Scholarly record
GEOGRAPHIC INFORMATION SYSTEMS AND ARTIFICIAL NEURAL NETWORKS COUPLING MODEL TO PREDICT MEAN YEARLY PRECIPITATION IN STUDY AREAS, CASE STUDY: DENA SUB BASIN, KOHGILOUYE PROVINCE, IRAN
Abstract
One of the main purposes of GIS is to provide organization with increased information and analyzing spatial data. A GIS can become increasingly more valuable in prediction of quantitative or qualitative variables in no measured locations when coupled to Artificial Intelligence (AI). ANN is one of the AI fields that is a highly simplified model for the biological structure of a human brain. An ANN when linked to GIS, can be useful for evaluating, monitoring, decision making and of course predicting. In this study, an ANN is used to determine the effect of location and elevation of each pixel in base raster layer on mean yearly precipitation. That is conducted by design a Conceptual Model (CM) and any raster calculations in a GIS frame work. Finally, microzonation of precipitation in study area has been encountered. Performing the sensitivity analysis for each factor is the next step in this research.
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