Çakmakçı, Mehmet | Kınacı, Cumali | Bayramoğlu, Mahmut | Yıldırım, Yılmaz

Article | 2010 | Expert Systems with Applications37 ( 2 ) , pp.1369 - 1373

Effluent iron concentration is an important water quality criterion used for the assessment of the performance of rapid sand filters, in addition to other criteria. This study deals with the prediction of effluent iron concentrations by adaptive neuro-fuzzy (ANFIS) model with input parameters including filter hydraulic loading rate, influent iron concentration, bed porosity and operation time. With trying various types of membership functions, two rule base generation methods, namely subtractive clustering and grid partition were used for a first order Sugeno type inference system. Models were evaluated using root mean squared error . . . (RMSE), index of agreement (IA) and R2 as statistical performance parameters. The fit between experimental results and model outputs showed good agreement for tap water and deionized water; testing RMSE values were 36.33 and 7.66 µg/L, the IA values were 0.996 and 0.971, and R2 values were 0.99 and 0.89, respectively. It was concluded that neuro-fuzzy modeling may be successfully used to predict effluent iron concentration in sand filtration. © 2009 Elsevier Ltd. All rights reserved Daha fazlası Daha az

Yıldırım, Yılmaz

Article | 2010 | Fresenius Environmental Bulletin19 ( 7 ) , pp.1320 - 1327

In this study, an adaptive neuro-fuzzy logic method has been proposed to estimate roadside NO2 concentration levels. In the analysis, data from summer and winter seasons were modeled separately and five statistical measures, namely, RMSE, IA, R2, NMSE and FB, were used for modeling evaluation. The available data (N=5797) for 2003 were divided into three categories: training, testing and checking, to set up the ANFIS model. The model was trained using 4923 data with 13 input variables consisting of air quality and meteorological data. Summer season data set (between July and August, N=361) and winter season data set (between December . . . and February, N=361) have been separately used for prediction (testing) purposes. In general, RMSE (4.78 and 4.53), NMSE (0.029 and 0.026) and FB (0.03 and 0.01) values are low but IA (0.96 and 0.98) and R2 (0.92 and 0.95) are reasonably high enough to predict the observed values for winter and summer season test data, respectively. In addition, the FOEX values show that the model slightly under-predicts for all input parameters. Overall, the statistical measures confirm the adequacy of the model for predicting NO2 levels in M25 Roadside for winter and summer season test data. © by PSP Daha fazlası Daha az