Knowledge-based rigorous machine learning techniques to predict the deliverability of underground natural gas storage sites for contributing to sustainable development goals

A Comprehensive Investigation into the Application of Convolutional Neural Networks (ConvNet/CNN) in Smart Grids

“…Recently, machine learning approaches like Extra Tree, Least Squares Support Vector Machine, and Gaussian Process Regression have significantly improved several science and technology disciplines. This can be used to forecast whether underground natural gas storage sites will be available in time to support sustainable development goals [8]. The random forest, decision tree, support vector regression, and artificial neural network algorithms can be employed to determine the pore pressure.…”