Long-term runoff forecast using BP neural network based on climatic factors and mutual information method in the Qujiang Catchment of China

2020 ◽  
Author(s):  
Huihui Dai
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Mutual Information ◽  
Bp Neural Network ◽  
Climatic Factors ◽  
Principal Component ◽  
Component Analysis ◽  
Information Method ◽  
Runoff Forecast

<p>The formation of runoff is extremely complicated, and it is not good enough to forecast the future runoff only by using the previous runoff or meteorological data. In order to improve the forecast precision of the medium and long-term runoff forecast model, a set of forecast factor group is selected from meteorological factors, such as rainfall, temperature, air pressure and the circulation factors released by the National Meteorological Center  using the method of mutual information and principal component analysis respectively. Results of the forecast in the Qujiang Catchment suggest the climatic factor-based BP neural network hydrological forecasting model has a better forecasting effect using the mutual information method than using the principal component analysis method.</p>

scholarly journals Model Building for Regional Ecological Risk Prediction and Evaluation of Prediction Accuracy

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Jia Shao ◽  
Bei-lan Li ◽  
Wei-jun Liu ◽  
Min Chen
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Ecological Risk ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Model Building ◽  
Risk Model ◽  
Principal Component ◽  
Component Analysis ◽  
Risk Level

The regional ecological risk model is built to predict the regional ecological risk level more accurately by using principal component analysis and optimizing standard BP neural network. Taking Xiangxi Tujia and Miao Autonomous Prefecture as an example, twelve primary factors affecting regional risk are selected. The sample data are processed by principal component analysis. The obtained main components are then used as input factors of the improved BP neural network, and the level of ecological risk is used as output factor. The results indicate that the error between the expected output and the actual output is 4.36% in 2016, 1.08% in 2017, and 5.18% in 2018, respectively, with all controlled within 6%. Compared with the prediction accuracy made by standard BP neural network without principal component analysis, the prediction accuracy made by improved BP neural network with principal component analysis is greatly improved. This comprehensive prediction model provides a better evaluation method for prediction of ecological risk level.

Study on Prediction Model of Grain Yield Based on Principal Component Analysis and BP Neural Network

2015 ◽  
Vol 713-715 ◽  
pp. 1939-1942
Author(s):  
Xing Mei Xu ◽  
Li Ying Cao ◽  
Jing Zhou
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Prediction Model ◽  
Grain Yield ◽  
Bp Neural Network ◽  
Principal Component ◽  
Component Analysis ◽  
Jilin Province ◽  
Average Error ◽  
Main Factors

Taking the grain yield data from 1980 to 2012 of Jilin Province for example, this paper analyzes the main factors that influences the grain yield based on the principle component analysis method. According to these main factors, the input samples of BP neutral network are definite. Thereby, the BP neutral networks could be trained to predict. The results show that the fertilizer consumption, large cattle head number, end grain sowing area, effective irrigation area and rural per capita living space are the main effect factor on grain yield. The BP neural network was built by using it as the input samples. The number of input nodes of the network is determined. Then build the prediction model of grain production in Jilin province. The simulation results show that, the average error of prediction results of BP neural network model based on principal component analysis is 4.48%.

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