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http://hdl.handle.net/2080/2475
Title: | Development of Anfis Model with Optimised Inputs to Reduce the Computational Cost and Time for Ground Level Ozone Forecasting |
Authors: | Gorai, A K |
Keywords: | ANFIS model Ozone Forecasting ANFIS for ozone concentrations |
Issue Date: | Mar-2016 |
Publisher: | Center for Health and the Global Environment. |
Citation: | 10th International Conference on Air Quality-Science and Application, Milan, Italy, 14-18 March 2016 |
Abstract: | This study aims to develop adaptive neurofuzzy inference system (ANFIS) for forecasting of daily ozone (O3) concentrations in the atmosphere of a mega city. The ANFIS model predictor considers the value of seven meteorological factors (pressure, temperature, relative humidity, dew point, visibility, wind speed, and precipitation), NO2 concentration, and previous day’s ozone concentration in different combinations as the inputs to predict the 1day advance and same day ozone concentration. Collinearity tests were conducted to eliminate the redundant input variables. A forward selection (FS) method is used for selecting the different subsets of input variables. The method reduces the computational cost and time. The performances of the models were evaluated on the basis of four statistical indices [(coefficient of determination (R2), normalized mean square error (NMSE), index of agreement (IOA), and fractional bias (FB)]. |
Description: | Copyright belongs to proceeding publisher |
URI: | http://hdl.handle.net/2080/2475 |
Appears in Collections: | Conference Papers |
Files in This Item:
File | Description | Size | Format | |
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2016_Amit_Development.pdf | 127.8 kB | Adobe PDF | View/Open |
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