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


Affiliations
1 National Science and Technology Center for Disaster Reduction, New Taipei City 23143, Taiwan, Province of China
2 Department of Civil and Disaster Prevention Engineering, National United University, Miaoli 36063, Taiwan, Province of China
 

In this study, two artificial neural networkmodels (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 α, 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.
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  • Water Quality Modeling in Reservoirs Using Multivariate Linear Regression and Two Neural Network Models

Abstract Views: 148  |  PDF Views: 35

Authors

Wei-Bo Chen
National Science and Technology Center for Disaster Reduction, New Taipei City 23143, Taiwan, Province of China
Wen-Cheng Liu
Department of Civil and Disaster Prevention Engineering, National United University, Miaoli 36063, Taiwan, Province of China

Abstract


In this study, two artificial neural networkmodels (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 α, 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.