Back to Search
Start Over
Evaluating the ability of artificial neural network and PCA-M5P models in predicting leachate COD load in landfills
- Source :
- Waste management (New York, N.Y.). 55
- Publication Year :
- 2015
-
Abstract
- Waste burial in uncontrolled landfills can cause serious environmental damages and unpleasant consequences. Leachates produced in landfills have the potential to contaminate soil and groundwater resources. Leachate management is one of the major issues with respect to landfills environmental impacts. Improper design of landfills can lead to leachate spread in the environment, and hence, engineered landfills are required to have leachate monitoring programs. The high cost of such programs may be greatly reduced and cost efficiency of the program may be optimized if one can predict leachate contamination level and foresee management and treatment strategies. The aim of this study is to develop two expert systems consisting of Artificial Neural Network (ANN) and Principal Component Analysis-M5P (PCA-M5P) models to predict Chemical Oxygen Demand (COD) load in leachates produced in lab-scale landfills. Measured data from three landfill lysimeters, including rainfall depth, number of days after waste deposition, thickness of top and bottom Compacted Clay Liners (CCLs), and thickness of top cover over the lysimeter, were utilized to develop, train, validate, and test the expert systems and predict the leachate COD load. Statistical analysis of the prediction results showed that both models possess good prediction ability with a slight superiority for ANN over PCA-M5P. Based on test datasets, the mean absolute percentage error for ANN and PCA-M5P models were 4% and 12%, respectively, and the correlation coefficient for both models was greater than 0.98. Developed models may be used as a rough estimate for leachate COD load prediction in primary landfill designs, where the effect of a top and/or bottom liner is disputed.
- Subjects :
- Engineering
Correlation coefficient
0208 environmental biotechnology
02 engineering and technology
010501 environmental sciences
01 natural sciences
Deposition (geology)
Leachate
Waste Management and Disposal
0105 earth and related environmental sciences
Biological Oxygen Demand Analysis
Principal Component Analysis
Waste management
Cost efficiency
business.industry
Chemical oxygen demand
Water Pollution
Environmental engineering
Contamination
Models, Theoretical
020801 environmental engineering
Waste Disposal Facilities
Mean absolute percentage error
Lysimeter
Neural Networks, Computer
business
Water Pollutants, Chemical
Subjects
Details
- ISSN :
- 18792456
- Volume :
- 55
- Database :
- OpenAIRE
- Journal :
- Waste management (New York, N.Y.)
- Accession number :
- edsair.doi.dedup.....fdc4c313920a54a8413db8548214d765