Optimized BP neural network for Dissolved Oxygen prediction

Abstract To solve the low accuracy, slow convergence and poor robustness problem of traditional neural network method for water quality forecasting, a new model of dissolved oxygen content prediction is proposed based on sliding window, particle swarm optimization (PSO) and BP neural network. dissolved oxygen content prediction model in water quality is established by handling dissolved oxygen content data through sliding window, and using particle swarm optimization algorithm to obtain BP neural network parameters. This model is applied to prediction analysis of dissolved oxygen with online monitoring of regional groundwater in Xilin Gol League on July 25, 2017 to December 5, 2017. Experimental results show that the model has better prediction effect, and mean square error (MSE), root mean square error(RMSE), mean absolute error(MAE) value of PSO algorithm to optimize the BP neural network based on sliding window are 0.437% and 6.611%, 0. 251% respectively, which are better than single forecasting method by using sliding window, PSO, and BP neural network individually. The Optimized BP neural network not only has fast convergence speed and high prediction accuracy, but also provides decision-making basis for water pollution control and water management.

[1]  George D. Magoulas,et al.  Improving the Convergence of the Backpropagation Algorithm Using Learning Rate Adaptation Methods , 1999, Neural Computation.

[2]  Han-Xiong Huang,et al.  A proposed iteration optimization approach integrating backpropagation neural network with genetic algorithm , 2015, Expert Syst. Appl..

[3]  Yan Wu,et al.  Prediction of gas solubility in polymers by back propagation artificial neural network based on self-adaptive particle swarm optimization algorithm and chaos theory , 2013 .

[4]  Masoud Monjezi,et al.  Blast-induced air and ground vibration prediction: a particle swarm optimization-based artificial neural network approach , 2015, Environmental Earth Sciences.

[5]  Pei Xin Lu.  Research on BP Neural Network Algorithm Based on Quasi-Newton Method , 2014 .

[6]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[7]  Marc A. Rosen,et al.  Application of sliding window technique for prediction of wind velocity time series , 2014 .

[8]  Mark A. Kramer,et al.  Improvement of the backpropagation algorithm for training neural networks , 1990 .

[9]  Wu Wei.  A back-propagation algorithm with adaptive momentum factor , 2008 .

[10]  E. Baafi,et al.  Application of artificial neural network coupled with genetic algorithm and simulated annealing to solve groundwater inflow problem to an advancing open pit mine , 2016 .

[11]  Jian Wang,et al.  A novel conjugate gradient method with generalized Armijo search for efficient training of feedforward neural networks , 2018, Neurocomputing.

[12]  Md. Afroz Alam,et al.  Performance comparison of artificial neural networks learning algorithms and activation functions in predicting severity of autism , 2015, Network Modeling Analysis in Health Informatics and Bioinformatics.

[13]  Marc Toussaint,et al.  Rprop Using the Natural Gradient , 2005 .

[14]  Chao Ren,et al.  Optimal parameters selection for BP neural network based on particle swarm optimization: A case study of wind speed forecasting , 2014, Knowl. Based Syst..