Increasing the Accuracy of Solar Radiation Interpolation Using Auxiliary Data Obtained from DEM in Cokriging

Accurate calculation of the amount of solar radiation in an area has an effective role in the climatology and agriculture of the region, estimating the rate of evapotranspiration, site selection of solar power plant and using photovoltaic systems. Point measurements at ground stations using pyranometers are the most accurate method of estimating solar radiation, in which measurements are extended to a continuous surface using spatial interpolation methods. The main purpose of this study is to increase the accuracy of solar radiation zoning in Iran using Cokriging method. For this purpose, the amount of solar radiation was first calculated using the Digital Elevation Model (DEM) and the Solar Radiation spatial toolbox in ArcGIS software. Then the correlation coefficient (R) between the obtained values from the software with the values of solar radiation measured at ground stations was calculated. According to R = 0.713 between these two data, by Cokriging method, these two data were combined and the continuous surface of solar radiation for the whole of the country was calculated. The results showed that the calculation of solar radiation using Area Solar GIS tool is not accurate enough compared to ground data, but the combination of the two data, while affecting the topography in the calculation of solar radiation, increases the interpolation accuracy by 11%. Therefore, although existing models may not be accurate enough to estimate solar radiation on a national scale compared to terrestrial data, they can be used to improve the accuracy of terrestrial data zoning. According to the final map, most regions of the country, except the northern and northwestern regions, receive solar radiation above the global average (340 w/m2)

Developing an Agent-Based Simulation System for Post-Earthquake Operations in Uncertainty Conditions: A Proposed Method for Collaboration among Agents

Agent-based modeling is a promising approach for developing simulation tools for natural hazards in different areas, such as during urban search and rescue (USAR) operations. The present study aimed to develop a dynamic agent-based simulation model in post-earthquake USAR operations using geospatial information system and multi agent systems (GIS and MASs, respectively). We also propose an approach for dynamic task allocation and establishing collaboration among agents based on contract net protocol (CNP) and interval-based Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods, which consider uncertainty in natural hazards information during agents’ decision-making. The decision-making weights were calculated by analytic hierarchy process (AHP). In order to implement the system, earthquake environment was simulated and the damage of the buildings and a number of injuries were calculated in Tehran’s District 3: 23%, 37%, 24% and 16% of buildings were in slight, moderate, extensive and completely vulnerable classes, respectively. The number of injured persons was calculated to be 17,238. Numerical results in 27 scenarios showed that the proposed method is more accurate than the CNP method in the terms of USAR operational time (at least 13% decrease) and the number of human fatalities (at least 9% decrease). In interval uncertainty analysis of our proposed simulated system, the lower and upper bounds of uncertain responses are evaluated. The overall results showed that considering uncertainty in task allocation can be a highly advantageous in the disaster environment. Such systems can be used to manage and prepare for natural hazards