Aytac Guven

Genetic Programming to Predict Bridge Pier Scour

Bridge-pier scour is a significant problem for the safety of bridges. Extensive laboratory and field studies have been conducted examining the effect of relevant variables. This note presents an alternative to the conventional regression-based equations (HEC-18 and regression equation developed by the writers), in the form of artificial neural networks (ANNs) and genetic programming (GP). There had been 398 data sets of field measurements that were collected from published literature and were used to train the network or evolve the program. The developed network and evolved programs were validated by using the observations that were not involved in the training. The performance of GP was found more effective when compared to regression equations and ANNs in predicting the scour depth at bridge piers.

New Approach for Stage–Discharge Relationship: Gene-Expression Programming

This study presents gene-expression programming (GEP), which is an extension to genetic programming, as an alternative approach to modeling stage–discharge relationship. The results obtained are compared to more conventional methods, stage rating curve and multiple linear regression techniques. Statistical measures such as average, standard deviation, minimum and maximum values, as well as criteria such as mean square error and determination coefficient, the coefficient of efficiency, and the adjusted coefficient of efficiency are used to measure the performance of the models developed by employing GEP. Also, the explicit formulations of the developed GEP models are presented. Statistics and scatter plots indicate that the proposed equations produce quite satisfactory results and perform superior to conventional models.

Daily pan evaporation modeling using linear genetic programming technique

This paper investigates the ability of linear genetic programming (LGP), which is an extension to genetic programming (GP) technique, in daily pan evaporation modeling. The daily climatic data, air temperature, solar radiation, wind speed, pressure and humidity of three automated weather stations, Fresno, Los Angeles and San Diego in California, are used as inputs to the LGP to estimate pan evaporation. The LGP estimates are compared with those of the Gene-expression programming (GEP), which is another branch of GP, multilayer perceptrons (MLP), radial basis neural networks (RBNN), generalized regression neural networks (GRNN) and Stephens–Stewart (SS) models. The performances of the models are evaluated using root mean square errors (RMSE), mean absolute error (MAE) and determination coefficient (R2) statistics. Based on the comparisons, it was found that the LGP technique could be employed successfully in modeling evaporation process from the available climatic data.

Prediction of lateral outflow over triangular labyrinth side weirs under subcritical conditions using soft computing approaches

Highlights? ANN and GEP techniques could be employed in modeling discharge coefficient. ? The performance of ANN and GEP models is better than that of regression models. ? The value of RMSE of the RBNN model is lower than that of the other models. This paper presents the results of laboratory model testing of triangular labyrinth side weirs located on the straight open channel flume. The discharge capacity of triangular labyrinth side weirs is estimated by using two different artificial neural network (ANN) techniques, that is, the radial basis neural network (RBNN) and generalized regression neural network (GRNN), and gene-expression programming (GEP), which is an extension to genetic programming. 2500 laboratory test results are used for determining discharge coefficient of triangular labyrinth side weirs. The performance of the ANN and GEP models is compared with multi-linear and nonlinear regression models. Comparison results indicated that the neural computing and gene-expression programming techniques could be employed successfully in modeling discharge coefficient from the available experimental data.

New algebraic formulations of evapotranspiration extracted from gene-expression programming in the tropical seasonally dry regions of West Africa

Within hydrological nonlinear complex functions, taking only few parameters into the modeling process is still a challenging task. The present paper has for objective to investigate for the first time the predictive ability of the Gene-expression Programming (GEP) for modeling reference evapotranspiration (ETo) using routing weather data from the tropical seasonally dry regions of West Africa in Burkina Faso. The regions under study are located in three agro-climatic zones, Bobo Dioulasso in the Guinea Savanna zone, and Dédougou and Fada N’Gourma in the Sudan zone, and Ouagadougou in the Sudano-Sahelian Savanna zone. Several meteorological data combinations are used as inputs to the GEP to estimate ETo, and their performances are evaluated using R2 and RMSE. Statistically, it was found that GEP can be an alternative to the conventional methods, and its accuracy improves significantly up to R2 (0.979) and RMSE(0.108) when critical variables are taking into account in the model. The results revealed that GEP model is fairly a promising approach with the advantage to provide successfully simple algebraic formulas ease to use without recourse to the full set of meteorological data requirement for accurately estimate ETo in Sub-Saharan Africa regions.

Evapotranspiration modeling using linear genetic programming technique.

The study investigates the accuracy of linear genetic programming (LGP), which is an extension to genetic programming (GP) technique, in daily reference evapotranspiration ( ET0 ) modeling. The daily climatic data, solar radiation, air temperature, relative humidity, and wind speed from three stations, Windsor, Oakville, and Santa Rosa, in central California, are used as inputs to the LGP to estimate ET0 obtained using the FAO-56 Penman-Monteith equation. The accuracy of the LGP is compared with those of the support vector regression (SVR), artificial neural network (ANN), and those of the following empirical models: the California irrigation management system Penman, Hargreaves, Ritchie, and Turc methods. The root-mean-square errors, mean-absolute errors, and determination coefficient ( R2 ) statistics are used for evaluating the accuracy of the models. Based on the comparison results, the LGP is found to be superior alternative to the SVR and ANN techniques.

An explicit neural network formulation for evapotranspiration

Abstract An explicit neural network formulation (ENNF) is developed for estimating reference evapotranspiration (ET0) using daily meteorological variables obtained from the California Irrigation Management Information System (CIMIS) database. First, the ENNF is trained and tested using the CIMIS database, and then compared with five conventional ET0 models, as well as the multiple linear regression method. Statistics such as average, standard deviation, minimum and maximum values, and criteria such as mean square error and determination coefficient are used to measure the performance of the ENNF. Daily atmospheric data of four climatic stations in central California are taken into consideration in the model development and those of three other stations are used for comparison purposes. The meteorological variables employed in the formulation are solar radiation, air temperature, relative humidity and wind speed. It is concluded from the results that ENNF offers an alternative ET0 formulation, but that the gain in skill is marginal compared with simpler linear techniques. However, this finding needs to be tested using sites drawn from a wider range of climate regimes.

Discussion of “Generalized regression neural networks for evapotranspiration modelling”

Kisi (2006) used generalized regression neural networks (GRNNs) for modelling reference evapotranspiration (ET0). Results presented in the study show the potential of GRNNs as an alternative to the existing methods. Working on ET0 modelling by means of similar, soft computational methods, we think that the study contains some points, which, from our experience, require further discussion and clarification.

Genetic programming approach to predict a model acidolysis system

This paper models acidolysis of triolein and palmitic acid under the catalysis of immobilized sn-1,3 specific lipase. A gene-expression programming (GEP), which is an extension to genetic programming (GP)-based model was developed for the prediction of the concentration of major reaction products of this reaction (1-palmitoyl-2,3-oleoyl-glycerol (POO), 1,3-dipalmitoyl-2-oleoyl-glycerol (POP) and triolein (OOO). Substrate ratio (SR), reaction temperature (T) and reaction time (t) were used as input parameters. The predicted models were able to predict the progress of the reactions with a mean standard error (MSE) of less than 1.0 and R of 0.978. Explicit formulation of proposed GEP models was also presented. Considerable good performance was achieved in modelling acidolysis reaction by using GEP. The predictions of proposed GEP models were compared to those of neural network (NN) modelling, and strictly good agreement was observed between the two predictions. Statistics and scatter plots indicate that the new GEP formulations can be an alternative to experimental models.

Gene-expression programming for flip-bucket spillway scour

During the last two decades, researchers have noticed that the use of soft computing techniques as an alternative to conventional statistical methods based on controlled laboratory or field data, gave significantly better results. Gene-expression programming (GEP), which is an extension to genetic programming (GP), has nowadays attracted the attention of researchers in prediction of hydraulic data. This study presents GEP as an alternative tool in the prediction of scour downstream of a flip-bucket spillway. Actual field measurements were used to develop GEP models. The proposed GEP models are compared with the earlier conventional GP results of others (Azamathulla et al. 2008b; RMSE = 2.347, δ = 0.377, R = 0.842) and those of commonly used regression-based formulae. The predictions of GEP models were observed to be in strictly good agreement with measured ones, and quite a bit better than conventional GP and the regression-based formulae. The results are tabulated in terms of statistical error measures (GEP1; RMSE = 1.596, δ = 0.109, R = 0.917) and illustrated via scatter plots.