Runoff Prediction under Climate Change: Artificial Neural Network Approach

OMID, BOZORG-HADDAD and PARISA, SARZAEIM (2015) Runoff Prediction under Climate Change: Artificial Neural Network Approach. In: Third International Conference on Advances In Civil, Structural and Environmental Engineering- ACSEE 2015, 10-11 October, 2015, Zurich, Switzerland.

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Nowadays climate change phenomena is identified as an environmental issue all over the world. In result of human industrial activities, measurements of green house gases are increased which leads to global warming and its sequences. In the last decades, concerns about average temperature rising and its potential destructive results were noted. Also water resources which is one of the most effective in human life, are not in security. So to efficient management, it is worthy to calculate the climate change impacts on important parameters in water resources such as runoff. But rainfallrunoff models are complex and in other hand data mining models had impressive progress in recent years and are helpful to predict runoff. Genetic programming (GP), artificial neural network (ANN) and support vector machine (SVM) are such data mining tools that have many uses in various fields. In the present paper, climate precipitation and temperature are estimated by HadCM3 AOGCM and statistic downscaling and then by using ANN runoff was calculated in Aydoghmoush basin, Iran. The result shows that ANN could be an efficient and simple tool to this purpose.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: climate change, runoff prediction, artificial neural network.
Depositing User: Mr. John Steve
Date Deposited: 19 Apr 2019 12:03
Last Modified: 19 Apr 2019 12:03

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