Table of Contents
Advances in Artificial Neural Systems
Volume 2015, Article ID 521721, 12 pages
Research Article

Water Quality Modeling in Reservoirs Using Multivariate Linear Regression and Two Neural Network Models

1National Science and Technology Center for Disaster Reduction, New Taipei City 23143, Taiwan
2Department of Civil and Disaster Prevention Engineering, National United University, Miaoli 36063, Taiwan
3Taiwan Typhoon and Flood Research Institute, National Applied Research Laboratories, Taipei 10093, Taiwan

Received 12 April 2015; Accepted 25 May 2015

Academic Editor: Ozgur Kisi

Copyright © 2015 Wei-Bo Chen and Wen-Cheng Liu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In this study, two artificial neural network models (i.e., a radial basis function neural network, RBFN, and an adaptive neurofuzzy inference system approach, ANFIS) and a multilinear regression (MLR) model were developed to simulate the DO, TP, Chl a, and SD in the Mingder Reservoir of central Taiwan. The input variables of the neural network and the MLR models were determined using linear regression. The performances were evaluated using the RBFN, ANFIS, and MLR models based on statistical errors, including the mean absolute error, the root mean square error, and the correlation coefficient, computed from the measured and the model-simulated DO, TP, Chl a, and SD values. The results indicate that the performance of the ANFIS model is superior to those of the MLR and RBFN models. The study results show that the neural network using the ANFIS model is suitable for simulating the water quality variables with reasonable accuracy, suggesting that the ANFIS model can be used as a valuable tool for reservoir management in Taiwan.