scholarly journals A system-theory-based model for monthly river runoff forecasting: model calibration and optimization

2014 ◽  
Vol 62 (1) ◽  
pp. 82-88 ◽  
Author(s):  
Jianhua Wu ◽  
Hui Qian ◽  
Peiyue Li ◽  
Yanxun Song
Keyword(s):  
System Theory ◽  
Frequency Analysis ◽  
River Runoff ◽  
Optimization Technique ◽  
Essential Element ◽  
Forecasting Model ◽  
Observation Data ◽  
Prediction Errors ◽  
Groundwater Exploitation ◽  
Runoff Forecasting

Abstract River runoff is not only a crucial part of the global water cycle, but it is also an important source for hydropower and an essential element of water balance. This study presents a system-theory-based model for river runoff forecasting taking the Hailiutu River as a case study. The forecasting model, designed for the Hailiutu watershed, was calibrated and verified by long-term precipitation observation data and groundwater exploitation data from the study area. Additionally, frequency analysis, taken as an optimization technique, was applied to improve prediction accuracy. Following model optimization, the overall relative prediction errors are below 10%. The system-theory-based prediction model is applicable to river runoff forecasting, and following optimization by frequency analysis, the prediction error is acceptable.

The importance of short lag-time in the runoff forecasting model based on long short-term memory

2020 ◽  
Vol 589 ◽  
pp. 125359
Author(s):  
Xi Chen ◽  
Jiaxu Huang ◽  
Zhen Han ◽  
Hongkai Gao ◽  
Min Liu ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Lag Time ◽  
Forecasting Model ◽  
Short Term ◽  
Term Memory ◽  
Model Based ◽  
Runoff Forecasting ◽  
Long Short Term Memory

scholarly journals A snowmelt runoff forecasting model coupling WRF and DHSVM

2009 ◽  
Vol 13 (10) ◽  
pp. 1897-1906 ◽  
Author(s):  
Q. Zhao ◽  
Z. Liu ◽  
B. Ye ◽  
Y. Qin ◽  
Z. Wei ◽  
...  
Keyword(s):  
Weather Forecasting ◽  
Atmospheric Model ◽  
Forecasting Model ◽  
Tianshan Mountain ◽  
Mountain Range ◽  
Snowmelt Runoff ◽  
Runoff Simulation ◽  
Limited Region ◽  
Runoff Forecasting ◽  
Forecasting System

Abstract. This study linked the Weather Research and Forecasting (WRF) modelling system and the Distributed Hydrology Soil Vegetation Model (DHSVM) to forecast snowmelt runoff. The study area was the 800 km2 Juntanghu watershed of the northern slopes of Tianshan Mountain Range. This paper investigated snowmelt runoff forecasting models suitable for meso-microscale application. In this study, a limited-region 24-h Numeric Weather Forecasting System was formulated using the new generation atmospheric model system WRF with the initial fields and lateral boundaries forced by Chinese T213L31 model. Using the WRF forecasts, the DHSVM hydrological model was used to predict 24 h snowmelt runoff at the outlet of the Juntanghu watershed. Forecasted results showed a good similarity to the observed data, and the average relative error of maximum runoff simulation was less than 15%. The results demonstrate the potential of using a meso-microscale snowmelt runoff forecasting model for forecasting floods. The model provides a longer forecast period compared with traditional models such as those based on rain gauges or statistical forecasting.

Study on the second grey relational grade and its properties

Kybernetes ◽  
2010 ◽  
Vol 39 (8) ◽  
pp. 1330-1335 ◽  
Author(s):  
Yan Ma
Keyword(s):  
System Theory ◽  
Design Methodology ◽  
Grey System Theory ◽  
Forecasting Model ◽  
Grey System ◽  
Content Type ◽  
Grey Forecasting ◽  
Grey Relational Grade ◽  
Grey Relational ◽  
Practical Implications

PurposeThe purpose of this paper is to propose a second relational grade based on the general grey relational grade and analyze several of its properties.Design/methodology/approachGrey system theory. The paper proposes and studies second grey relational grade, establishes second grey relational formula, and studies several characteristics of second grey relational formula.FindingsProposing a second relational grade proved it could solve the problem of the parallelism partly and weaken relativity of space position.Research limitations/implicationsUntil now, the problem of the consistency could not be solved, nor could the problem of the effect which keeps the sequence the same.Practical implicationsThe precision of the grey forecasting model could be strengthened if used in the forecasting model.Originality/valueThe general relational grade only thinks over the relation between two sequences but does not involve the relation in one sequence. The second relational grade considers these two, so if the forecasting model is established with it, the model should be more exact.

Modifying Wind Speed Data of Manual Observation Sequence to Automatic Observation Sequence Using a PSO-Based Neural Network

2011 ◽  
Vol 187 ◽  
pp. 688-692
Author(s):  
Xia Xiao ◽  
Hong Chao Zuo ◽  
Wen Yu Zhang ◽  
Ju Jie Wang
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Optimization Technique ◽  
Weather Data ◽  
Percentage Error ◽  
Observation Data ◽  
Data Sequence ◽  
Wind Speed Data ◽  
Observation Sequence ◽  
Particle Swarm Optimization Technique

Recently, manual observation sequence has been gradually replaced by automatic observation sequence. The difference between manual observation sequence and automatic observation sequence is somewhat inevitable. This challenges the the homogeneity and the continuity of historical weather data, and influences atmospheric researches and applications. Therefore, based on the understanding of the influence caused by the two observation sequences, how to modify the data sequence of manual observation to automatic observation sequence has become a problem. In this paper, a model, which is a neural network based on the particle swarm optimization technique (PSONN), is established to modify the wind speed data sequence from manual observation to automatic observation. The proposed model achieves 15.6% in mean absolute percentage error (MAPE) compared to manual observation data sequence. For wind speed, it could be a promising candidate for modifying manual observing data sequence to automatic observing data sequence.

Research on Predictive Model of Gas Concentration for Driving Ventilation in Coal Mine Based on RBF and BP

2013 ◽  
Vol 706-708 ◽  
pp. 1805-1809
Author(s):  
Xiao Yan Gong ◽  
Jun Guo ◽  
He Xue ◽  
Dong Hui Yan ◽  
Zhe Wu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Coal Mine ◽  
Rbf Neural Network ◽  
Coal Mines ◽  
Observation Data ◽  
Prediction Errors ◽  
Application Research ◽  
Gas Concentration ◽  
Mine Sites

In order to predict accurately gas concentration and design ventilation scheme in driving ventilation process under different gas emission in coal mine, based on the analysis of various ventilation factors, the prediction model structure of gas concentration for driving ventilation was designed based on RBF and BP neural network in this paper. Then MATLAB software and the observation data obtained from the coal mine sites were used to compare and analyze the prediction errors of two models, and a RBF neural network model with higher prediction precision was obtained. After that, the prediction model was used for practical application research on the gas concentration of the heading face in concrete coal mines. The research shows that the settled prediction model can not only predict the gas concentration precisely of driving ventilation, but also provide a certain theory basis for different driving ventilation equipment layout and parameters configuration in the driving ventilation process of coal mines.

scholarly journals 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

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.

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

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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