Monthly Temperature Prediction using ANNs and ANFIS (Case Study: Tehran City)
Journal Title: Journal of Science and today’s world - Year 2013, Vol 2, Issue 4
Abstract
Temperature is one of the most important parameters among climate parameters. Prediction of monthly temperature is very important in water resources management, agriculture, and many other fields. For this purpose, different empirical or semi-empirical models and time series analysis are used. Recently, new intelligent methods such as ANNs and ANFIS have largely been applied in all scientific and engineering fields. In this research, the two intelligent models including the ANNs and ANFIS for monthly minimum, maximum, and mean temperatures estimation were developed in synoptic station of Tehran, Iran. Minimum, maximum, and mean data were divided to three parts including training, testing, and checking data for a month ago, two months ago, and three months ago. After introducing the data to each model, the training, testing, and checking of models were done separately and results were analyzed. Results showed that the ANNs are better that ANFIS for predicting of temperatures.
Authors and Affiliations
Karim Khodaie , Zahra Korehbandi , Kazem Shahverdi
Keywords
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