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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

AUTHOR/S: M. AHMADI, S. PARTANI, M. PARSOON
Sunday 1 August 2010 by Libadmin2009

9th International Multidisciplinary Scientific GeoConference - SGEM2009, www.sgem.org, SGEM2009 Conference Proceedings/ ISBN 10: 954-91818-1-2, June 14-19, 2009, Vol. 2, 199-206 pp

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.

Keywords: ANN, GIS, Conceptual Model, Precipitation

PAPER 2009/s13.3: 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

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