Time Series Analysis of Air Pollutant Gas Concentrations: A Case Study in Isfahan's Urban Area

The article introduces a new method to estimate land subsidence using deep convolutional neural networks (CNNs) and persistent scatterer interferometric synthetic aperture radar (PSInSAR), addressing the limitations of traditional methods. It focuses on Isfahan Province, Iran, and demonstrates substantial improvement over conventional techniques. The deep CNN method showed a 70 % enhancement in subsidence prediction, with the study area experiencing over 38 cm of subsidence between 2014 and 2020.