فهرست مطالب محمدرضا سراجیان مارالان

In order to determine the spatial pattern of the probability of fire in the forests of Boyerahmad city, Belife evidence function models and support vector machines were used. For this purpose, at first 145 past fire positions were reported, MODIS data and field surveys were recorded using GPS, of which 70% were used for modeling and 30% for model validation. Next, 15 factors (altitude, slope gradient, slope direction, topographic position index, topographic moisture index, surface curvature, distance from village, distance from river, distance from road, geological formations, NDVI, land use, evapotranspiration annual, annual rainfall and annual temperature) were selected to assess the fire risk and maps were prepared. After performing a linear test between the independent variables, the Belife evidence function and the support vector machine models were used to create the fire zoning map. For modeling, past fire locations were identified and 70% of the data collected were used as training data for modeling and 30% for model validation. The results of the fire map study showed that areas with very high sensitivity cover 40% of the area. The results of the validation of the performance of the Belife evidence function models showed that the area under the curve was equal to 72.2% and the support vector machine with the area below the curve was 83%.Predication. Results of the current research can be used to plan and manage future fire hazards in the study area.

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.