Methane Emissions Forecasting Using Hybrid Quantum-Classical Deep Learning Models: Case Study of North Africa

Abstract

This study explores climate change by predicting methane emissions in North Africa using classical and quantum deep learning methods. Using data from Sentinel-5P, we developed hybrid quantum-classical models, such as quantum long short-term memory (QLSTM) and quantum-gated recurrent unit networks (QGRUs), along with a novel hybrid architecture combining quantum convolutional neural networks (QCNNs) with LSTM and GRU, namely QCNN-LSTM and QCNN-GRU. The results show that these quantum models, especially the proposed hybrid architectures, outperform classical models by approximately seven percent in root-mean-squared error with fewer training epochs. These findings highlight the potential of quantum methodologies for enhancing environmental monitoring accuracy. Future research will aim to refine model performance, incorporate explainable AI techniques, and expand to forecasting other greenhouse gases, contributing to climate change mitigation efforts.