Predicting WastewaterBOD Levels with Neural Network Time Series Models

2011 ◽  
pp. 102-120
Author(s):  
David West ◽  
Scott Dellana
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Wastewater Treatment ◽  
Wastewater Effluent ◽  
Treated Wastewater ◽  
Human Populations ◽  
Effluent Quality ◽  
Network Time ◽  
Accurate Forecast ◽  
Using Data

The quality of treated wastewater has always been an important issue, but it becomes even more critical as human populations increase. Unfortunately, current ability to monitor and control effluent quality from a wastewater treatment process is primitive (Wen & Vassiliadis, 1998). Control is difficult because wastewater treatment consists of complex multivariate processes with nonlinear relationships and time varying dynamics. Consequently, there is a critical need for forecasting models that are effective in predicting wastewater effluent quality. Using data from an urban wastewater treatment plant, we tested several linear and nonlinear models, including ARIMA and neural networks. Our results provide evidence that a nonlinear neural network time series model achieves the most accurate forecast of wastewater effluent quality.

Time Series Forecasting of Tunnel Surrounding Rock Displacement

2011 ◽  
Vol 261-263 ◽  
pp. 1789-1793 ◽  
Author(s):  
Guang Xiang Mao ◽  
Yuan You Xia ◽  
Ling Wei Liu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Bp Neural Network ◽  
Time Series Prediction ◽  
Surrounding Rock ◽  
Tunnel Construction ◽  
Rock Stress ◽  
Rock Types ◽  
Network Time ◽  
Tunnel Depth

In the process of tunnel construction, because the rock stress redistribute, the vault and the two groups will generate displacement constantly. This paper adopts the genetic algorithm to optimize the weight and threshold of BP neural network, taking the tunnel depth, rock types and part measured values of displacement as input parameters to construct a neural network time series prediction model of tunnel surrounding rock displacement. The method proposed in the paper has been applied in the Ma Tou Tang tunnel construction successfully, and the results show that the model can predict the displacement of the surrounding rock quickly and accurately.

scholarly journals A Novel Model with GA Evolving FWNN for Effluent Quality and Biogas Production Forecast in a Full-Scale Anaerobic Wastewater Treatment Process

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Zehua Huang ◽  
Renren Wu ◽  
XiaoHui Yi ◽  
Hongbin Liu ◽  
Jiannan Cai ◽  
...  
Keyword(s):  
Neural Network ◽  
Wastewater Treatment ◽  
Treatment Process ◽  
Biogas Production ◽  
Anaerobic Treatment ◽  
Production Rates ◽  
Wastewater Treatment Process ◽  
Effluent Quality ◽  
Anaerobic Wastewater Treatment ◽  
Novel Model

The anaerobic treatment process is a complicated multivariable system that is nonlinear and time varying. Moreover, biogas production rates are an important indicator for reflecting operational performance of the anaerobic treatment system. In this work, a novel model fuzzy wavelet neural network based on the genetic algorithm (GA-FWNN) that combines the advantages of the genetic algorithm, fuzzy logic, neural network, and wavelet transform was established for prediction of effluent quality and biogas production rates in a full-scale anaerobic wastewater treatment process. Moreover, the dataset was preprocessed via a self-adapted fuzzy c-means clustering before training the network and a hybrid algorithm for acquiring the optimal parameters of the multiscale GA-FWNN for improving the network precision. The analysis results indicate that the FWNN with the optimal algorithm had a high speed of convergence and good quality of prediction, and the FWNN model was more advantageous than the traditional intelligent coupling models (NN, WNN, and FNN) in prediction accuracy and robustness. The determination coefficients R2 of the FWNN models for predicting both the effluent quality and biogas production rates were over 0.95. The proposed model can be used for analyzing both biogas (methane) production rates and effluent quality over the operational time period, which plays an important role in saving energy and eliminating pollutant discharge in the wastewater treatment system.

Roughness/error trade-offs in neural network time series models

1997 ◽  
Author(s):  
Steven C. Gustafson ◽  
Gordon R. Little ◽  
John S. Loomis ◽  
Theresa A. Tuthill
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Models ◽  
Trade Offs ◽  
Network Time

Forecasting Emerging Market Indexes with Neural Networks

2011 ◽  
pp. 80-101 ◽  
Author(s):  
Steven Walczah
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Global Knowledge ◽  
Index Change ◽  
Market Index ◽  
The Neural Network ◽  
Neural Network System ◽  
Homogeneous Models ◽  
Using Data

Forecasting financial time series with neural networks is problematic. Multiple decisions, each of which affects the performance of the neural network forecasting model, must be made, including which data to use and the size and architecture of the neural network system. While most previous research with neural networks has focused on homogenous models, that is, only using data from the single time series to be forecast, the ever more global nature of the world’s financial markets necessitates the inclusion of more global knowledge into neural network design. This chapter demonstrates how specific markets are at least partially dependent on other global markets and that inclusion of heterogeneous market information will improve neural network forecasting performance over similar homogeneous models by as much as 12 percent (i.e., moving from a near 51% prediction accuracy for the direction of the market index change to a 63% accuracy of predicting the direction of the market index change).

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