Forecasting the cost of municipal engineering based on PCA and NARX neural network

There are many factors influencing the cost of municipal engineering. It is usually difficult and time consuming to predict the cost of municipal engineering. This paper proposes a two-phase method to predict the cost of municipal engineering by combining principal component analysis (PCA) and NARX neural network. In the first phase of the proposed method, PCA is used to analyze the correlations among involved factors based on the original data and extract key factors that influence the cost of municipal engineering. In the second phase, taking the key factors extracted by PCA as input and the unit area engineering cost as the output, a NARX neural network model is constructed based on the Bayesian regularization algorithm to predict the cost of municipal engineering. The experimental results show that the proposed method can predict the cost of municipal engineering fast and accurately, which shows that the proposed prediction method is feasible and effective.