Quantifying the relative importance of soil moisture, nitrogen, and temperature on the urea hydrolysis rate

ABSTRACTUrea hydrolysis is a complicated process influenced by multiple factors. Most previous studies only examined the causative factors of the process without ranking these factors’ relative importance quantitatively. In this work, the experimental analysis method, ANOVA method, and backpropagation (BP) neural network method were used to rank the effects of moisture content (W), nitrogen amount (F), and temperature (T) on the urea hydrolysis rate. A group of 22 artificial neural network structures with different numbers of neurons in the hidden layer (2 ≤ L ≤ 12), different initial connection weights, and different learning algorithms was trained and validated using the data set. The relative importance of the factors that affect urea hydrolysis was studied on the basis of information on the weight and threshold value. Results showed that the individual factors and the interaction between any two factors exerted an extremely significant effect (p < 0.01) on the hydrolysis rate, except for the interacti...