A model to forecast methane emissions from topical and subtropical reservoirs on the basis of artificial neural networks

Abstract

In view of the great paucity of information on the exact contributions of different causes which lead to different extents of emission of the greenhouse gas methane (CH4) form reservoirs, it is tremendously challenging to develop statistical or analytical models for forecasting such emissions. Artificial neural networks (ANNs) have the ability to discern linear or non-linear relationships despite very limited data inputs and can recognize even complex patterns in a data set without a priori understating of the underlying mechanism. Hence, we have used ANNs to develop a model linking CH4 emissions to five of the reservoir parameters about which data is most commonly available in the prior art. Using a compendium of all available data on these parameters, of which a small part was kept aside for use in model validation, it has been possible to develop a model which is able to forecast CH4 emissions with a root mean square error of 37. It indicates a precision significantly better than the ones achieved in previous reports. The model provides a means to estimate CH4 emissions from reservoirs of which age, mean depth, surface area, latitude and longitude are known.