Application of Artificial Neural Network to Relative Humidity Prediction
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Abstract:
Relative humidity environment is an important item of agricultural production monitoring and forecasting. Relative humidity concerns to plant growth condition, ecological prevention and control of several plant diseases, irrigation regulation. Because relative humidity has many affecting factors, there are nonlinear relations between relative humidity and its influence factors. An improved BP artificial neural network was put forward as the relative humidity prediction model, in order to improve the relative humidity prediction accuracy. The model can predict the air relative humidity with such inputs as the weather data (hours of sunlight, precipitation, the lowest temperature, the average temperature, the highest temperature). Experiments were carried out in Lingshui area to collect data to validate the model. The simulation results indicate that the monthly average relative humidity for the period of 1987~1998 was simulated and predicted for the period of 1999~2000. Relative error between the simulation and measured air relative humidity was 0.21%, and relative error between the prediction and measured air relative humidity was 0.28%. The improved BP artificial neural network has higher prediction precision. It was simple that using the BP model method to study relative humidity. And also it can provide reference for the other area’ research about relative humidity.
