Prediction of the waste stabilization pond performance using linear multiple regression and multi-layer perceptron neural network: a case study of Birjand, Iran
Journal Title: UNKNOWN - Year 2016, Vol 3, Issue 2
Abstract
Background: Data mining (DM) is an approach used in extracting valuable information from environmental processes. This research depicts a DM approach used in extracting some information from influent and effluent wastewater characteristic data of a waste stabilization pond (WSP) in Birjand, a city in Eastern Iran. Methods: Multiple regression (MR) and neural network (NN) models were examined using influent characteristics (pH, Biochemical oxygen demand [BOD5], temperature, chemical oxygen demand [COD], total suspended solids [TSS], total dissolved solid [TDS], electrical conductivity [EC] and turbidity) as the regression input vectors. Models were adjusted to input attributes, effluent BOD5 (BODout) and COD (CODout). The models performances were estimated by 10-fold external cross-validation. An internal 5-fold cross-validation was also used for the training data set in NN model. The models were compared using regression error characteristic (REC) plot and other statistical measures such as relative absolute error (RAE). Sensitivity analysis was also applied to extract useful knowledge from NN model. Results: NN models (with RAE = 78.71 ± 1.16 for BODout and 83.67 ± 1.35 for CODout) and MR models (with RAE = 84.40% ± 1.07 for BODout and 88.07 ± 0.80 for CODout) indicate different performances and the former was better (P < 0.05) for the prediction of both effluent BOD5 and COD parameters. For the prediction of CODout the NN model with hidden layer size (H) = 4 and decay factor = 0.75 ± 0.03 presented the best predictive results. For BODout the H and decay factor were found to be 4 and 0.73 ± 0.03, respectively. TDS was found as the most descriptive influent wastewater characteristics for the prediction of the WSP performance. The REC plots confirmed the NN model performance superiority for both BOD and COD effluent prediction. Conclusion: Modeling the performance of WSP systems using NN models along with sensitivity analysis can offer better understanding on exploring the most significant parameters for the prediction of system performance. The findings of this study could build the foundation for prospective work on the characterization of WSP operations and optimization of their performances with a view to conducting statistical approaches
Authors and Affiliations
mehdi hadi
Keywords
Related Articles
- EP ID EP285601
- DOI 10.15171/EHEM.2016.05
- Views 82
- Downloads 0
Sulfur dioxide AQI modeling by artificial neural network in Tehran between 2007 and 2013
Background: Air pollution and concerns about health impacts have been raised in metropolitan cities like Tehran. Trend and prediction of air pollutants can show the effectiveness of strategies for the management and cont...
Evaluation of removal efficiency of 2-chlorophenol in aquatic environments by modified fly ash
Background: Chlorophenols are classified as priority toxic pollutants. These acidic organic compounds present a serious potential hazard for human health and aquatic life. Chlorophenols accumulate in water, soil and air...
An environmentally friendly soil improvement technology for sand and dust storms control
Background: Dust storms occur when unchecked, strong, or turbulent winds combine with exposed loose and dried soil surfaces. Sand and dust storms have a significant impact on society, economy, and environment at local, r...
Evaluation of factors affecting on lipid extraction for recovery of fatty acids from Nannochloropsis oculata micro-algae to biodiesel production
Background: This study aimed at determining the appropriate method for dewatering and drying biomass and selecting a suitable organic solvent for lipid extraction. Methods: Nannochloropsis Oculata was cultured in Gillard...
Bioremediation of methyl tertiary-butyl ether (MTBE) by three pure bacterial cultures
Background: Bioremediation of groundwater and soil contamination is more economical than physicochemical remediation. The present study focused on the bioremediation capability of two bacterial species (Klebsiella planti...