Application of Artificial Neural Network in Forecasting Water Consumption of Populus (P.xeuramericana cv.74/76) Seedlings
In this experiment, by using the method of artificial neural network and DPS DATA PROCESSING SYSTEM combined with the meteorological data of air temperature, relative air humidity, solar radiation, wind speed, soil water content and dew point temperature as the input variable, the author established the artificial neural network system to forecast the seedling water consumption of P.xeuramericana cv.74/76, and through the experiments it has been examined that three neural network system models can be applied in forecasting water consumption of seedlings, and the average relative error of Back Propagation (BP) neural network prediction model was 0.04, the projection pursuit regression (PPR) neural network prediction model was 0.03, the multiple stepwise regression anatomic model was 0.10, moreover, the latter two ones had good stability, while that of BP neural network prediction model was poor. Therefore, we propose that PPR neural network model can be used in prediction of seedling water consumption. Furthermore, the maximum relative error of PPR neural network predication model was 0.073, the minimum relative error was 0.002. The neural network model is superior to the former linear model that the neural network model performs a higher forecasting accuracy with relativ ely shorter time consumption in training.
water consumption meteorological factor artificial neural network forecasting
GAO Wei-dong MA Lu-yi JIA Zhong-kui NING Yang-cui
The Key Laboratory for Silviculture and Conservation of the Ministry of Education, College of Forestry, Beijing Forestry University, Beijing 100083, P.R.China
国际会议
太原
英文
585-589
2010-10-22（万方平台首次上网日期，不代表论文的发表时间）