Staff View: Leachate generation rate modeling using artificial intelligence algorithms aided by input optimization method for an MSW landfill

Leachate generation rate modeling using artificial intelligence algorithms aided by input optimization method for an MSW landfill

Leachate is one of the main surface water pollution sources in Selangor State (SS), Malaysia. The prediction of leachate amounts is elementary in sustainable waste management and leachate treatment processes, before discharging to surrounding environment. In developing countries, the accurate evalua...

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Main Authors:	Abunama, Taher, Othman, Faridah, Ansari, Mozafar, El-Shafie, Ahmed
Format:	Article
Published:	Springer Verlag (Germany) 2019
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://eprints.um.edu.my/24295/ https://doi.org/10.1007/s11356-018-3749-5
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spelling	my.um.eprints.242952020-05-18T02:39:03Z http://eprints.um.edu.my/24295/ Leachate generation rate modeling using artificial intelligence algorithms aided by input optimization method for an MSW landfill Abunama, Taher Othman, Faridah Ansari, Mozafar El-Shafie, Ahmed TA Engineering (General). Civil engineering (General) Leachate is one of the main surface water pollution sources in Selangor State (SS), Malaysia. The prediction of leachate amounts is elementary in sustainable waste management and leachate treatment processes, before discharging to surrounding environment. In developing countries, the accurate evaluation of leachate generation rates has often considered a challenge due to the lack of reliable data and high measurement costs. Leachate generation is related to several factors, including meteorological data, waste generation rates, and landfill design conditions. The high variations in these factors lead to complicating leachate modeling processes. This study aims at identifying the key elements contributing to leachate production and developing various AI-based models to predict leachate generation rates. These models included Artificial Neural Network (ANN)-Multi-linear perceptron (MLP) with single and double hidden layers, and support vector machine (SVM) regression time series algorithms. Various performance measures were applied to evaluate the developed model’s accuracy. In this study, input optimization process showed that three inputs were acceptable for modeling the leachate generation rates, namely dumped waste quantity, rainfall level, and emanated gases. The initial performance analysis showed that ANN-MLP2 model—which applies two hidden layers—achieved the best performance, then followed by ANN-MLP1 model—which applies one hidden layer and three inputs—while SVM model gave the lowest performance. Ranges and frequency of relative error (RE%) also demonstrate that ANN-MLP models outperformed SVM models. Furthermore, low and peak flow criterion (LFC and PFC) assessment of leachate inflow values in ANN-MLP model with two hidden layers made more accurate values than other models. Since minimizing data collection and processing efforts as well as minimizing modeling complexity are critical in the hydrological modeling process, the applied input optimization process and the developed models in this study were able to provide a good performance in the modeling of leachate generation efficiently. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature. Springer Verlag (Germany) 2019 Article PeerReviewed Abunama, Taher and Othman, Faridah and Ansari, Mozafar and El-Shafie, Ahmed (2019) Leachate generation rate modeling using artificial intelligence algorithms aided by input optimization method for an MSW landfill. Environmental Science and Pollution Research, 26 (4). pp. 3368-3381. ISSN 0944-1344 https://doi.org/10.1007/s11356-018-3749-5 doi:10.1007/s11356-018-3749-5
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	TA Engineering (General). Civil engineering (General)
spellingShingle	TA Engineering (General). Civil engineering (General) Abunama, Taher Othman, Faridah Ansari, Mozafar El-Shafie, Ahmed Leachate generation rate modeling using artificial intelligence algorithms aided by input optimization method for an MSW landfill
description	Leachate is one of the main surface water pollution sources in Selangor State (SS), Malaysia. The prediction of leachate amounts is elementary in sustainable waste management and leachate treatment processes, before discharging to surrounding environment. In developing countries, the accurate evaluation of leachate generation rates has often considered a challenge due to the lack of reliable data and high measurement costs. Leachate generation is related to several factors, including meteorological data, waste generation rates, and landfill design conditions. The high variations in these factors lead to complicating leachate modeling processes. This study aims at identifying the key elements contributing to leachate production and developing various AI-based models to predict leachate generation rates. These models included Artificial Neural Network (ANN)-Multi-linear perceptron (MLP) with single and double hidden layers, and support vector machine (SVM) regression time series algorithms. Various performance measures were applied to evaluate the developed model’s accuracy. In this study, input optimization process showed that three inputs were acceptable for modeling the leachate generation rates, namely dumped waste quantity, rainfall level, and emanated gases. The initial performance analysis showed that ANN-MLP2 model—which applies two hidden layers—achieved the best performance, then followed by ANN-MLP1 model—which applies one hidden layer and three inputs—while SVM model gave the lowest performance. Ranges and frequency of relative error (RE%) also demonstrate that ANN-MLP models outperformed SVM models. Furthermore, low and peak flow criterion (LFC and PFC) assessment of leachate inflow values in ANN-MLP model with two hidden layers made more accurate values than other models. Since minimizing data collection and processing efforts as well as minimizing modeling complexity are critical in the hydrological modeling process, the applied input optimization process and the developed models in this study were able to provide a good performance in the modeling of leachate generation efficiently. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.
format	Article
author	Abunama, Taher Othman, Faridah Ansari, Mozafar El-Shafie, Ahmed
author_facet	Abunama, Taher Othman, Faridah Ansari, Mozafar El-Shafie, Ahmed
author_sort	Abunama, Taher
title	Leachate generation rate modeling using artificial intelligence algorithms aided by input optimization method for an MSW landfill
title_short	Leachate generation rate modeling using artificial intelligence algorithms aided by input optimization method for an MSW landfill
title_full	Leachate generation rate modeling using artificial intelligence algorithms aided by input optimization method for an MSW landfill
title_fullStr	Leachate generation rate modeling using artificial intelligence algorithms aided by input optimization method for an MSW landfill
title_full_unstemmed	Leachate generation rate modeling using artificial intelligence algorithms aided by input optimization method for an MSW landfill
title_sort	leachate generation rate modeling using artificial intelligence algorithms aided by input optimization method for an msw landfill
publisher	Springer Verlag (Germany)
publishDate	2019
url	http://eprints.um.edu.my/24295/ https://doi.org/10.1007/s11356-018-3749-5
_version_	1669007988418936832
score	13.211869

Leachate generation rate modeling using artificial intelligence algorithms aided by input optimization method for an MSW landfill

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