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Samsun, Turkey

Ay M.,Bozok University | Kisi O.,Canik Basari University | Kisi O.,Erciyes University
Journal of Environmental Engineering (United States) | Year: 2012

The aim of this study is to examine the accuracy of two different artificial neural network (ANN) techniques, the multilayer perceptron (MLP) and radial basis neural network (RBNN), to estimate dissolved oxygen (DO) concentration. The ANN results are compared with multilinear regression (MLR) model. The neural network model is developed using experimental data collected from the upstream (USGS Station No: 07105530) and downstream (USGS Station No: 07106000) stations on Foundation Creek, CO. The input variables used for the ANN models are water pH, temperature, electrical conductivity, and discharge. The determination coefficient (R2), mean absolute error (MAE), and root mean square error (RMSE) statistics are used for the evaluation of the applied models. The MLP and RBNN models are also compared with MLR model in estimating the DO of the downstream station by using the input parameters of the upstream station. Comparison results indicate that the RBNN model performs better than the MLP and MLR models. © 2012 American Society of Civil Engineers. Source

Kisi O.,Canik Basari University
Journal of Hydrology | Year: 2012

The ability of least square support vector machine (LSSVM) is investigated in this paper for modeling discharge. -suspended sediment relationship. The daily stream flow and suspended sediment concentration data from two stations on the Eel River in California were used as case studies. In the first part of the study, the LSSVM was compared with those of the artificial neural networks (ANNs) and sediment rating curve (SRC) in the prediction of upstream and downstream station sediment data, separately. Two different algorithms, Levenberg-Marquardt and Conjugate Gradient, were employed for the ANN applications. For evaluating the ability of the models, root mean square errors, mean absolute errors and determination coefficient statistics were used. Comparison results showed that the LSSVM model was able to produce better results than the ANN models. LSSVM and ANN models were found to be better than the SRC model for the upstream station. For the downstream station, however, SRC model outperformed the LSSVM and ANN models. In the second part of the study, the models were compared to each other in estimation of downstream suspended sediment data by using data from both stations. It was found that the LSSVM model performs slightly better than the ANN models and both models performed much better than the SRC model. © 2012 Elsevier B.V. Source

Sanikhani H.,Islamic Azad University at Tabriz | Kisi O.,Canik Basari University
Water Resources Management | Year: 2012

This paper demonstrates the application of two different adaptive neuro-fuzzy (ANFIS) techniques for the estimation of monthly streamflows. In the first part of the study, two different ANFIS models, namely ANFIS with grid partition (ANFIS-GP) and ANFIS with sub clustering (ANFIS-SC), were used in one-month ahead streamflow forecasting and the results were evaluated. Monthly flow data from two stations, the Besiri Station on the Garzan Stream and the Baykan Station on the Bitlis Stream in the Firat-Dicle Basin of Turkey were used in the study. The effect of periodicity on the model's forecasting performance was also investigated. In the second part of the study, the performance of the ANFIS techniques was tested for streamflow estimation using data from the nearby river. The results indicated that the performance of the ANFIS-SC model was slightly better than the ANFIS-GP model in streamflow forecasting. © 2012 Springer Science+Business Media B.V. Source

Kisi O.,Canik Basari University
Journal of Hydrology | Year: 2013

Estimating pan evaporation is very important for monitoring, survey and management of water resources. This study proposes the application evolutionary neural networks (ENN) for modeling monthly pan evaporations. Solar radiation, air temperature, relative humidity, wind speed and pan evaporation data from two stations, Antalya and Mersin, in Mediterranean Region of Turkey are used in the study. In the first part of the study, ENN models are compared with those of the fuzzy genetic (FG), neuro-fuzzy (ANFIS), artificial neural networks (ANN) and Stephens-Stewart (SS) methods in estimating pan evaporations of Antalya and Mersin stations, separately. Comparison results indicate that the ENN models generally perform better than the FG, ANFIS, ANN and SS models. In the second part of the study, models are compared with each other in estimating Mersin's pan evaporations using input data of both stations. Results reveal that the ENN models performed better than the FG, ANFIS and ANN models. It was concluded that monthly pan evaporations can be successfully estimated by the ENN method. The performance of the ENN model with full weather data as inputs presents 0.749 and 0.759. mm of mean absolute error for the Antalya and Mersin stations, respectively. © 2013 Elsevier B.V.. Source

Kisi O.,Canik Basari University
Journal of Hydrology | Year: 2013

This study investigates the applicability of Mamdani and Sugeno fuzzy genetic approaches in modeling reference evapotranspiration (ET0). The daily air temperature, solar radiation, relative humidity and wind speed data from Adana and Antalya stations, Turkey, were used as inputs to the fuzzy genetic models for estimating ET0 obtained using the standard FAO-56 Penman-Monteith equation. Comparison of two different fuzzy genetic methods indicated that the Sugeno fuzzy genetic (SFG) method was faster and had a better accuracy than the Mamdani fuzzy genetic (MFG) method in modeling daily ET0. SGF and MFG models were also compared with the recently proposed Valiantzas's equations and following empirical models: Hargreaves-Samani and Priestley-Taylor methods. Root mean-squared errors (RMSE), mean-absolute errors (MAE) and determination coefficient (R2) were used for the evaluation of the models' performances. Results revealed that the SFG and MFG models were performed better than the empirical models in modeling daily ET0 process. Comparison of the two different fuzzy genetic approaches indicated that the SFG had a better accuracy than the MFG. For the Adana and Antalya stations, the SFG1 model with RMSE=0.219 and 111mm/day, MAE=0.097 and 0.080mm/day and R2=0.983 and 0.998 in validation period was found to be superior in modeling daily ET0 than the other models, respectively. © 2013 Elsevier B.V. Source

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