Simulation and Prediction of Water Allocation Using Artificial Neural Networks and a Spatially Distributed Hydrological Model

Lake Koronia is located in the North part of Greece and is protected by the Ramsar Convention of wetlands. A deficit in the water balance has been presented at the last twenty years due to the excessive water consumption for agricultural uses. This research is an attempt to simulate water flow with MIKE SHE model in order to observe how the water is allocated in the study area. The results of water flow module used for the estimation of Lake’s water balance for 4 hydrological years (2008-2012). Furthermore the Artificial Neural Networks (ANNs) was used for the prediction of water flow in two sub-catchments. The coefficient correlation (R) was found for Bogdanas (0.9) and Kolxikos (0.86). The Root Mean Square Error (RMSE) and the Mean Absolute Percentages Error (MAPE) were also calculated in order to evaluate the quality of the ANNs results.


Issue Date:
Dec 31 2014
Publication Type:
Journal Article
DOI and Other Identifiers:
1804-1930 (Other)
PURL Identifier:
http://purl.umn.edu/196580
Published in:
AGRIS on-line Papers in Economics and Informatics, Volume 06, Number 4
Page range:
101-111
Total Pages:
11
JEL Codes:
GA; IN
Series Statement:
VI
4




 Record created 2017-04-01, last modified 2017-04-27

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