A feedforward neural network based on normalization and error correction for predicting water resources carrying capacity of a city
Chunxue Yu, Zuoyong Li, Zhifeng Yang, Xiaohong Chen, Meirong Su
Abstract
The water resources carrying capacity (WRCC) is fundamental in aiding sustainable socioeconomic regional development, and increasing attention is being paid to WRCC forecasting. The challenge of WRCC forecasting lies in the complex characteristics of nonlinear multiple indicator time series data. To address this problem, this study proposed a feedforward neural network (FNN) based on normalization and error correction for WRCC forecasting. Firstly, units and values of multiple indicators for use in the WRCC were normalized simultaneously to allow the data to be treated as a single equivalent indicator. Thus, the high-dimensional forecasting model was simplified to a low-dimensional model. To improve the effectiveness of the model, an error correction method was then adopted to correct forecasted WRCC values according to similar corresponding sample values. Two simple types of structured FNN were used to address the overfitting problem, and the bipolar sigmoid function was used as the activation function of hidden nodes. The final calibrated model was expressed as an equation rather than a programming language, thus making it easier to use. Yantai, a city in Shandong Province, was selected as a case study to validate this proposed method. Results showed that the mean relative absolute errors of these two simplified FNN models are 1.23% and 1.18%, respectively. Compared to other models, this model has been shown to be feasible and simple to use for WRCC forecasting.