Evaluating the ability of artificial neural network and PCA-M5P models in predicting leachate COD load in landfills
• Leachate COD in different time and weather conditions is predicted. • Two intelligent models consisting of ANN and PCA-M5P are utilized. • Efficiency of ANN and PCA-M5P models for the prediction of COD leachate is evaluated. • Effects of deposited waste age, weight of waste, rainfall, base and top...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Azadi, Sama [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Umfang: |
11 |
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| Übergeordnetes Werk: |
Enthalten in: Structural and mechanical behavior of colloidal fat crystal networks of fully hydrogenated lauric acid-rich fats and rapeseed oils mixtures - Chai, Xiuhang ELSEVIER, 2019, international journal of integrated waste management, science and technology, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:55 ; year:2016 ; pages:220-230 ; extent:11 |
| Links: |
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| DOI / URN: |
10.1016/j.wasman.2016.05.025 |
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| 520 | |a • Leachate COD in different time and weather conditions is predicted. • Two intelligent models consisting of ANN and PCA-M5P are utilized. • Efficiency of ANN and PCA-M5P models for the prediction of COD leachate is evaluated. • Effects of deposited waste age, weight of waste, rainfall, base and top CCL thicknesses and the thickness of landfill cover on the leachate COD are considered. • Statistical analysis is performed to evaluate the prediction of leachate COD. | ||
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evaluating the ability of artificial neural network and pca-m5p models in predicting leachate cod load in landfills |
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Evaluating the ability of artificial neural network and PCA-M5P models in predicting leachate COD load in landfills |
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• Leachate COD in different time and weather conditions is predicted. • Two intelligent models consisting of ANN and PCA-M5P are utilized. • Efficiency of ANN and PCA-M5P models for the prediction of COD leachate is evaluated. • Effects of deposited waste age, weight of waste, rainfall, base and top CCL thicknesses and the thickness of landfill cover on the leachate COD are considered. • Statistical analysis is performed to evaluate the prediction of leachate COD. |
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• Leachate COD in different time and weather conditions is predicted. • Two intelligent models consisting of ANN and PCA-M5P are utilized. • Efficiency of ANN and PCA-M5P models for the prediction of COD leachate is evaluated. • Effects of deposited waste age, weight of waste, rainfall, base and top CCL thicknesses and the thickness of landfill cover on the leachate COD are considered. • Statistical analysis is performed to evaluate the prediction of leachate COD. |
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• Leachate COD in different time and weather conditions is predicted. • Two intelligent models consisting of ANN and PCA-M5P are utilized. • Efficiency of ANN and PCA-M5P models for the prediction of COD leachate is evaluated. • Effects of deposited waste age, weight of waste, rainfall, base and top CCL thicknesses and the thickness of landfill cover on the leachate COD are considered. • Statistical analysis is performed to evaluate the prediction of leachate COD. |
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Evaluating the ability of artificial neural network and PCA-M5P models in predicting leachate COD load in landfills |
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Amiri, Hamid Rakhshandehroo, G. Reza |
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10.1016/j.wasman.2016.05.025 |
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2024-07-06T22:41:42.174Z |
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