A hybrid model based on an artificial neural network with a long chain of short-term memory elements and a discrete wavelet transform for predicting surface methane content in the Arctic area

Abstract

The study of the dynamics of greenhouse gases in the Arctic regions of the planet is becoming increasingly important. Such studies are especially relevant due to the climate change observed in this region. The paper propose a hybrid model that combines wavelet transformation of the original data and an artificial neural network with a long chain of short-term memory (LSTM) elements to predict changes in the surface methane concentration in the Arctic latitudes. The methane concentration time series via a discrete wavelet transform was decomposed into four components — one approximating and three detailing ones. These components were used to train the LSTM network. The forecast was calculated as the sum of forecasts for each of the components. Three predictive models were built. In the first, the LSTM network was trained in a non-linear autoregressive mode. The second one was a combination of discrete wavelet transform with LSTM neural network. An additional model based on a non-linear autoregressive neural network (NAR) was also used for comparison. The work is based on data from environmental monitoring of greenhouse gases on Bely Island, Yamalo-Nenets Autonomous Area of Russia. The initial data for building the proposed model were obtained within July-August 2017. The accuracy of the forecast was assessed using several indicators. The hybrid model based on LSTM showed the best accuracy.