Water Quality Evaluation Using Back Propagation Artificial Neural Network Based on Self-Adaptive Particle Swarm Optimization Algorithm and Chaos Theory

Mengshan Li,Bingsheng Chen,Lixin Guan,Yan Wu,Wei Wu

doi:10.4236/cweee.2017.63016

Mengshan Li, Bingsheng Chen + Show 3 more

Open Access

https://doi.org/10.4236/cweee.2017.63016

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Abstract

To overcome the shortcomings of the traditional methods of water quality evaluation, in this paper, a novel model combines particle swarm optimization (PSO), chaos theory, self-adaptive strategy and back propagation artificial neural network (BP ANN) that was proposed to evaluate the water quality of Weihe River in China. An improved PSO algorithm with a self-adaptive inertia weight and a chaotic learning factor tuned by logistic function was developed and used to optimize the network parameters of BP ANN. The values of average absolute deviation (AAD), root mean square error of prediction (RMSEP) and squared correlation coefficient are 0.0061, 0.0163 and 0.9903, respectively. Compared with other methods, such as BP ANN, and PSO BP ANN, the proposed model displays optimal prediction performance with high precision and good correlation. The results show that the proposed method has the good prediction ability for evaluating water quality. It is convenient, reliable and high precision, which provides good analysis and evaluation method for water quality.

Highlights

IntroductionThe water quality evaluation is an important link of its research system, and has almost become an indispensable important part of all the environmental quality evaluation, accurately orienting the pollution level of lakes/rivers and the trend of future development, and more efficiently utilizing and protect-
An improved particle swarm optimization (PSO) algorithm with a self-adaptive inertia weight and a chaotic learning factor tuned by logistic function was developed and used to optimize the network parameters of back propagation artificial neural network (BP artificial neural network (ANN))
In order to improve the BP ANN’s shortcomings of easy to fall into local optimum and depend on the choice of initial weight, this paper proposes an improved PSO algorithm based on chaos theory and adaptive strategy, and it’s used to optimize the parameters of BP ANN, obtaining a hybrid artificial neural network prediction model, called CSAPSO BP ANN at the same time, to discuss the prediction effect of the model through making the CSAPSO BP ANN model apply to water quality evaluation

Summary

Introduction

The water quality evaluation is an important link of its research system, and has almost become an indispensable important part of all the environmental quality evaluation, accurately orienting the pollution level of lakes/rivers and the trend of future development, and more efficiently utilizing and protect-. Because the water quality is affected by many factors, there is a complex non-linear relationship between the evaluation index and water quality standard These traditional processing methods can’t be addressed complex nonlinear problems well and the traditional mathematical evaluation method gradually replaced by intelligent optimization algorithm. In order to improve the BP ANN’s shortcomings of easy to fall into local optimum and depend on the choice of initial weight, this paper proposes an improved PSO algorithm based on chaos theory and adaptive strategy, and it’s used to optimize the parameters of BP ANN, obtaining a hybrid artificial neural network prediction model, called CSAPSO BP ANN at the same time, to discuss the prediction effect of the model through making the CSAPSO BP ANN model apply to water quality evaluation

Improved Particle Swarm Optimization Algorithm CSAPSO

CSAPSO BP ANN Model

Model Structure

Results and Discussion

Conclusions