Determination of radiation amplification factor of atmospheric aerosol from the surface UV irradiance measurement at Gwangju, Korea

Summary This study investigated the impact of atmospheric aerosols on surface ultraviolet (UV) irradiance at Gwangju, Korea (35°13′N, 126°50′E). Data analyzed included surface UV irradiance measured by UV radiometers from June 1998 to April 2001 and the aerosol optical depth (AOD) in the visible range determined from a rotating shadow-band radiometer (RSR). The radiation amplification factor (RAF) of ozone for UV-B (280–315 nm) at Gwangju was 1.32–1.62. Values of the RAF of aerosols (RAF_AOD) for UV-A and UV-B were 0.18–0.20 and 0.22–0.26, respectively. Authors’ addresses: Jeong Eun Kim, Advanced Environmental Monitoring Research Center (ADEMRC), Gwangju Institute of Science and Technology (GIST) and Korea Meteorological Administration (KMA); Seong Yoon Ryu, Advanced Environmental Monitoring Research Center (ADEMRC), Gwangju Institute of Science and Technology (GIST) and Division of Metrology, Korea Research Institute of Standards and Science (KRISS); Young Joon Kim, Advanced Environmental Monitoring Research Center (ADEMRC) Gwangju Institute of Science and Technology (GIST), 1 Oryong-dong, Buk-gu, Gwangju 500-712, Republic of Korea.     

The Application of Artificial Neural Networks to Landslide Susceptibility Mapping at Janghung, Korea

The purpose of this study was to develop techniques for landslide susceptibility using artificial neural networks and then to apply these to the selected study area at Janghung in Korea. Landslide locations were identified from interpretation of satellite images and field survey data, and a spatial database of the topography, soil, forest, and land use. Thirteen landslide-related factors were extracted from the spatial database. These factors were then used with an artificial neural network to analyze landslide susceptibility. Each factor's weight was determined by the back-propagation training method. Five different training sets were applied to analyze and verify the effect of training. Then the landslide susceptibility indices were calculated using the back-propagation weights, and susceptibility maps were constructed from Geographic Information System (GIS) data for the five cases. Landslide locations were used to verify results of the landslide susceptibility maps and to compare them. The artificial neural network proved to be an effective tool for analyzing landslide susceptibility.