The problem of accurately forecasting the amount and content of a landfill gas has remained an active area of research, due to the safety and environmental concerns, as well as use of the gas as an energy source. The key obstacle to the forecasting is that only limited measured data, such as the gas flux and composition, are usually available. In this part of the series, we propose a novel approach, based on a combination of the genetic algorithm (GA) and the ensemble Kalman filter (EnKF), to the problem of generation and dynamic updating of a landfill model. First, the GA, whose use in landfill problems was demonstrated in a previous part of this series, is used to generate the initial spatial distribution of the permeability in a landfill by assimilating the available measured data. Then, the EnKF is employed to continuously update the model using the data measured in real time. The effectiveness of the combination of the GA and EnKF is demonstrated with the simulation of a model landfill and gas generation and transport therein, developed previously in this series, using synthetic data and a parallel computational strategy. (C) 2012 Elsevier Ltd. All rights reserved.