ABSTRACT The adaptive neuro-fuzzy inference system (Anfis) is considered for flow over rectangula... more ABSTRACT The adaptive neuro-fuzzy inference system (Anfis) is considered for flow over rectangular side weirs located on a straight channel as a substantial part of distribution channels in irrigation systems and treatment units. To estimate the outflow over a rectangular sharp-crested side weir, the discharge coefficient in the side weir equation needs to be determined in accordance with the effective dimensionless parameters F-1 (Froude number), L/b (weir length/channel width), L/h(1) (weir length/flow depth), and p/h(1) (weir height/flow depth). The discharge coefficient of rectangular side weirs was determined using 843 laboratory test results. The performance of the Anfis model is compared with multilinear and nonlinear regression models. The criteria used for the evaluation of the performance of models are root mean square errors (RMSE), mean absolute errors (MAE) and correlation coefficient (R) statistics. Comparison results indicated that the Anfis technique could be successfully employed in modelling discharge coefficients. It is found that the Anfis model with RMSE of 0.043 in the test period is superior in estimation of discharge coefficient than the multiple nonlinear and linear regression models with RMSE of 0.054 and 0.106, respectively.
... OZGUR KISI1, IBRAHIM YUKSEL2 & EMRAH DOGAN3 1Erciyes University, ... more ... OZGUR KISI1, IBRAHIM YUKSEL2 & EMRAH DOGAN3 1Erciyes University, Engineering Faculty, Civil Engineering Department, Hydraulics Division, 38039 Kayseri ... Tayfur (2002) developed an ANN model for sheet sediment transport and indicated that the ANN could perform ...
The ,prediction and estimation of suspended ,sediment ,concentration are investigated by using ,m... more The ,prediction and estimation of suspended ,sediment ,concentration are investigated by using ,multi-layer perceptrons ,(MLP). The fastest MLP training algorithm, that is the Levenberg-Marquardt algorithm, is used for optimization of the network weights for data from two stations on the Tongue River in Montana, USA. The first part of the study deals with prediction and estimation of upstream and
ABSTRACT This paper investigates the ability of five different data-driven methods, artificial ne... more ABSTRACT This paper investigates the ability of five different data-driven methods, artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS) with grid partition (GP), ANFIS with subtractive clustering (SC), support vector regression (SVR) and gene expression programming (GEP), in predicting long-term monthly temperatures by using data from 50 stations in Iran. The periodicity component (month of the year), station latitude, longitude and altitude values were used as inputs to the applied models to predict the long-term monthly temperatures. The overall accuracy of the SVR model was found to be better than that of the other models. The GEP model gave the worst estimates. The maximum determination coefficient (R2) values were found to be 0.996, 0.999, 0.997 and 0.959 for the ANN, ANFIS-GP, ANFIS-SC and GEP models in Karaj and Qazvin stations, respectively. The highest R2 value (0.999) of SVR model was found for the Tabas station. The minimum R2 values were respectively found as 0.988, 0.946 and 0.985 for the ANFIS-GP, ANFIS-SC and SVR models in Bandar Abbas station while the ANN and GEP models gave the minimum R2 values of 0.982 and 0.886 in the Abadan and Kerman stations, respectively. The results indicated that the long-term monthly temperatures of any site can be successfully estimated by data-driven methods applied in this study using geographical inputs. The interpolated maps of temperatures were also obtained by using the optimal SVR model and evaluated in the study. The temperature maps showed that the highest temperatures were occurred in the southeastern and central parts of the Iran.
... (1997), Refsgaard (1997), and others. ... The upstream station (station no: 6307830) below Br... more ... (1997), Refsgaard (1997), and others. ... The upstream station (station no: 6307830) below Brandenberg Bridge near Ashland and the downstream station (station no: 6308500) at Miles City are operated by the US Geological Survey (USGS). ...
ground-level ozone (O3) has been a serious air pollution problem for several decades and in many ... more ground-level ozone (O3) has been a serious air pollution problem for several decades and in many metropolitan areas, due to its adverse impact on the human respiratory system. Therefore, to reduce the risks of O3 related damages, developing, maintaining and improving short term ozone forecasting models is needed. This paper presents the results of two prognostic models including gene expression programming (GEP), which is a variant of genetic programming (GP), and multiple linear regression (MLR) to forecast ozone levels in real-time up to 6 hours ahead at four stations in Bilbao, Spain. The inputs to the GEP were meteorological conditions (wind speed and direction, temperature, relative humidity, pressure, solar radiation and thermal gradient), hourly ozone levels and traffic parameters (number of vehicles, occupation percentage and velocity), which were measured in the years of 1993–94. The performances of developed models were compared with observed values and were evaluated usin...
The International Journal of Ocean and Climate Systems, 2014
ABSTRACT The accuracy of three different data-driven methods, namely, Gene Expression Programming... more ABSTRACT The accuracy of three different data-driven methods, namely, Gene Expression Programming (GEP), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANN), is investigated for hourly sea water level prediction at the Mukho Station in the East Sea (Sea of Japan). Current and four previous level measurements are used as input variables to predict sea water levels up to 1, 24, 48, 72, 96 and 120 hours ahead. Three statistical evaluation parameters, namely, the correlation coefficient, the root mean square error and the scatter index are used to assess how the models perform. Investigation results indicate that, when compared to measurements, for +1h prediction interval, all three models perform well (with average values of R = 0.993, RMSE = 1.3 cm and SI = 0.04), with slightly better results produced by the ANNs and ANFIS, while increasing the prediction interval degrades model performance.
ABSTRACT In this study, a nonparametric technique to set up a river stage forecasting model based... more ABSTRACT In this study, a nonparametric technique to set up a river stage forecasting model based on empirical mode decomposition (EMD) is presented. The approach is based on the use of the EMD and artificial neural networks (ANN) to forecast next month's monthly streamflows. The proposed approach is applied to a real case study. The data from station on the Kizilirmak River in Turkey was used. The mean square errors (MSE), mean absolute errors (MAE) and correlation coefficient (R) statistics were used for evaluating the accuracy of the EMD-ANN model. The accuracy of the EMD-ANN model was then compared to the artificial neural networks (ANN) model. The results showed that EMD-ANN approach performed better than the ANN in predicting stream flows. The most accurate EMD-ANN model had MSE=0.0132, MAE=0.0883 and R=0.8012 statistics, respectively.
ABSTRACT The adaptive neuro-fuzzy inference system (Anfis) is considered for flow over rectangula... more ABSTRACT The adaptive neuro-fuzzy inference system (Anfis) is considered for flow over rectangular side weirs located on a straight channel as a substantial part of distribution channels in irrigation systems and treatment units. To estimate the outflow over a rectangular sharp-crested side weir, the discharge coefficient in the side weir equation needs to be determined in accordance with the effective dimensionless parameters F-1 (Froude number), L/b (weir length/channel width), L/h(1) (weir length/flow depth), and p/h(1) (weir height/flow depth). The discharge coefficient of rectangular side weirs was determined using 843 laboratory test results. The performance of the Anfis model is compared with multilinear and nonlinear regression models. The criteria used for the evaluation of the performance of models are root mean square errors (RMSE), mean absolute errors (MAE) and correlation coefficient (R) statistics. Comparison results indicated that the Anfis technique could be successfully employed in modelling discharge coefficients. It is found that the Anfis model with RMSE of 0.043 in the test period is superior in estimation of discharge coefficient than the multiple nonlinear and linear regression models with RMSE of 0.054 and 0.106, respectively.
... OZGUR KISI1, IBRAHIM YUKSEL2 & EMRAH DOGAN3 1Erciyes University, ... more ... OZGUR KISI1, IBRAHIM YUKSEL2 & EMRAH DOGAN3 1Erciyes University, Engineering Faculty, Civil Engineering Department, Hydraulics Division, 38039 Kayseri ... Tayfur (2002) developed an ANN model for sheet sediment transport and indicated that the ANN could perform ...
The ,prediction and estimation of suspended ,sediment ,concentration are investigated by using ,m... more The ,prediction and estimation of suspended ,sediment ,concentration are investigated by using ,multi-layer perceptrons ,(MLP). The fastest MLP training algorithm, that is the Levenberg-Marquardt algorithm, is used for optimization of the network weights for data from two stations on the Tongue River in Montana, USA. The first part of the study deals with prediction and estimation of upstream and
ABSTRACT This paper investigates the ability of five different data-driven methods, artificial ne... more ABSTRACT This paper investigates the ability of five different data-driven methods, artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS) with grid partition (GP), ANFIS with subtractive clustering (SC), support vector regression (SVR) and gene expression programming (GEP), in predicting long-term monthly temperatures by using data from 50 stations in Iran. The periodicity component (month of the year), station latitude, longitude and altitude values were used as inputs to the applied models to predict the long-term monthly temperatures. The overall accuracy of the SVR model was found to be better than that of the other models. The GEP model gave the worst estimates. The maximum determination coefficient (R2) values were found to be 0.996, 0.999, 0.997 and 0.959 for the ANN, ANFIS-GP, ANFIS-SC and GEP models in Karaj and Qazvin stations, respectively. The highest R2 value (0.999) of SVR model was found for the Tabas station. The minimum R2 values were respectively found as 0.988, 0.946 and 0.985 for the ANFIS-GP, ANFIS-SC and SVR models in Bandar Abbas station while the ANN and GEP models gave the minimum R2 values of 0.982 and 0.886 in the Abadan and Kerman stations, respectively. The results indicated that the long-term monthly temperatures of any site can be successfully estimated by data-driven methods applied in this study using geographical inputs. The interpolated maps of temperatures were also obtained by using the optimal SVR model and evaluated in the study. The temperature maps showed that the highest temperatures were occurred in the southeastern and central parts of the Iran.
... (1997), Refsgaard (1997), and others. ... The upstream station (station no: 6307830) below Br... more ... (1997), Refsgaard (1997), and others. ... The upstream station (station no: 6307830) below Brandenberg Bridge near Ashland and the downstream station (station no: 6308500) at Miles City are operated by the US Geological Survey (USGS). ...
ground-level ozone (O3) has been a serious air pollution problem for several decades and in many ... more ground-level ozone (O3) has been a serious air pollution problem for several decades and in many metropolitan areas, due to its adverse impact on the human respiratory system. Therefore, to reduce the risks of O3 related damages, developing, maintaining and improving short term ozone forecasting models is needed. This paper presents the results of two prognostic models including gene expression programming (GEP), which is a variant of genetic programming (GP), and multiple linear regression (MLR) to forecast ozone levels in real-time up to 6 hours ahead at four stations in Bilbao, Spain. The inputs to the GEP were meteorological conditions (wind speed and direction, temperature, relative humidity, pressure, solar radiation and thermal gradient), hourly ozone levels and traffic parameters (number of vehicles, occupation percentage and velocity), which were measured in the years of 1993–94. The performances of developed models were compared with observed values and were evaluated usin...
The International Journal of Ocean and Climate Systems, 2014
ABSTRACT The accuracy of three different data-driven methods, namely, Gene Expression Programming... more ABSTRACT The accuracy of three different data-driven methods, namely, Gene Expression Programming (GEP), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANN), is investigated for hourly sea water level prediction at the Mukho Station in the East Sea (Sea of Japan). Current and four previous level measurements are used as input variables to predict sea water levels up to 1, 24, 48, 72, 96 and 120 hours ahead. Three statistical evaluation parameters, namely, the correlation coefficient, the root mean square error and the scatter index are used to assess how the models perform. Investigation results indicate that, when compared to measurements, for +1h prediction interval, all three models perform well (with average values of R = 0.993, RMSE = 1.3 cm and SI = 0.04), with slightly better results produced by the ANNs and ANFIS, while increasing the prediction interval degrades model performance.
ABSTRACT In this study, a nonparametric technique to set up a river stage forecasting model based... more ABSTRACT In this study, a nonparametric technique to set up a river stage forecasting model based on empirical mode decomposition (EMD) is presented. The approach is based on the use of the EMD and artificial neural networks (ANN) to forecast next month's monthly streamflows. The proposed approach is applied to a real case study. The data from station on the Kizilirmak River in Turkey was used. The mean square errors (MSE), mean absolute errors (MAE) and correlation coefficient (R) statistics were used for evaluating the accuracy of the EMD-ANN model. The accuracy of the EMD-ANN model was then compared to the artificial neural networks (ANN) model. The results showed that EMD-ANN approach performed better than the ANN in predicting stream flows. The most accurate EMD-ANN model had MSE=0.0132, MAE=0.0883 and R=0.8012 statistics, respectively.
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