep learning studies in hydrology where more domain knowledge and physical features can support further generalization.

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“…Due to the influence of precipitation, underlying surface conditions, evapotranspiration and other factors, runoff series yield instability, time-varying and randomness, so making it difficult to accurately predict runoff. In order to remedy this problem, many models, such as physically-based models and databased models, have been developed and reported in the relevant literature (Chen et al 2020a;Chen et al 2020b;Yue et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Evaluating The Performance of Several Data Preprocessing Methods Based On GRU in Forecasting Monthly Runoff Time Series

Wang¹,

Du²,

Chau³

et al. 2021

Preprint

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The optimal planning and management of modern water resources depends highly on reliable and accurate runoff forecasting. Data preprocessing technology can provide new possibilities for improving the accuracy of runoff forecasting, when basic physical relationships cannot be captured using a single prediction model. Yet, few researches evaluated the performances of various data preprocessing technology in predicting monthly runoff time series so far. In order to fill this research gap, this paper investigates the potential of five data preprocessing techniques based on gated recurrent unit network (GRU) model in monthly runoff prediction, namely variational mode decomposition (VMD), wavelet packet decomposition (WPD), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), extreme-point symmetric mode decomposition (ESMD), and singular spectrum analysis (SSA). In this study, the original monthly runoff data is first decomposed into a set of subcomponents using five data preprocessing methods; second, each component is predicted by developing an appropriate GRU model; finally, the forecasting results of different two-stage hybrid models are obtained by aggregating of forecast results of the corresponding subcomponents. Four performance metrics are employed as the evaluation benchmarks. The experimental results from two hydropower stations in China show that five data preprocessing techniques can attain satisfying prediction results, while VMD and WPD methods can yield better performance than CEEMDAN, ESMD and SSA in both training and testing periods in terms of four indexes. Indeed, it is significantly important to carefully determine an appropriate data preprocessing method according to the actual characteristics of the study area.

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Mid- to long-term runoff prediction by combining the deep belief network and partial least-squares regression

Cited by 17 publications

References 28 publications

A novel medium- and long-term runoff combined forecasting model based on different lag periods

A novel medium- and long-term runoff combined forecasting model based on different lag periods

A Regional Semi-Distributed Streamflow Model Using Deep Learning

Evaluating The Performance of Several Data Preprocessing Methods Based On GRU in Forecasting Monthly Runoff Time Series

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