Gene Expression Programming Technique Research Articles

MONTHLY REFERENCE EVAPOTRANSPIRATION MODELING USING GENE EXPRESSION PROGRAMMING FROM MINIMUM CLIMATIC DATA

Evapotranspiration is a key factor for water balance, irrigation scheduling, and crop yield. Even though, Penman-Monteith FAO-56 (PMF-56) equation had estimated the highest accuracy reference evapotranspiration (ETo), it required complete climatic records, which may not be easily available. The present study is to develop and evaluate a gene expression programming (GEP) model for estimating mean monthly ETo using minimal number of climatic data. Climatic variables used to estimate ETo are maximum and minimum air temperature (Tmax and Tmin), mean relative humidity (RH), solar radiation (Rs), and wind speed at 2-m height (u2). The data used in the analysis refer to 32 weather stations available at different locations in Egypt through the CLIMWAT database. The PMF-56 method was used as the reference standard for evaluating the developed GEP models based on statistical criteria such as: index of agreement (IA) and the root mean square error (RMSE). The results showed that the accuracy of the GEP model significantly improved when either RH or u2 was used as additional input variables. The GEP model with the inputs: Tmax, Tmin, RH, andu2 showed the highest IA (0.991 and 0.990) and the lowest RMSE (0.426 mm d-1 and 0.430 mm d-1) for training and testing sets, respectively. Comparing the results of GEP models with other empirical models showed that ETo values estimated by using the GEP models are more accurate. Accordingly, the GEP technique can be employed successfully in modeling ETo from the available climatic data and allowed for providing simple algebraic formulas.

New tools to determine bubble point pressure of crude oils: Experimental and modeling study

Adequate Knowledge of reservoir fluid characteristics (e.g., bubble point pressure) plays a crucial role while conducting modeling/simulation of production processes in petroleum reservoirs. Although many efforts have been made to obtain proper correlations for prediction of bubble point pressure (BPP) of reservoir fluids, there is still relatively high magnitude of error with the developed predictive tools available in the literature. To fill this lacuna, a robust and effective technique, called gene expression programming (GEP), is employed to determine BPP of crude oil samples as a function of temperature, oil composition, molecular weight of C7+, and specific gravity of C7+. The GEP method is built based on the experimental (or real) data used for training and testing phases in order to develop an appropriate correlation. The previous predictive methods are also reported in this study and employed to calculate BPP as a function of independent parameters when the same data bank is utilized. Comparing the outputs obtained from the previous models with the BPP values predicted by the GEP technique, it was found that the GEP approach exhibits higher accuracy and lower uncertainty on the basis of statistical analysis in terms of coefficient of determination (R2) and mean squared error (MSE). Great precision attained in this study through using GEP recommends linking reservoir simulator packages with the GEP tool when thermodynamic properties such as BPP are required for modeling and optimization purposes.

Predicting house price via gene expression programming

PurposeThe housing sector is one of the main sources of economic growth in both developing and developed countries. Although many methods for modeling house prices have been proposed, each has its own limitations. The present paper aims to propose gene expression programming (GEP) as a new approach for prediction of housing price.Design/methodology/approachThis study introduces gene expression programming (GEP) as a new approach for predicting housing price. This is the first time that this metaheuristic method is used in the housing literature.FindingsThe housing price model based on the gene expression programming is compared with a least square regression model that is derived from a stepwise process. The results indicate that the GEP‐based model provides superior performance to the traditional regression.Originality/valueData used in this study is derived from the Household Income and Expenditure Survey (HIES) in Iran that is conducted by the Statistical Center of Iran (SCI). Housing price model is estimated by administering the questionnaires of this survey in Hamedan Province. To show the applicability of the derived model by GEP technique, it is verified applying parts of the data, namely test data sets that were not included in the modeling process.

Estimation of dimension and time variation of local scour at short abutment

Accurate prediction of the local scour at abutments is an important criterion to design a safe depth for the bridge foundation. In this paper, the dimension and variation of local scour with time at a vertical-wall abutment were investigated experimentally under clear-water conditions. The multiple linear regression (MLR), gene expression programming (GEP) and artificial neural networks (ANNs), feed forward back propagation and radial basis function were used to predict the time variation of scour depth at a short abutment. Results indicated that the dimension of the scour hole in the x-direction ranged from 3L to 5L upstream and downstream of the abutment, respectively, and also 4L in the y-direction. Statistical analysis showed that, although the ANNs technique produced better results (R 2 = 0.997, RMSE = 0.0113 and MAE = 0.0071) in comparison with the GEP (R 2 = 0.959, RMSE = 0.068 and MAE = 0.044) and MLR techniques (R 2 = 0.958, RMSE = 0.059 and MAE = 0.041), both GEP and MLR are more practical methods. Finally, sensitivity analysis indicated that the local scour was greatly affected by the three studied parameters in the following order, time ratio (t/t e) > abutment length ratio (L/y) > velocity ratio (U/U c).

Estimation of Daily Suspended Sediment Load by Using Wavelet Conjunction Models

Accurate estimation of sediment loads is important for the management and construction of water resources projects. In the first part of this study, the convenient gene expression programming (GEP), neuro-fuzzy (NF), and artificial neural network (ANN) techniques were applied to estimate suspended sediment loads by using recorded daily river discharge and sediment load data. These models were compared with one another in terms of the coefficient of determination, root mean square error, mean absolute error, variance accounted for, and Nash-Sutcliffe statistic criteria. It was found that the GEP model performed better than the NF and ANN models. In the second part of this study, the discrete wavelet conjunction models with convenient GEP, NF, and ANN techniques were constructed and compared with one another. Comparison results indicated that the wavelet conjunction models significantly increased the accuracy of single GEP, NF, and ANN models in suspended sediment estimation. The wavelet-GEP model performed better than the wavelet-NF and wavelet-ANN models.

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

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.

Predicting axial capacity of driven piles in cohesive soils using intelligent computing

An accurate prediction of pile capacity under axial loads is necessary for the design. This paper presents the development of a new model to predict axial capacity of pile foundations driven into cohesive soils. Gene expression programming technique (GEP) has been utilized for this purpose. The data used for development of the GEP model is collected from the literature and comprise a series of in-situ driven piles load tests as well as cone penetration test (CPT) results. The data are divided into two subsets: training set for model calibration and independent validation set for model verification. Predictions from the GEP model are compared with experimental data and with predictions of number of currently adopted CPT-based methods. The results have demonstrated that the GEP model performs well with coefficient of correlation, mean and probability density at 50% equivalent to 0.94, 0.96 and 1.01, respectively, indicating that the proposed model predicts pile capacity accurately.

A robust data mining approach for formulation of geotechnical engineering systems

PurposeThe complexity of analysis of geotechnical behavior is due to multivariable dependencies of soil and rock responses. In order to cope with this complex behavior, traditional forms of engineering design solutions are reasonably simplified. Incorporating simplifying assumptions into the development of the traditional models may lead to very large errors. The purpose of this paper is to illustrate capabilities of promising variants of genetic programming (GP), namely linear genetic programming (LGP), gene expression programming (GEP), and multi‐expression programming (MEP) by applying them to the formulation of several complex geotechnical engineering problems.Design/methodology/approachLGP, GEP, and MEP are new variants of GP that make a clear distinction between the genotype and the phenotype of an individual. Compared with the traditional GP, the LGP, GEP, and MEP techniques are more compatible with computer architectures. This results in a significant speedup in their execution. These methods have a great ability to directly capture the knowledge contained in the experimental data without making assumptions about the underlying rules governing the system. This is one of their major advantages over most of the traditional constitutive modeling methods.FindingsIn order to demonstrate the simulation capabilities of LGP, GEP, and MEP, they were applied to the prediction of: relative crest settlement of concrete‐faced rockfill dams; slope stability; settlement around tunnels; and soil liquefaction. The results are compared with those obtained by other models presented in the literature and found to be more accurate. LGP has the best overall behavior for the analysis of the considered problems in comparison with GEP and MEP. The simple and straightforward constitutive models developed using LGP, GEP and MEP provide valuable analysis tools accessible to practicing engineers.Originality/valueThe LGP, GEP, and MEP approaches overcome the shortcomings of different methods previously presented in the literature for the analysis of geotechnical engineering systems. Contrary to artificial neural networks and many other soft computing tools, LGP, GEP, and MEP provide prediction equations that can readily be used for routine design practice. The constitutive models derived using these methods can efficiently be incorporated into the finite element or finite difference analyses as material models. They may also be used as a quick check on solutions developed by more time consuming and in‐depth deterministic analyses.

Estimation of Suspended Sediment Yield in Natural Rivers Using Machine-coded Linear Genetic Programming

Estimation of suspended sediment yield is subject to uncertainty and bias. Many methods have been developed for estimating sediment yield but they still lack accuracy and robustness. This paper investigates the use of a machine-coded linear genetic programming (LGP) in daily suspended sediment estimation. The accuracy of LGP is compared with those of the Gene-expression programming (GEP), which is another branch of GP, and artificial neural network (ANN) technique. Daily streamflow and suspended sediment data from two stations on the Tongue River in Montana, USA, are used as case studies. Root mean square error (RMSE) and determination coefficient (R2) statistics are used for evaluating the accuracy of the models. Based on the comparison of the results, it is found that the LGP performs better than the GEP and ANN techniques. The GEP was also found to be better than the ANN. For the upstream and downstream stations, it is found that the LGP models with RMSE = 175 ton/day, R2 = 0.941 and RMSE = 254 ton/day, R2 = 0.959 in test period is superior in estimating daily suspended sediments than the best accurate GEP model with RMSE = 231 ton/day, R2 = 0.941 and RMSE = 331 ton/day, R2 = 0.934, respectively.

Short-term load forecasting of power systems by gene expression programming

Short-term load forecasting is a popular topic in the electric power industry due to its essentiality in energy system planning and operation. Load forecasting is important in deregulated power systems since an improvement of a few percentages in the prediction accuracy will bring benefits worth of millions of dollars. In this study, a promising variant of genetic programming, namely gene expression programming (GEP), is utilized to improve the accuracy and enhance the robustness of load forecasting results. With the use of the GEP technique, accurate relationships were obtained to correlate the peak and total loads to average, maximum and lowest temperatures of day. The presented model is applied to forecast short-term load using the actual data from a North American electric utility. A multiple least squares regression analysis was performed using the same variables and same data sets to benchmark the GEP models. For more verification, a subsequent parametric study was also carried out. The observed agreement between the predicted and measured peak and total load values indicates that the proposed correlations are capable of effectively forecasting the short-term load. The GEP-based formulas are relatively short, simple and particularly valuable for providing an analysis tool accessible to practicing engineers.

554. Mechanotron converters and their application in measuring technique

Artificial neural networks (ANNs) and gene expression programming (GEP) were compared to estimate daily reference evapotranspiration (ETref) under arid conditions. The daily climatic variables were collected by 13 meteorological stations from 1980 to 2010. The ANN and GEP models were trained on 65% of the climatic data and tested using the remaining 35%. The generalised Penman–Monteith (PMG) model was used as a reference target for evapotranspiration values, with hc varies from 5 to 105 cm with increment of a centimetre. The developed models were spatially validated using climatic data from 1980 to 2010 taken from another six meteorological stations. The results showed that the eight ETref models developed using the ANN technique were slightly more accurate than those developed using the GEP technique. The ANN models’ determination coefficients (R2) ranged from 67.6% to 99.8% and root mean square error (RMSE) values ranged from 0.20 to 2.95 mm d-1. The GEP models’ R2 values ranged from 64.4% to 95.5% and RMSE values ranged from 1.13 to 3.1 mm d-1. Although the GEP models performed slightly worse than the ANN models, the GEP models used explicit equations.