A Novel Runoff Forecasting Model Based on the Decomposition-Integration-Prediction Framework

Runoff forecasting is of great importance for flood mitigation and power generation plan preparation. To explore the better application of time-frequency decomposition technology in runoff forecasting and improve the prediction accuracy, this research has developed a framework of runoff forecasting named Decomposition-Integration-Prediction (DIP) using parallel-input neural network, and proposed a novel runoff forecasting model with Variational Mode Decomposition (VMD), Gated Recurrent Unit (GRU), and Stochastic Fractal Search (SFS) algorithm under this framework. In this model, the observed runoff series is first decomposed into several sub-series via the VMD method to extract different frequency information. Secondly, the parallel layers in the parallel-input neural network based on GRU are trained to receive the input samples of each subcomponent and integrate their output adaptively through the concatenation layers. Finally, the output of concatenation layers is treated as the final runoff forecasting result. In this process, the SFS algorithm was adopted to optimize the structure of the neural network. The prediction performance of the proposed model was evaluated using the historical monthly runoff data at Pingshan and Yichang hydrological stations in the Upper Yangtze River Basin of China, and seven various single and decomposition-based hybrid models were developed for comparison. The results show that the proposed model has obvious advantages in overall prediction performance, model training time, and multi-step-ahead prediction compared to several comparative methods, which is a reasonable and more efficient monthly runoff forecasting method based on time series decomposition and neural networks.

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A Compound Approach for Monthly Runoff Forecasting Based on Multiscale Analysis and Deep Network with Sequential Structure

Water ◽

10.3390/w12082274 ◽

2020 ◽

Vol 12 (8) ◽

pp. 2274 ◽

Cited By ~ 2

Author(s):

Shi Chen ◽

Shuning Dong ◽

Zhiguo Cao ◽

Junting Guo

Keyword(s):

Water Resource Management ◽

Sequential Structure ◽

Runoff Forecasting ◽

Mode Decomposition ◽

Proposed Model ◽

Runoff Series ◽

Efficiency And Effectiveness ◽

Predicted Values ◽

Monthly Runoff ◽

Gated Recurrent Unit

Accurate runoff forecasting is of great significance for the optimization of water resource management and regulation. Given such a challenge, a novel compound approach combining time-varying filtering-based empirical mode decomposition (TVFEMD), sample entropy (SE)-based subseries recombination, and the newly developed deep sequential structure incorporating convolutional neural network (CNN) into a gated recurrent unit network (GRU) is proposed for monthly runoff forecasting. Firstly, the runoff series is disintegrated into a collection of subseries adopting TVFEMD, considering the volatility of runoff series caused by complex environmental and human factors. The subseries recombination strategy based on SE and recombination criterion is employed to reconstruct the subseries possessing the approximate complexity. Subsequently, the newly developed deep sequential structure based on CNN and GRU (CNNGRU) is applied to predict all the preprocessed subseries. Eventually, the predicted values obtained above are aggregated to deduce the ultimate prediction results. To testify to the efficiency and effectiveness of the proposed approach, eight relevant contrastive models were applied to the monthly runoff series collected from Baishan reservoir, where the experimental results demonstrated that the evaluation metrics obtained by the proposed model achieved an average index decrease of 44.35% compared with all the contrast models.

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Recurrent neural networks with long term temporal dependencies in machine tool wear diagnosis and prognosis

SN Applied Sciences ◽

10.1007/s42452-021-04427-5 ◽

2021 ◽

Vol 3 (4) ◽

Author(s):

Jianlei Zhang ◽

Yukun Zeng ◽

Binil Starly

Keyword(s):

Neural Network ◽

Tool Wear ◽

Machine Tool ◽

Recurrent Neural Network ◽

Machine Tools ◽

Prediction Performance ◽

Sequential Data ◽

Diagnosis And Prognosis ◽

Proposed Model

AbstractData-driven approaches for machine tool wear diagnosis and prognosis are gaining attention in the past few years. The goal of our study is to advance the adaptability, flexibility, prediction performance, and prediction horizon for online monitoring and prediction. This paper proposes the use of a recent deep learning method, based on Gated Recurrent Neural Network architecture, including Long Short Term Memory (LSTM), which try to captures long-term dependencies than regular Recurrent Neural Network method for modeling sequential data, and also the mechanism to realize the online diagnosis and prognosis and remaining useful life (RUL) prediction with indirect measurement collected during the manufacturing process. Existing models are usually tool-specific and can hardly be generalized to other scenarios such as for different tools or operating environments. Different from current methods, the proposed model requires no prior knowledge about the system and thus can be generalized to different scenarios and machine tools. With inherent memory units, the proposed model can also capture long-term dependencies while learning from sequential data such as those collected by condition monitoring sensors, which means it can be accommodated to machine tools with varying life and increase the prediction performance. To prove the validity of the proposed approach, we conducted multiple experiments on a milling machine cutting tool and applied the model for online diagnosis and RUL prediction. Without loss of generality, we incorporate a system transition function and system observation function into the neural net and trained it with signal data from a minimally intrusive vibration sensor. The experiment results showed that our LSTM-based model achieved the best overall accuracy among other methods, with a minimal Mean Square Error (MSE) for tool wear prediction and RUL prediction respectively.

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A Novel Method for Intelligent Fault Diagnosis of Bearing Based on Capsule Neural Network

Complexity ◽

10.1155/2019/6943234 ◽

2019 ◽

Vol 2019 ◽

pp. 1-17 ◽

Cited By ~ 33

Author(s):

Zhijian Wang ◽

Likang Zheng ◽

Wenhua Du ◽

Wenan Cai ◽

Jie Zhou ◽

...

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Weight Coefficient ◽

Convergence Condition ◽

Intelligent Fault Diagnosis ◽

Wind Turbine Gearbox ◽

Time Frequency ◽

Proposed Model ◽

Novel Method ◽

Relationship Of

In the era of big data, data-driven methods mainly based on deep learning have been widely used in the field of intelligent fault diagnosis. Traditional neural networks tend to be more subjective when classifying fault time-frequency graphs, such as pooling layer, and ignore the location relationship of features. The newly proposed neural network named capsules network takes into account the size and location of the image. Inspired by this, capsules network combined with the Xception module (XCN) is applied in intelligent fault diagnosis, so as to improve the classification accuracy of intelligent fault diagnosis. Firstly, the fault time-frequency graphs are obtained by wavelet time-frequency analysis. Then the time-frequency graphs data which are adjusted the pixel size are input into XCN for training. In order to accelerate the learning rate, the parameters which have bigger change are punished by cost function in the process of training. After the operation of dynamic routing, the length of the capsule is used to classify the types of faults and get the classification of loss. Then the longest capsule is used to reconstruct fault time-frequency graphs which are used to measure the reconstruction of loss. In order to determine the convergence condition, the three losses are combined through the weight coefficient. Finally, the proposed model and the traditional methods are, respectively, trained and tested under laboratory conditions and actual wind turbine gearbox conditions to verify the classification ability and reliable ability.

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Development of a deep neural network for predicting 6-hour average PM<sub>2.5</sub> concentrations up to two subsequent days using various training data

10.5194/gmd-2021-356 ◽

2021 ◽

Author(s):

Jeong-Beom Lee ◽

Jae-Bum Lee ◽

Youn-Seo Koo ◽

Hee-Yong Kwon ◽

Min-Hyeok Choi ◽

...

Keyword(s):

Neural Network ◽

Air Quality ◽

Deep Neural Network ◽

Model Performance ◽

Prediction Performance ◽

Training Data ◽

Forecasting Model ◽

Observation Data ◽

Forecast Data ◽

Air Quality Forecasting

Abstract. This study aims to develop a deep neural network (DNN) model as an artificial neural network (ANN) for the prediction of 6-hour average fine particulate matter (PM2.5) concentrations for a three-day period—the day of prediction (D+0), one day after prediction (D+1) and two days after prediction (D+2)—using observation data and forecast data obtained via numerical models. The performance of the DNN model was comparatively evaluated against that of the currently operational Community Multiscale Air Quality (CMAQ) modelling system for air quality forecasting in South Korea. In addition, the effect on predictive performance of the DNN model on using different training data was analyzed. For the D+0 forecast, the DNN model performance was superior to that of the CMAQ model, and there was no significant dependence on the training data. For the D+1 and D+2 forecasts, the DNN model that used the observation and forecast data (DNN-ALL) outperformed the CMAQ model. The root-mean-squared error (RMSE) of DNN-ALL was lower than that of the CMAQ model by 2.2 μgm−3, and 3.0 μgm−3 for the D+1 and D+2 forecasts, respectively, because the overprediction of higher concentrations was curtailed. An IOA increase of 0.46 for D+1 prediction and 0.59 for the D+2 prediction was observed in case of the DNN-ALL model compared to the IOA of the DNN model that used only observation data (DNN-OBS). In additionally, An RMSE decrease of 7.2 μgm−3 for the D+1 prediction and 6.3 μgm−3 for the D+2 prediction was observed in case of the DNN-ALL model, compared to the RMSE of DNN-OBS, indicating that the inclusion of forecast data in the training data greatly affected the DNN model performance. Considering the prediction of the 6-hour average PM2.5 concentration, the 8.8 μgm−3 RMSE of the DNN-ALL model was 2.7 μgm−3 lower than that of the CMAQ model, indicating the superior prediction performance of the former. These results suggest that the DNN model could be utilized as a better-performing air quality forecasting model than the CMAQ, and that observation data plays an important role in determining the prediction performance of the DNN model for D+0 forecasting, while prediction data does the same for D+1 and D+2 forecasting. The use of the proposed DNN model as a forecasting model may result in a reduction in the economic losses caused by pollution-mitigation policies and aid better protection of public health.

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Runoff forecasting model based on variational mode decomposition and artificial neural networks

Mathematical Biosciences and Engineering ◽

10.3934/mbe.2022076 ◽

2021 ◽

Vol 19 (2) ◽

pp. 1633-1648

Author(s):

Xin Jing ◽

◽

Jungang Luo ◽

Shangyao Zhang ◽

Na Wei

Keyword(s):

Neural Networks ◽

Water Resource Management ◽

Short Term Memory ◽

Forecasting Model ◽

Variational Mode Decomposition ◽

Model Based ◽

Runoff Forecasting ◽

Mode Decomposition ◽

Runoff Series ◽

Correlation Information

<abstract> <p>Accurate runoff forecasting plays a vital role in water resource management. Therefore, various forecasting models have been proposed in the literature. Among them, the decomposition-based models have proved their superiority in runoff series forecasting. However, most of the models simulate each decomposition sub-signals separately without considering the potential correlation information. A neoteric hybrid runoff forecasting model based on variational mode decomposition (VMD), convolution neural networks (CNN), and long short-term memory (LSTM) called VMD-CNN-LSTM, is proposed to improve the runoff forecasting performance further. The two-dimensional matrix containing both the time delay and correlation information among sub-signals decomposing by VMD is firstly applied to the CNN. The feature of the input matrix is then extracted by CNN and delivered to LSTM with more potential information. The experiment performed on monthly runoff data investigated from Huaxian and Xianyang hydrological stations at Wei River, China, demonstrates the VMD-superiority of CNN-LSTM to the baseline models, and robustness and stability of the forecasting of the VMD-CNN-LSTM for different leading times.</p> </abstract>

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Short-Term Gas Load Forecasting Based on Wavelet BP Neural Network Optimized by Genetic Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.631-632.79 ◽

2014 ◽

Vol 631-632 ◽

pp. 79-85 ◽

Cited By ~ 2

Author(s):

Feng Yu ◽

Zhi Qing Wang ◽

Xiao Zhong Xu

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Bp Neural Network ◽

Load Forecasting ◽

Learning Ability ◽

Time Frequency ◽

Proposed Model ◽

Real Coded Genetic Algorithm ◽

Self Learning ◽

Single Neural Network

Aiming at the limitations of a single neural network for effective gas load forecasting, a combinational model based on wavelet BP neural network optimized by genetic algorithm is proposed. The problems that traditional BP algorithm converges slowly and easily falls into local minimum are overcame. The wavelet neural network strengthens the function approximation capacity of the network by combining the well time-frequency local feature of wavelet transform with the self-learning ability of neural network. And optimized by the real coded genetic algorithm, the network converges more quick than non-optimized one. This proposed model is applied to daily gas load forecasting for Shanghai and the simulation results indicate that this algorithm has excellent prediction effect.

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A Forecasting Model for Patient Arrivals of an Emergency Department in Healthcare Management Systems

Advances in Healthcare Information Systems and Administration - Intelligent Systems for Healthcare Management and Delivery ◽

10.4018/978-1-5225-7071-4.ch012 ◽

2019 ◽

pp. 266-284 ◽

Cited By ~ 1

Author(s):

Melih Yucesan ◽

Muhammet Gul ◽

Suleyman Mete ◽

Erkan Celik

Keyword(s):

Neural Network ◽

Human Resources ◽

Medical Devices ◽

Moving Average ◽

Healthcare Management ◽

Management Systems ◽

Forecasting Model ◽

Autoregressive Integrated Moving Average ◽

Proposed Model ◽

Artificial Neural Network Ann

Emergency departments (EDs) are one of the most valuable departments of healthcare management systems. Patient arrivals at the EDs are crucial for planning of the future. Accurate forecasting of patient arrivals contributes to better organized human resources and medical devices in the EDs. Therefore, in this chapter, the authors aim to develop a hybrid model including the methods of autoregressive integrated moving average with external variables (ARIMAX) and artificial neural network (ANN) in a hospital ED. The arrival data was collected from the hospital information system of a public hospital in eastern Turkey. The model incorporates factors related to ED arrivals such as climatic and calendar variables. By the aid of the proposed model, an insight to arrangement and planning of ED resources can be provided in a better way.

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Estimating Parameters of SAR Model by Recursive Least Square Method

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.518-523.4171 ◽

2012 ◽

Vol 518-523 ◽

pp. 4171-4176

Author(s):

Meng Cheng ◽

Jo Song Guk ◽

Jin Wen Wang

Keyword(s):

Autoregressive Model ◽

Least Square Method ◽

Parameters Estimation ◽

Least Square ◽

Recursive Least Square ◽

Moment Estimation ◽

Runoff Forecasting ◽

Runoff Series ◽

Monthly Runoff ◽

Recursive Least Square Method

Seasonal runoff series can be described by seasonal autoregressive model, which is extensively applied in long-term runoff forecasting. The common way of parameters estimation is moment estimation. This paper estimates parameters of seasonal autoregressive model by recursive least square method and applies the method in forecasting the monthly runoff for the Three Gorges. An effective procedure based upon the least fitting error is proposed to determine the model order. The forecasting results are satisfactory.

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Annual Runoff Forecasting Based on Multi-Model Information Fusion and Residual Error Correction in the Ganjiang River Basin

Water ◽

10.3390/w12082086 ◽

2020 ◽

Vol 12 (8) ◽

pp. 2086

Author(s):

Peibing Song ◽

Weifeng Liu ◽

Jiahui Sun ◽

Chao Wang ◽

Lingzhong Kong ◽

...

Keyword(s):

Neural Network ◽

Time Series ◽

Error Correction ◽

River Basin ◽

Information Fusion ◽

Principal Components ◽

Annual Runoff ◽

Forecasting Model ◽

Runoff Forecasting ◽

Ganjiang River Basin

Accurate forecasting of annual runoff time series is of great significance for water resources planning and management. However, considering that the number of forecasting factors is numerous, a single forecasting model has certain limitations and a runoff time series consists of complex nonlinear and nonstationary characteristics, which make the runoff forecasting difficult. Aimed at improving the prediction accuracy of annual runoff time series, the principal components analysis (PCA) method is adopted to reduce the complexity of forecasting factors, and a modified coupling forecasting model based on multiple linear regression (MLR), back propagation neural network (BPNN), Elman neural network (ENN), and particle swarm optimization-support vector machine for regression (PSO-SVR) is proposed and applied in the Dongbei Hydrological Station in the Ganjiang River Basin. Firstly, from two conventional factors (i.e., rainfall, runoff) and 130 atmospheric circulation indexes (i.e., 88 atmospheric circulation indexes, 26 sea temperature indexes, 16 other indexes), principal components generated by linear mapping are screened as forecasting factors. Then, based on above forecasting factors, four forecasting models including MLR, BPNN, ENN, and PSO-SVR are developed to predict annual runoff time series. Subsequently, a coupling model composed of BPNN, ENN, and PSO-SVR is constructed by means of a multi-model information fusion taking three hydrological years (i.e., wet year, normal year, dry year) into consideration. Finally, according to residual error correction, a modified coupling forecasting model is introduced so as to further improve the accuracy of the predicted annual runoff time series in the verification period.

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Wavelet ANN Based Monthly Runoff Forecast

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.421.803 ◽

2013 ◽

Vol 421 ◽

pp. 803-807

Author(s):

Hui Jun Xu

Keyword(s):

Neural Network ◽

Jiangxi Province ◽

Good Precision ◽

Hydro Power ◽

Runoff Series ◽

Inflow Forecasting ◽

Runoff Forecast ◽

Monthly Runoff ◽

Different Time Scales

A wavelet artificial neural network to forecasting monthly runoff is proposed. The monthly runoff series is firstly decomposed to sub-series on different time scales, and each sub-series is modeled. The weights of the network are replaced by wavelet functions and are corrected by conjugate gradient method in the training iteration. Then the proposed network is trained with 49 years (1952-2000) actual data of one hydro power plant of Jiangxi province and is tested for target year (2001-2003). Finally, some actual results for mid and long term water inflow forecasting are obtained and which show the proposed method has a good precision for forecasting.

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