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Kisi O.,Canik Basari University | Zounemat-Kermani M.,Shahid Bahonar University of Kerman
Water Resources Management | Year: 2014

This study compares two different adaptive neuro-fuzzy inference systems, adaptive neuro-fuzzy inference system (ANFIS) with grid partition (GP) method and ANFIS with subtractive clustering (SC) method, in modeling daily reference evapotranspiration (ET 0). Daily climatic data including air temperature, solar radiation, relative humidity and wind speed from Adana Station, Turkey were used as inputs to the fuzzy models to estimate daily ET 0 values obtained using FAO 56 Penman Monteith (PM) method. In the first part of the study, the effect of each climatic variable on FAO 56 PM ET 0 was investigated by using fuzzy models. Wind speed was found to be the most effective variable in modeling ET 0. In the second part of the study, the effect of missing data on training, validation and test accuracy of the neuro-fuzzy models was examined. It was found that the ANFIS-GP model was not affected by missing data while the test accuracy of the ANFIS-SC model slightly decreases by increasing missing data's percent. In the third part of the study, the effect of training data length on training, validation and test accuracy of the ANFIS models was investigated. It was found that training data length did not significantly affect the accuracy of ANFIS models in modeling daily ET 0. ANFIS-SC model was found to be more sensitive to the training data length than the ANFIS-GP model. In the fourth part of the study, both ANFIS models were compared with the following empirical models and their calibrated versions; Valiantzas' equations, Turc, Hargreaves and Ritchie. Comparison results indicated that the three-and four-input ANFIS models performed better than the corresponding empirical equations in modeling ET 0 while the calibrated two-parameter Ritchie and Valiantzas' equations were found to be better than the two-input ANFIS models. © 2014 Springer Science+Business Media Dordrecht.


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.


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.


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.


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..


Kisi O.,Canik Basari University
Irrigation Science | Year: 2013

The accuracy of a least square support vector machine (LSSVM) in modeling of reference evapotranspiration (ET0) was examined in this study. The daily weather data, solar radiation, air temperature, relative humidity and wind speed of two stations, Glendale and Oxnard, in southern district of California, were used as inputs to the LSSVM models to estimate ET0 obtained using the FAO-56 Penman-Monteith equation. In the first part of the study, LSSVM estimates were compared with those of the following empirical models: Priestley-Taylor, Hargreaves and Ritchie methods. The comparison results indicated that the LSSVM performed better than the empirical models. In the second part of the study, the LSSVM results were compared with those of the conventional feed-forward artificial neural networks (ANN). It was found that the LSSVM models were superior to the ANN in modeling ET0 process. © 2012 Springer-Verlag.


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.


The study investigates the ability of FG (fuzzy genetic) approach in modeling solar radiation of seven cities from Mediterranean region of Anatolia, Turkey. Latitude, longitude, altitude and month of the year data from the Adana, K. Maras, Mersin, Antalya, Isparta, Burdur and Antakya cities are used as inputs to the FG model to estimate one month ahead solar radiation. FG model is compared with ANNs (artificial neural networks) and ANFIS (adaptive neruro fuzzzy inference system) models with respect to RMSE (root mean square errors), MAE (mean absolute errors) and determination coefficient (R2) statistics. Comparison results indicate that the FG model performs better than the ANN and ANFIS models. It is found that the FG model can be successfully used for estimating solar radiation by using latitude, longitude, altitude and month of the year information. FG model with RMSE=6.29MJ/m2, MAE=4.69MJ/m2 and R2=0.905 in the test stage was found to be superior to the optimal ANN model with RMSE=7.17MJ/m2, MAE=5.29MJ/m2 and R2=0.876 and ANFIS model with RMSE=6.75MJ/m2, MAE=5.10MJ/m2 and R2=0.892 in estimating solar radiation. © 2013 Elsevier Ltd.


Kisi O.,Canik Basari University | Sanikhani H.,Islamic Azad University at Sāveh
International Journal of Climatology | Year: 2015

Accurate estimation of precipitation is an important issue in water resources engineering, management and planning. The accuracy of four different soft computing methods, adaptive neuro-fuzzy inference system (ANFIS) with grid partition (GP), ANFIS with subtractive clustering (SC), artificial neural networks (ANN) and support vector regression (SVR), is investigated in predicting long-term monthly precipitation without climatic data. The periodicity component, longitude, latitude and altitude data from 50 stations in Iran are used as inputs to the applied models. The ANFIS-GP model is found to perform generally better than the other models in predicting long-term monthly precipitation. The SVR model provides the worst estimates. The maximum correlations are found to be 0.935 and 0.944 for the ANFIS-SC and SVR models in Fasa station, respectively. The highest correlations of the ANFIS-GP and ANN models are found to be 0.964 and 0.977 for the Bam and Tabas (Zabol) stations. The minimum correlations are 0.683 and 0.661 for the ANFIS-GP and SVR models in Urmia station while the ANFIS-SC and ANN models provide the minimum correlations of 0.696 and 0.785 in the Sari and Bandar Lengeh stations, respectively. The comparison results show that the long-term monthly precipitations of any site can be successfully predicted by ANFIS-GP model without any weather data. The monthly and annual precipitations are also mapped and evaluated by using the optimal ANFIS-GP model in the study. The precipitation maps revealed that the highest amounts of precipitation occur in the north, southwestern and west regions, while the lowest values are seen in the east and southeastern parts of the Iran. © 2015 Royal Meteorological Society.


Kisi O.,Canik Basari University | Cengiz T.M.,Canik Basari University
Water Resources Management | Year: 2013

The applicability of fuzzy genetic (FG) approach in modeling reference evapotranspiration (ET0) is investigated in this study. Daily solar radiation, air temperature, relative humidity and wind speed data of two stations, Isparta and Antalya, in Mediterranean region of Turkey, are used as inputs to the FG models to estimate ET0 obtained using the FAO-56 Penman-Monteith equation. The FG estimates are compared with those of the artificial neural networks (ANN). Root mean-squared error, mean absolute error and determination coefficient statistics were used as comparison criteria for the evaluation of the models' accuracies. It was found that the FG models generally performed better than the ANN models in modeling ET0 of Mediterranean region of Turkey. © 2013 Springer Science+Business Media Dordrecht.

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