Abstract
A method is proposed for modeling leachate flow-rate in a municipal solid waste (MSW) landfill site, based on a popular neural network - the backpropagation algorithm (neural network-based leachate prediction method; NN-LEAP). After backpropagation training, the neural network model predicts flow-rates based on meteorological data. Depending on output value, relevant control strategies and actions are activated. To illustrate and validate the proposed method, a case study was carried out, based on the data obtained from the Istanbul Odayeri landfill site. As a critical model parameter (neural network outputs), daily flow-rate of leachate from the landfill site was considered. The Levenberg-Marquardt algorithm was selected as the best of 13 backpropagation algorithms. The optimal neural network architecture has been determined, and the advantages, disadvantages and further developments are discussed.
Original language | English |
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Pages (from-to) | 1190-1197 |
Number of pages | 8 |
Journal | Environmental Modelling and Software |
Volume | 21 |
Issue number | 8 |
DOIs | |
Publication status | Published - Aug 1 2006 |
Externally published | Yes |
Keywords
- Backpropagation algorithm
- Flow-rate
- Leachate
- Modeling
- Neural network
ASJC Scopus subject areas
- Software
- Environmental Engineering
- Ecological Modelling