A Local Field Correlated and Monte Carlo Based Shallow Neural Network Model for Nonlinear Time Series Prediction

• Main Authors: Qingguo Zhou, Huaming Chen, Hong Zhao, Gaofeng Zhang, Jianming Yong, Jun Shen
• Format: Article
• Language: English
• Published: European Alliance for Innovation (EAI) 2016-08-01
• Series: EAI Endorsed Transactions on Scalable Information Systems
• Online Access: http://eudl.eu/doi/10.4108/eai.9-8-2016.151634

Water resource problems currently are much more important in proper planning especially for arid regions, such as Gansu in China. For agricultural and industrial activities, prediction of groundwater status is critical. As a main branch of neural network, shallow artificial neural network models have been deployed in prediction areas such as groundwater and rainfall since late 1980s. In this paper, artificial neural network (ANN) model within a newly proposed algorithm has been developed for groundwater status forecasting. Having considered previous algorithms for ANN model in time series forecast, this new Monte Carlo based algorithm demonstrated a good result. The experiments of this ANN model in predicting groundwater status were conducted on the Heihe River area dataset, which was curated on the collected data. When compared with its original physical based model, this ANN model was able to achieve a more stable and accurate result. A comparison and an analysis of this ANN model were also presented in this paper.