NN-LEAP: A neural network-based model for controlling leachate flow-rate in a municipal solid waste landfill site

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.