In the first four parts of this series a three-dimensional model was developed for transport and reaction of gaseous mixtures in a landfill. An optimization technique was also utilized in order to determine a landfill's spatial distributions of the permeability, porosity, the tortuosity factors, and the total gas generation potential of the wastes, given a limited amount of experimental data. In the present paper we develop an artificial neural network (ANN) in order to make accurate short-term predictions for several important quantities in a large landfill in southern California, including the temperature, and the CH4, CO2, and 02 concentration profiles. The ANN that is developed utilizes a back-propagation algorithm. The results indicate that the ANN can be successfully trained by the experimental data, and provide accurate predictions for the important quantities in the sector of the landfill where the data had been collected. Thus, an ANN may be used by landfills' operators for short-term plannings. Moreover, we showed that a novel combination of the three-dimensional model of gas generation, flow, and transport in landfills developed in Parts I, II, and IV, the optimization technique described in Part III, and the ANN developed in the present paper is a powerful approach for developing an accurate model of a landfill for long-term predictions and planning. (C) 2011 Elsevier Ltd. All rights reserved.