Thermal Anomaly Detection prior to earthquakes with training artificial neural networks with ant colony optimization

Remote sensing techniques made it possible to study thermal anomalies prior to major earthquakes regardless of complications in comprehending earthquake mechanisms. Thermal pre-cursors are one the main resources for earthquake prediction. In this article, land surface temperature, atmospheric temperature, surface latent heat flux and outgoing long-wave radiation have been studied to detect anomalies prior to Varzaghan (August 11, 2012), Boushehr (April 9, 2013) and Saravan (April 16, 2013) earthquakes.
To detect earthquake related anomalies, time series of each pre-cursor has been produced within the period of earthquake, land surface temperature and atmospheric temperature were acquired from MODIS products, surface latent heat flux from GLDAS library and outgoing long-wave radiation from AIRS products. These time series were predicted by an artificial neural network with ant colony optimization training method. The results of this study were compared with artificial neural network with Levenberg-Marquardt training algorithm. It has been shown that 10 to 13 days before Varzaghan earthquake, anomalies has appeared in all of the mentioned precursors, in case of Boushehr earthquake 6 to 9 days before the event, anomalies appeared in atmospheric temperature and outgoing long-wave radiation and also a strong anomaly appeared in surface latent heat flux 2 days prior to earthquake and in Saravan earthquakes anomalies have been detected 5 to 8 days before the earthquake in all of the studied thermal pre-cursors.