As one of the hazardous pollutants, ozone (O3), has significant adverse effects on urban dwellers' health. Predicting the concentration of ozone in the air can be used to control and prevent unpleasant effects. In this paper, an attempt was made to find out two empirical relationships incorporating multiple linear regression (MLR) and Gene Expression Programming ((GEP)) to predict the ozone concentration in the vicinity of Zrenjanin, Serbia. For this purpose, 1564 data sets were collected, each containing 18 input parameters such as concentrations of air pollutants (SO2, CO, H2S, NO, NO2, NOx, PM10, benzene, toluene, m-and p-xylene, o-xylene, ethylbenzene), and meteorological conditions (wind direction, wind speed, air pressure, air temperature, solar radiation, and relative humidity (RH)). In contrast, the output parameter was ozone concentrate. The correlation coefficient and root mean squared error for the MLR were 0. 61 and 21. 28, respectively, while the values for the (GEP) were 0. 85 and 13. 52, respectively. Also, to evaluate these two methods' validity, a feed-forward artificial neural network (ANN) with an 18-10-5-1 structure has been used to predict the ozone concentration. The correlation coefficient and root mean squared error for the ANN were 0. 78 and 16. 07, respectively. Comparisons of these parameters revealed that the proposed model based on the (GEP) is more reliable and more reasonable for predicting the ozone concentrate. Also, the sensitivity analysis of the input parameters indicated that the air temperature has the most significant influence on ozone concentration variations.

Determining the amount of solar radiation reaching the ground in each location is important for many practical applications such as the use of solar energy. However, in many stations due to the high cost of installing and maintaining solar radiation measuring equipment, the direct measurement of this parameter is limited. Hence, in the past decades, some empirical equations have been developed to estimate the received solar radiation that needs to calibrate for use in any location. In this study in order to evaluate the performance of Gene Expression Programming method for solar radiation simulation, daily meteorological data of Ahwaz synoptic station were used. For this purpose, day of the year parameter and daily data of the minimum temperature, maximum temperature and average temperature, relative humidity, sunshine hours and the extraterrestrial radiation of three consecutive years (2006-2008) in Ahwaz were selected as input for (GEP) models. The performance of the (GEP) model in comparison with experimental methods angstrom and Hargreaves-Samani were studied also. Generally the results showed that, (GEP) model had better performance than empirical equations for estimates of solar radiation and among of the empirical equation used in this study, the Angstrom equation was accurate compared to the Hargreaves-Samani model.

1. Introduction: Water level variations are highly sensitive to many environmental factors, such as lunar and solar gravitational attraction, waves and currents, atmospheric pressure and wind forcing, as well as many other dynamic presumably nonlinear and interconnected physical variables.

Accurate estimation as one of the important elements of the hydrological cycle of evaporation play an important role in the development and management of water resources plays countries facing a water crisis. so far methods, and many empirical formulas in estimating the nonlinear process evaporation of the basin, which provide high accuracy and as well as access to all the input parameters problem or measure they need a lot of time and money. The aim of this study was to compare of ability of Gene Expression Programming and neuro-fuzzy methods for estimation of evaporation in South Khorasan province. For this purpose daily data collected from occurs six synoptic stations during years 1990-2010. Input parameters are daily mean temperature, relative humidity, max and min temperature, wind speed and sun shine. Finally, to evaluate of models and compare them criteria such as coefficient of determination (R2) and Mean Bias Error (MBE) and Root Mean Square Error (RMSE) and MBE is used. Compression of result in test period showed the (GEP) Model has better performance than the neuro-fuzzy model to estimate the daily evaporation. The best result of (GEP) is R2=0.79, RMSE=1.44 and MBE=0.35 in Boshroie station and worst is R2=0.7, RMSE=2.6 and MBE=1.2 in Birgand station. Also the results showed the main factor in estimation of evaporation is mean temperature in all station except that mine temperature is impact parameter.

Background and Objectives: With the emergence of computers and geographic information system (GIS), as well as access to spatial digital data, different methods of data mining, modeling and estimation of soil properties found their place in soil sciences. Data mining of soil properties using computer-based statistical methods uncovers hidden patterns in the database which ultimately leads to the fitness of a model for estimation of soil properties. These methods can be used in the scorpan equation. Two main components of scorpan model include environmental variables and learning program. In the present study, three different methods including multiple linear regression (MLR), artificial neural network (ANN) and Gene Expression Programming ((GEP)) as “f’ function in scorpan model were evaluated and compared in estimating of soil properties using auxiliary data such as vegetation data, topography and remote sensing data.Material and Methods: The study area with an area of 1225 ha was located in Bajgiran rangelands, Khorasan Razavi province, Iran. In order to investigate vegetation cover and soil 137 units were investigated in which 3-5 plots were selected with a distance of 10 meters apart along an accidental transect and plant species names and numbers besides vegetation percentage were recorded. Next, one soil sample was taken from each transect (Totally 137 soil sample). Train attributes derived from digital elevation model; different bands derived from the ETM and used for computing spectral indices; and plant diversity indices were calculated using Simpson and Shannon-Wiener. These obtained parameters were used as covariate in estimating calcium carbonate equivalent, clay, density, nitrogen, carbon, sand, silt and saturated moisture capacity. Data deduction was done by PCA analysis to deduct the number of input data for ANN and (GEP) models and finally, Normalization and standardization were carried out on the data.Results: The results obtained from the evaluation of three numerical methods based on root mean square error (RMSE), mean bias error (MBE) and coefficient of determination (R2) showed that ANN model had the highest accuracy in estimating soil properties, given the higher coefficients of determination for calcium carbonate equivalent, clay, density, nitrogen, carbon, sand, silt and saturated moisture capacity with the values of 0.72, 0.46, 0.69, 0.67, 0.77, 0.62, 0.7 and 0.85, respectively, moreover, lower RMSE with the values of 7.46, 4.46, 0.08, 0.03, 0.27, 5.6, 3.5 and 3.4, respectively. ANN could explain 60-85 percent of variability of soil properties, among which the best estimates were for saturated moisture capacity and soil organic carbon with R2=0.85 and R2=0.77, respectively.Conclusion: Evaluating the estimation of soil properties through three numerical models introduced ANN as the most accurate model. ANN validation results showed that mean bias error (MBE) for estimated soil properties were close to zero and this confirms that the fitting has been created unbiased by model. Furthermore, the low RMSE of model verified accurate estimation of soil variables. The results also indicate that (GEP) had higher accuracy than the linear regression method for most soil properties.

One of the main concerns in the aquifers adjacent to oil facilities is the leakage of LNAPLs. Since remediation processes costly and time consuming, so the first step in these systems is determining design goals. Often the most important goal of these systems is to maximize pollutant removal and minimize the cost. Identifying the thickness of LNAPL and its fluctuations can determine the type of recovery method and thus can be effective on the amount of removal and the cost of the implementation. In this study, three methods of Gene Expression Programming ((GEP)), adaptive neuro-fuzzy inference system (ANFIS) and multivariate linear regression (MLR) were used to estimate and predict the LNAPL level. Input variables are groundwater level elevation and discharge rate of LNAPL and the output variable is the LNAPL level elevation. The results of the three models were analyzed by statistical parameters and it was determined that (GEP) technique has better results and could be used successfully in predicting LNAPL level fluctuations in recovery processes. Also, the (GEP) model provides an equation for predicting the LNAPL level that can be used in the field to predict the elevation of the LNAPL level.

One of the strategies for efficient use of water resources in agriculture is development of new irrigation systems including pressurized irrigation methods. Field evaluation and accurate data of pressurized irrigation systems performance are considered as the crucial tools for correct operation of these systems in different terms plan. One of the most important performance evaluation criteria in the design of pressurized irrigation systems, such as solid-set sprinkle, is water distribution uniformity index. On the other hand, field measurements of the water distribution uniformity index in different climatic and hydraulic conditions and projects executive specifications require spending too much time and cost. Therefore, use of indirect methods such as intelligent models can be useful. By checking the studies in simulation of water distribution uniformity coefficient in sprinkle irrigation systems, no research was found using of adaptive neuro-fuzzy inference system (ANFIS) method. Therefore, the present study aimed to check the performance of adaptive neuro-fuzzy inference system and compare with results of Gene Expression Programming method in estimating the water distribution uniformity coefficient. This research was done in solid-set sprinkle irrigation system in different climatic, hydraulic and physical conditions. The research method consisted of two parts: field measurements and simulations by ANFIS and Gene Expression Programming intelligent models. For this purpose, a solid-set sprinkle irrigation system with considering of different arrangements of pipes and sprinklers were designed and performed. Then, 54 field experiments were done to evaluate the performance of the solid-set sprinkle irrigation system. In each experiment, water cans were used to determine the water distribution uniformity coefficient. Input parameters of adaptive neuro-fuzzy inference system and Gene Expression Programming were the combination of: climatic factors (average of temperature, relative humidity, average of wind speed and direction) and physical factors (arrangement and different distances of sprinklers and sprinkler model (amount of output volumetric flow rate)). Output parameter, in all simulations, was the water distribution uniformity coefficient (cu) in percent. 70 percent of obtained field data were used for learning of the models and 30 percent for testing them. In order to compare and evaluate the performance of the intelligent models in estimating the water distribution uniformity coefficient, Pearson correlation coefficient statistics, root mean square error and mean absolute error were used. Results showed that Generally with increasing laterals distance and wind speed, the amount of water distribution uniformity coefficient decreased. Field observations were indicated in the same terms, Ambo’ s model sprinkler had the better results in water distribution uniformity than vyr-155. The simulation results showed that use of all input data, including volumetric flow rate of sprinkler, sprinklers distances, wind speed, wind direction, relative humidity and average of temperature as well as considering the effective radius equal one, lead to gain the best results. So in ANFIS model, the maximum amount of correlation coefficient (R) and root mean square error (RMSE) for the test phase were obtained equal to 0. 77 and 7. 7 %, respectively. By eliminating the parameters of relative humidity and average of temperature from the model inputs, no significant changes were observed in the results. While by eliminating wind speed parameter, results of model's output (including RMSE index) were changed significantly. It can be concluded that the water distribution uniformity coefficient values in the experiments were strongly influenced by wind speed. Also, the best performance of Gene Expression Programming model was related to the combination of the input data including: volumetric flow rate of sprinkler, sprinklers distance, wind speed and direction. So, the maximum amount of correlation coefficient achieved in the test phase was equal to 0. 72 and the lowest amount of RMSE was 7. 13%. One of the advantages of Gene Expression Programming model comparing with ANFIS model and other intelligent models is offering the optimal mathematical equation between the dependent variable of uniformity coefficient and the other independent variables (inputs). Generally, the performance of methods had little difference between Gene Expression Programming and adaptive neuro-fuzzy. Inference and sensitivity of the models showed the temperatures and wind speed had the lowest and the most effect on water distribution uniformity coefficient changes, respectively. The estimated amounts of checking for water distribution uniformity coefficient indicated that intelligent models, as well as factors effect such as wind speed and sprinklers distances, have been able to simulate the reducing amount of water distribution uniformity.

Spur dikes are one of the common methods to protect rivers against erosion. Scouring around the spur dike is an important factor that can disorder the structural performance. Using protective spur dike is proper technique reduce the scour amount. In this research, the GMDH and (GEP) model used in order to evaluate and estimate the effect of various parameters of protective spur dike on scour depth around the main spur dike. Important parameters consist of protective spur dike angle (Ө ), protective spur dike length ( ), main spur dike length ( ), distance from main spur dike (X), flow intensity ( ) and Froude number ( ) are considered as the model inputs. Results of training set and testing set indicate that GMDH model is better than (GEP) model as the MAE and RMSE error in the testing set data are reduced from 0. 063 and 0. 086 (in (GEP) model) to 0. 045 and 0. 061 (in GMDH model) respectively. Also, the Nash-Sutcliffe criteria increased from 0. 51 (in (GEP) model) to 0. 75 (in GMDH model). In the following, using the GMDH and (GEP) models and according to the nature of the problems, the equations are suggested to predict the scour depth reduction in the first main spur dike. The results of sensitivity analysis indicate that the most effective parameter in decreasing the scour depth around the first main spur dike is ( ).

Concrete shear resistance is one of the most important parameters in concrete structures design. Suitable understanding of concrete behavior against shear forces has always been a problem for researchers. Many different methods have been developed for measuring and predicting shear strength. One of the most common method, is to find a relationship between compressive and shear strength. In this study, an attempt was made to provide a practical relationship between compressive and shear strength using an experimental design with compressive strength in the range of 20-30 MPa which resulted in 50 specimens. Gene Expression Programming ((GEP)) method has been used to estimate shear resistance and five relationships have been provided. In order to select the best relationship, in addition to considering error measurement parameters such as correlation coefficient, Willmotts index, etc., the criterion of simplicity and easy use of the relation is also considered.

Lateral spreading is one of the most significant destructive and catastrophic phenomena associated with liquefaction caused by earthquake and it can cause very serious damage to structures and engineering facilities. The aim of this study is to evaluate liquefaction-induced lateral spreading and  nd new relations using Gene Expression Programming ((GEP)), which is a new and developed Generation of Genetic algorithms approaches. Since there are complicated, nonlinear, and higher-order relationships among many factors affecting the lateral spreading, (GEP) was assumed to be capable of finding complex and accurate relationships among the involved factors. This study includes three main stages: (i) compiling available database (484 data); (ii) dividing data into training and testing categories; and (iii) building new models and proposing new relationships to predict ground displacement in free face, gentle slope, and General ground conditions. The results of modeling each of these different ground conditions were presented in the form of mathematical equations. At the end, the final (GEP) models for 3 different cases of ground conditions were compared with Multiple Linear Regression (MLR) and other published models. The statistical parameters indicated the higher accuracy of (GEP) models over other relations.