Research Article
A Local Field Correlated and Monte Carlo Based Shallow Neural Network Model for Nonlinear Time Series Prediction
@ARTICLE{10.4108/eai.9-8-2016.151634, author={Qingguo Zhou and Huaming Chen and Hong Zhao and Gaofeng Zhang and Jianming Yong and Jun Shen}, title={A Local Field Correlated and Monte Carlo Based Shallow Neural Network Model for Nonlinear Time Series Prediction}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={3}, number={8}, publisher={EAI}, journal_a={SIS}, year={2016}, month={8}, keywords={}, doi={10.4108/eai.9-8-2016.151634} }- Qingguo Zhou
Huaming Chen
Hong Zhao
Gaofeng Zhang
Jianming Yong
Jun Shen
Year: 2016
A Local Field Correlated and Monte Carlo Based Shallow Neural Network Model for Nonlinear Time Series Prediction
SIS
EAI
DOI: 10.4108/eai.9-8-2016.151634
Abstract
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.
Copyright © 2016 Vimalachandran et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
