Prediction of atmospheric ammonia concentration in farmlands using BP neural network

The emission of ammonia from agricultural system is the main source of atmospheric ammonia and nitrogen fertilizer application is the main sources of ammonia emission in agriculture. The prediction of the changes in atmospheric ammonia concentration and the determination of the factors driving atmospheric ammonia emission will be benefit to the basis for scientific and rational farmland management and for control of environmental pollution. In this paper, BP neural network was used to analyze the concentration of ammonia in farmlands and its relationship with various meteorological factors. The aim was to better understand the changes in ammonia concentration in farmlands and provide new idea and method of study of ammonia in farmlands. First, farmland ammonia measured with Laser Analyzer and meteorological monitoring data from May to October 2015 were used to establish a model for the prediction of farmland ammonia with meteorological factors (air pressure, air temperature, relative humidity, precipitation, wind speed and sunshine hours) as input variables. Secondly, principal component analysis was used to screen meteorological factors with the strongest effect on the ammonia concentration in farmlands, including air temperature, relative humidity, precipitation and wind speed. Then four main factors and six original factors were used as input variables to predict ammonia concentration in farmlands in the region. The results showed that the actual ammonia concentration in farmlands was 0.148 5 mg·m^-3, the predicted value based on four factors was 0.159 4 mg·m^-3 (with predicted error of 7.35%) and the predicted value based on six factors was 0.173 2 mg·m^-3 (with predicted error of 16.65%). The range of the relative error of four prediction factors was 1.4%-27.0% and that of six factors was 1.1%-45.0%. The predicted concentration of ammonia in farmlands varied greatly in the first five days, decreased gradually and apparently flattened out with time, which was basically consistent with the measured value. Eventually, four factors were used as input variables in building the prediction model. The predicted values of farmland ammonia concentration used four factors were more consistent with measured values than that used six factors. It was noted that the established neural network model after removing redundant factors by principal component analysis method was more effective, and the prediction results were better than those before screening. The model established had better applicability for selecting key factors, and the prediction accuracy was higher. The model constructed in this paper for predicting ammonia concentration in farmlands provided more accurate method and newer idea than before on the analysis of farmland ammonia concentration and the related research.