جستجوی مقالات مرتبط با کلیدواژه « برنامه ریزی بیان ژن » در نشریات گروه « آب و خاک »

Rivers are always faced with erosion and sediment transport. Sediment transport in rivers is one of the most complex topics in river engineering and is always the focus of experts and water engineers. This phenomenon is one of the important hydrodynamic processes that affect many hydraulic systems and water facilities and is considered one of the basic problems in the exploiting surface water resources globally. Estimating the sediment load of rivers is one of the important and practical issues in the studies and design of water engineering projects, such as the design and development of irrigation and drainage networks, water extraction from rivers, etc. Sediment concentration can be calculated by direct or indirect methods, which are usually expensive and time-consuming direct methods. Various factors affect this phenomenon, which makes their analysis difficult. Therefore, they cannot model the sedimentation phenomenon with acceptable accuracy. Hydraulic models cannot always be trusted due to the need for a lot of data, unavailability of the required data, and the inaccuracy of the data due to human error for simulating sediments. Nowadays, fuzzy and neural intelligent conductor systems, due to their ability to solve complex and nonlinear phenomena, have found many applications in various water engineering problems, including sedimentation. The purpose of this research is to evaluate and compare adaptive neural fuzzy models (ANFIS), support vector machine (SVM), gene expression programming (GEP), and group model of data handling (GMDH) in estimating the sediment load of Tirah River, Markazi Province.

In this research, first, the long-term daily statistics of temperature, rainfall, average flow rate, and sediment concentration of Hasan Abad hydrometric and sediment measuring station located on the main branch of the Tirah River were collected. Then, the data sufficiency test for analysis, checking the correlation between parameters of river discharge, precipitation, temperature with sediment discharge, and determining the long-term average of suspended sediment in the studied stations were performed. In the next step, a suitable combination of input variables was selected. The design of the input parameter pattern can be based on the relationship between flow and sediment flow parameters, rainfall, temperature, flow, and sediment flow. Of course, considering that the mentioned parameters have a historical course, therefore, the design of the input patterns of soft computing models should be done based on time delays (like what is discussed in the analysis and forecasting of time series). Determining the most appropriate time delay of the input parameters in the modeling of discharge, sediment, temperature, and rainfall, then the appropriate design of the structure of the used soft calculation models was done. In the next step, the estimation of sediment discharge using an SVM, GEP, and ANFIS group method of GMDH data control and comparison of three data mining methods, and also with the sediment rating curve and observational data. About 70 % of the research data was used as training and between 20 to 30 % for validation and testing.

Based on the statistical indicators of optimal model selection, the best performance of the SVR model has been obtained for model number one. In this model, the R2 and RMSE obtained from the model are 0.96 and 0.0047, respectively. Besides, the R2 and the RMSE error of the models in predicting suspended sediment values in the test stage are 0.95 and 0.014, respectively for the ANFIS model, and 0.50 and 4.97, respectively for the GEP model. The best performance of the ANFIS model has been obtained for model number one. In this model, the R2 and the RMSE obtained from the model are 0.95 and 0.014. The R2 and RMSE of the models in predicting suspended sediment values in the test stage are 0.96, 0.0047 for the SVR model, and 0.50, 4.97 for the GEP model, respectively. The best performance of the GEP model has been obtained for pattern number nine. In this model, the R2 and RMSE obtained from the model are 0.99 and 0.010, respectively. The R2 and the RMSE of the models in predicting the amount of suspended sediment in the test stage are respectively equal to 0.70, 0.015 for the ANFIS model and 0.78, 0.0185 tons respectively for the SVR model.

It can be seen that the performance of the GEP model was better compared to other models. SVR and ANFIS models are ranked second and third. In the next step, the best-selected pattern of ANFIS, SVM, and GEP models was used as the input of the GMDH model. First, input pattern one, which was selected as the best pattern for ANFIS and SVM models, was introduced as the input of the GMDH model. In the training and test, the values of R2 statistical indices are 0.94 and 0.99, respectively, the RMSE error value is 0.0079 and 0.0038, respectively, the MSE value is 0.000062 and 0.000015, respectively, and the MAPE values are respectively 0.007 and 0.003. In the next step, input pattern nine, which was selected as the best pattern for the GEP model, is introduced as GMDH input. In the training and test steps, the value of R2 is equal to 0.95 and 0.98 respectively, the RMSE error value is equal to 0.0077 and 0.0045 respectively, and the MSE value is equal to 0.0006 and 0.00002 respectively, and MAPE value is equal to 363 and 502. The results showed the acceptable performance of the GMDH model with the highest R2 equal to 0.99 and 0.98 and the lowest RMSE equal to 0.0038 and 0.0045, respectively.

The significant wave height is one of the basic parameters for engineering operations of coasts and marine structures. In this study, significant wave height was estimated based on three scenarios including variables 1- waves, 2- meteorological and 3- combination of the first and second scenario, in time steps without time delay, 12 and 24 hours using adaptive neural fuzzy inference system (ANFIS) and gene expression programming (GEP) and combining them with wavelet theory (WANFIS, WGEP). Also, the results indicate that due to de-noising and removal of uncertainty in the data, combined-wavelet models have provided better results than singular models. The performance improvement percentage of WGEP models compared to GEP considering the RMSE criterion was 11%, 35%, and 7% for the first to third scenarios, respectively. Most of the hydrological conditions in the seas depend on temperature changes and the amount of this parameter is an important determining factor in the environmental conditions of each region. Also, changes in temperature and surface wind change the density of sea water. Therefore, the climatic variables of the region can affect different scenarios. The results of this study and the presentation of the governing mathematical relationship for estimating the value of significant wave height by the method of gene expression programming can be very useful in the field of coastal and water resources management and engineering.

Non-linear weirs meanwhile economic advantages, have more passing flow capacity than linear weirs. These weirs have higher discharge efficiency with less free height upstream compared to linear weirs by increasing the length of the crown at a certain width. Intelligent algorithms have found a valuable place among researchers due to their great ability to discover complex and hidden relationships between effective independent parameters and dependent parameters, as well as saving money and time. In this research, the performance of support vector machine (SVM) and gene expression programming algorithm (GEP) in predicting the discharge coefficient of arched non-linear weirs was investigated using 243 laboratory data series for the first scenario and 247 laboratory data series for the second scenario. The geometric and hydraulic parameters were used in this research including the water load (HT), weir height (P), total water load ratio (HT/p), arc cycle angle (Ɵ), cycle wall angle (α), and discharge coefficient (Cd). The results of artificial intelligence showed that the combination of parameters (Cd, H_T/p, α, Ɵ) respectively in GEP and SVM algorithms in the training phase related to the first scenario (Labyrinth weir with cycle wall angle 6 degrees) were respectively equal to (R2=0.9811), (RMSE=0.02120), (DC=0.9807), and (R2=0.9896), (RMSE=0.0189), (DC=0.9871) in the second scenario (Labyrinth weir with a cycle wall angle of 12 degrees) it was equal to (R2=0.9770), (RMSE=0.0193), (RMSE=0.9768), and (R2 = 0.9908), (RMSE = 0.0128), (DC = 0.9905), which compared to other combinations has led to the most optimal output that shows the very favorable accuracy of both algorithms in predicting the coefficient the Weir discharge is arched non-linear. The results of the sensitivity analysis indicated that the effective parameter in determining the discharge coefficient of the arched non-linear Weir in GEP and in SVM is the total water load ratio parameter (HT/p). Comparing the results of this research with other researchers revealed that the evaluation indices for GEP and SVM algorithms of this research had better estimates than other researchers.

Calculation and estimation of evapotranspiration is one of the most important parameters of water management in agricultural engineering projects. The aim of this study was to evaluate the models of gene expression programming (GEP), group method of data handling (GMDH), and Multivariate adaptive regression spline (MARS) to estimating daily reference evapotranspiration. For this purpose, daily data recorded during the last 25-year period (1993-2017) of Aligudarz region (located in the east of Lorestan province) were used. 80% of the data were used for training and the remaining 20% for testing the models. The modeling results showed that only with the maximum temperature and average wind speed can evapotranspiration be estimated with very good accuracy. The error indices of GEP model in testing stage are , the error indices of MARS and GMDH models are .  Comparing the performance of the models showed that the March model performed better than the other models.

Estimating the sediment load of rivers is one of the important and practical issues in the studies and design of water engineering projects, such as the design and development of irrigation and drainage networks, water extraction from rivers, etc. Sediment concentration can be calculated by direct or indirect methods, which are usually expensive and time-consuming direct methods. Various factors affect this phenomenon, which makes their analysis difficult. Statistical and regression models are the most common methods of analysis, which often provide erroneous results due to the linear solution of these phenomena. Therefore, they cannot model the sedimentation phenomenon with acceptable accuracy. Hydraulic models cannot always be trusted due to the need for a lot of data and sometimes the unavailability of the required data and the inaccuracy of the data due to human error for simulating sediments. Nowadays, intelligent fuzzy and neural conductor systems, due to their ability to solve complex and nonlinear phenomena, have found many applications in various water engineering problems, including sedimentation. The purpose of this research is to evaluate and compare adaptive neural fuzzy models (ANFIS), machine Support vector (SVM) and GEP gene expression programming in estimating the sediment load of selected rivers in Central Province. For this purpose, the performance of three types of support vector machine (SVM), adaptive neural system (ANFIS) and GEP gene expression programming in the simulation of sediment load of rivers was studied.

In this research, first, the long-term daily statistics of temperature, rainfall, average flow rate and sediment concentration of Hasan Abad hydrometric and sediment measuring station located on the main branch of the Tirah River were collected. Then, data sufficiency test for analysis, checking the correlation between parameters of river discharge, precipitation, temperature with sediment discharge, and determining the long-term average of suspended sediment in the studied stations were performed. In the next step, a suitable combination of input variables was selected. The design of input parameter pattern can be based on the relationship between flow and sediment flow parameters, rainfall, temperature, flow flow and sediment flow. Determining the most appropriate time delay of input parameters in flow and sediment modeling. Estimation of sediment discharge using support vector machine (Svm), gene expression programming (GEP) and fuzzy-adaptive neural system (Anfis), comparing three data mining methods with each other as well as with gauge curve and observational data. The appropriate design of the structure of soft calculation models is used, in this research the number of data required for training (usually more than 70 percent), research data as training and also determining the required data (usually between 20 and 30 percent) is used for validation and testing.

In this research, The best performance of the GEP model has been obtained for pattern number 13. In this model, the R2 explanatory coefficient and the RMSE error obtained from the model are 0.97 and 0.033, respectively. The coefficient of explanation R2 and the RMSE error of the models in predicting suspended sediment values in the test phase were obtained as 0.53, 3.18 for the ANFIS model, and 0.70, 15.16 for the SVR model, respectively. Comparing the results of ANFIS and GEP models with the SVR model indicates the superiority of the GEP model in predicting the amount of suspended sediment according to Verdi model number 13. According to the obtained results, it can be seen that the performance of GEP model was better compared to other models. SVR and ANFIS models are ranked second and third. According to the obtained results, it can be said that the GEP model as a powerful and high-speed model can be used to model the suspended sediment in the Qomroud catchment area of Bagherabad station.

The results show the acceptable performance of the models compared to the gauge curve. Also, the results showed the superiority of the GEP model with the highest coefficient of determination R2 with a value of 0.99 and the lowest root mean square error RMSE in terms of tons per day with a value of 0.010. In this regard, the efficiency of the SVR model was somewhat better than the ANFIS model. The obtained results showed that all three investigated data mining methods provide far better results than the sediment gauge curve.

Awareness about spatial distribution of soil properties plays an important role in the system of crop growth and yield in the agricultural field. In this regard, genetic algorithm was used to increase the accuracy in spatial distribution of soil pHwhich combines the estimates of linear regression, gene expression programming (GEP) and geostatistics (kriging interpolation) with data related to some parts of East Azerbaijan province (Bonab,Maragheh and Ajabshir). The linear model of kriging interpolation had the minimum error. Gene expression programming and linear regression had the lowest and highest error for soil pH spatial distribution estimation, respectively, for example the mean square error, root mean square error, relative root mean square errordecreasing from linear regression to geostatistics was 47.67%,27.58%, 26.47% and scatter index and adopted mean absolute percentage error decreasing from linear regression to GEP was 23.8% and 37.03%, respectively. The use of genetic algorithm in combination method reduced the error of spatial distribution compared to the tree types of models, for example mean square error, root mean square error, relative root mean square errordecreasing from GEP to combination method was 23.33%, 11.76%,10%, respectively. The estimates of soil pH with combination method are in the alkaline range, which is consistent with the obtained data.  The minimum and maximum value of the absolute difference between measured and estimated data were at point in Bonab (0.09) and Ajabshir(0.25), respectively. The mean absolute percentage error of combination method was in acceptable range and this shows the efficiency of combination method for soil pH spatial distribution estimation.

Estimation and simulation of precipitation is considered as one of the most issues in the field of hydrology. In this study, for the first time, the long-term rainfall in Babolsar city during a 68 years period from 1951 to 2019 was predicted by using an optimized hybrid artificial intelligence (AI) technique. To end this, the gene expression programming (GEP) model was combined with the wavelet transform. To training the AI models, 70% of the observed values were utilized and 30% of these values were used to testing those models. Additionally, the autocorrelation function (ACF) was applied to identify the most influential lags and then six GEP models were developed by means of these detected lags. The number of optimized genes was selected to be four. In addition, the Multiplication function was introduced as the best linking function of the GEP model. The superior GEP model was introduced through a sensitivity analysis that the correlation coefficient (R) and scatter index (SI) for this model were calculated to be 0.571 and 0.792, respectively. The (t-1), (t-2), (t-12), and (t-36) time series lags were introduced as the most effective input lags. The coif was detected as the best mother wavelet to simulate the target function. The hybrid WGEP model simulated the values of rainfall with acceptable accuracy. In the other words, the wavelet transform enhanced the performance of the GEP model significantly. For instance, the value of variance accounted for (VAF) for the GEP and WGEP models were respectively computed to be 31.710 and 82.064

In this study, a special type of sharp-crested weir is called sharp- crested morning glory weir (SCMGW) is introduced. Due to the special shape of this weir, high measurement accuracy in low discharge, and in flood and sudden discharges, less back water profile is expected upstream. On the other hand, there is no discontinuity in their stage-discharge relationship. Assuming the occurrence of a critical flow in the weir crest, the general stage-discharge relationship was extracted for this type of weir. The next step was to correct this assumption, considering that the assumption of the occurrence of a critical flow in the weir crest is accompanied by an error, and consequently the stage-discharge relationship obtained based on this assumption is not accurate. For this purpose, by measuring 57 experiments on 5 types of this weir, the stage-discharge relationship was extracted and it was shown that for a specific stage, the measured flow is greater than the calculated analytical flow. A correction coefficient was applied to the analytical relation with the aim of matching the stage-discharge analytic relationship with the measurement. This correction factor was optimized and extracted using the gene expression programming method as a function of the weir height-to-head ratio (p / H) and the a / n ratio (a and n parameters of the weir geometric shape). The results showed that the modified discharge-stage relationship estimated the measured discharge values as R2 = 0.98, mean absolute error MAE = 0.442 liters and root mean square error of RMSE = 0.08 liters.

Intelligent algorithms, have greatly enhanced the ability of engineers to analyze and model complex hydraulic phenomena. Among them is the analysis of transient flow, which are always an important part of hydraulic pressurized pipelines .over time, the pipelines will , leak, and so on. one of the most important characteristics of transient flow is the rate of losses in pressure waves, which will be exacerbated by leakage from the pipe wall. In this research, using intelligent algorithms such as artificial neural network (ANN), genetic algorithm (GA) and gene expression programming (GP), the rate of pressure losses passes the leak hole in the pipe wall (HLPW) to be determined and its application to be compared with hydraulic analysis. Therefore, first, with the help of dimensional analysis, the effective parameters on (HLPW) were determined and then a total of 120 experiments with 6 discharges, 5 leak diameters and 4 leak locations on a polyethylene pipe with a nominal diameter of 63 mm and a length of 47 m was considered for research. The results showed that the ANN model has the best performance among intelligent algorithms to estimate and calculate (HLPW). Also, ANN, GEP, ITA and GA models with R2 equal to 0.987, 0.905, 0.891 and 0.721, respectively, have the best performance in estimation of (HLPW). In general, for estimation of (HLPW) some intelligent algorithms are more powerful than the existing hydraulic analyzer. Therefore, their use is recommended in both terms of reducing time and increasing the accuracy of calculations.

It’s valuable to predict accurately the discharge coefficient due to its direct role in the determination of the weirs passing capacity. This study was carried out using intelligent GEP and SVM algorithms based on laboratory datasets to simulate the discharge coefficient of the rectangular side weir installed in a rectangular (the first scenario) and a trapezoidal main channel (the second scenario). The most effective parameters were determined as upstream Froud number (Fr1), upstream flow depth (h1 or yo), weir height (P or W), side weir length (L), main canal width (b), sidewall slope (Z). Dimensionless parameters were extracted as (Fr1, , , ) and (Fr1, Z, ) for the first and the second scenarios, respectively. The outputs of the two algorithms were compared with experimental and regression equations using statistical indices as root mean square error (RMSE), deterministic coefficient (R2), relative error (RE), and standardized developed discrepancy ratio (ZDDR). The values of (RMSE, R2, RE, ZDDR) during the test phase for the first scenario for GEP and SVM were calculated as (0.036, 0.962, 7.76, 5.48) and (0.037, 0.952, 9.6, 3.8) and those of the superior regression model were (0.040, 0.912, 4.527, 2.439), respectively. The corresponding values in the second scenario for GEP, SVM and regression model were obtained (0.068, 0.992, 3.1, 1.14), (0.043, 0.934, 10.3, 0.71) and (0.068, 0.818, 11.9, 0.511), respectively. The results showed the superiority of intelligent algorithm over classical regression, and also the GEP to the SVM.

In this research, monthly temperature, precipitation and discharge values of Jamishan Dam in 1988-2017 have been considered as the base period. Simulation of resources and uses of Dam-d-Gomishan basin in WEAP model with existing cropping patterns in the region was investigated. To evaluate the effect of climate change on precipitation and temperature parameters in this region, the outputs of the RCP8.5 scenario of the HADGEM2_ES models were used from the cmip5 fifth report series and the output of these models was downscaled for the desired area. In this study, using the method of change factor downscaling the climatic model and monthly temperature and precipitation parameters of Jamishan Dam for the period 2021-2050 were generated. In order to investigate the runoff of the area due to climate change, SVM, GEP and IHACRES models were investigated and compared. The results of the climatic model show an average temperature increase of 1.5 °C and an increase in precipitation of 5%. In general, results of discharge forecasting in all three models indicate a decrease in runoff that the highest runoff reduction was related to SVM with 21.6% and the lowest runoff reduction was related to IHACRES with 4%. In this study, IHACRES and GEP models have more favorable accuracy than SVR method. Simulation of different cropping patterns in WEAP shows that the highest supply is equal to 74.9% in GEP model and the lowest supply is equal to 70% in SVR model which is related to cropping pattern 1 and cropping pattern 3, respectively.

The FAO Penman-Monteith is a baseline method to estimate reference evapotranspiration. In many cases, it is difficult to access all data, so replacing simpler models with ‎lower input data and appropriate accuracy is necessary. ‎ The purpose of this study is to investigate the capability of the experimental ‎models, gene expression programming, stepwise regression, and Bayesian network in estimating ‎reference evapotranspiration.‎ In this research, daily information of the Boroujerd synoptic station in the period of 1996 -2017 was used as model inputs. ‎Based on the correlation between input and output parameters, six input patterns were ‎determined for modeling. The results showed that the Kimberly-Penman model has the ‎best performance among the experimental models.‎ Gene expression programming with fourth pattern ‎‎and Default Model Operators (R2 = 0.98 and RMSE = 0.9), Bayesian Network with sixth pattern (R2=0.91 and RMSE = 1.01), and stepwise regression with sixth pattern have the most accurate patterns at R2 = 0.91 and RMSE = 0.9 in the ‎training stage.‎ Comparison of the performance of the three models showed that the gene expression ‎programming model was superior to the other two models with the Average Absolute Relative Error (AARE) of 0.12 and the Mean Ratio (MR) of 0.94.‎ The results showed that gene expression programming had an acceptable ability to estimate ‎reference evapotranspiration under the weather conditions of Boroujerd and could be introduced as a ‎suitable model.‎

The concern of the present research was to do a comparative study between the GEP, ANN and ANFIS models to estimate monthly global solar radiation. For this purpose, long-term (24-years) monthly data of global solar radiation (RS, MJ m−2), sunshine hours and air temperature (°C), from Tabriz synoptic station were used. To perform the artificial intelligence models, a new combination of inputs including monthly mean clearness index (KT), monthly temperature range (ΔT), relative sunshine hours (n/N) and extraterrestrial global solar radiation (Ra) were employed. Since the lowest values of MBE and RMSE (0. 13 and 1.97 MJ m−2 respectively) and the highest value of R2 (0.92) were obtained for ANN model, and therefore, the ANN model was selected as the best model to estimate the monthly global solar radiation. Using quarter-quarter (Q-Q) plots revealed that although the ANN model generally presents the best fit for monthly global solar radiation data, this model is found to be not successful in estimating the higher values of monthly global solar radiation data. Therefore, the application of ANN model is recommended for regions with lower solar radiation values. The performance of the ANFIS model was better than other models in covering the highest and lowest values (the first and fourth quarter). Therefore, it can be concluded that the ANFIS model gives more accurate results in the areas with the higher values of solar radiation. The findings also show that unlike previous researches which were carried out in daily scale, the performance of GEP technique for modeling monthly global solar radiation is satisfactory especially in the ranges of 250 to 800 MJ m−2. Thus, it can be inferred that GEP can be more powerful in modeling the phenomena which have low fluctuations or a limited range.

Nowadays, the prediction of river discharge is one of the important issues in hydrology and water resources; the results of daily river discharge pattern could be used in the management of water resources and hydraulic structures and flood prediction. In this research, Gene Expression Programming (GEP), parametric Linear Regression (LR), parametric Nonlinear Regression (NLR) and non-parametric K- Nearest Neighbor (K-NN) were used to predict the average daily discharge of Karun River in Mollasani hydrometric station for the statistical period of 1967-2017. Different combinations of the recorded data were used as the input pattern to predict the mean daily river discharge. The obtained esults  indicated that GEP, with R2= 0.827, RMSE= 59.45 and MAE= 26.64, had a  better performance, as compared to LR, NLR and K-NN methods, at the  validation stage for daily Karun River discharge prediction with 5-day lag, at the Mollasani station. Also, the performance of the models in the maximum discharge prediction showed that all models underestimated the flow discharge in most cases.

Surface water has always been one of the most essential pillars of water projects and, with modeling and predicting the river flow, in addition to the management and utilization of water resources, it is possible to inhibit the natural disasters such as drought and floods. Therefore, researchers have always tried to improve the accuracy of hydrological parameters estimation by using new tools and combining them. In this study, the effect of seasonal coefficients and mathematical methods of signal analysis and signal processing on wavelet transform to improve the performance of the Gene Expression Programming (GEP) model were discussed.

In the present study, for the prediction of the monthly flow of Ab Zal River, the information of Pol Zal hydrometric station in period 1972 to 2017 was used. In the next step, different input patterns need to be ready. To this purpose, the data are presented in three different modes: (a) the use of flow data and considering the role of memory up to four delays; (b) the involvement of the periodic term in both linear (?-GEP) and nonlinear (PT-GEP) states, and (c): data analysis using the Haar wavelet, Daubechies 4 (db4), Symlet (sym), Meyer (mey), and Coiflet (coif), was done in two subscales, prepared, and introduced to the GEP model. To better analyze the effect of mathematical functions used in the GEP method, two linear modes (using Boolean functions including addition, multiplication, division, and minus) and nonlinear (including quadratic functions, etc.) were considered. The wavelet transform is a powerful tool in decomposing and reconstructing the original time series. Wavelet function is a type of function that has an oscillating property and can be quickly attenuated to zero. Modeling was done based on 80% of recorded data (432 months) and the validation was done based on the remaining 20% (108 months). To evaluate the performance of each of models, statistical indices such as mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R) were used.

Results and Dissection:

 The results of linear and nonlinear GEP models showed that in both cases, the four-delay model achieved the most accuracy in river flow prediction. Still the performance of nonlinear GEP model according to RMSE (4.093 (m3/s)), MAE (2.782 (m3/s)) and R (0.660) were better than another, respectively. In the next step, the periodic term was added to the model inputs. Based on the results, the PT-GEP model with M4 pattern had the lowest error, the highest accuracy and was able to reduce the RMSE index by 8%. Then, in the third step, the river flow data were divided into approximate subdivisions and details using five wavelet functions. The most appropriate level of analysis based on the number of data was considered as number three. The results of the W-GEP modes showed an excellent performance of this method so that the model was able to reduce the RMSE statistics with 48.6%, 41.2%, and 31.1% compared to the L-GEP, NL-GEP and PT-GEP methods, respectively. Also, the best performance of the W-GEP model with the Symlet wavelet and the decomposition level of one had the highest accuracy (R=0.847) and the lowest error (RMSE =2.898 (m3/s) and MAE =1.745 (m3/s) among all models (35 models) such as linear and nonlinear, seasonal and non-seasonal and wavelet hybrid models.

Based on the results, it can be concluded that the overall use of data preprocessing methods (including seasonal coefficients and wavelet functions) has improved the performance of the GEP model. However, the combination of wavelet functions with the GEP model has significantly increased the accuracy of the modeling. Therefore, it is recommended as the most suitable tool for river flow forecasting.

Today, the effects of climate change and global warming have been demonstrated by rising greenhouse gases. Occurrence of these conditions affects hydrological processes such as precipitation and river flow, which is one of the main sources of water for the basin. In this study, the monthly values of precipitation, temperature and inflow of Jamishan Dam during the period of 1988-2017 are considered as the basic period. The output of climate models does not have the desired accuracy and spatial and temporal resolution, so it is necessary to downscale the output of CMIP5 models for the study area. In this study, using Change Factor Method (CFM), the data of two FLO_ESM and CNRM_CM5 models were downscaled under the RCP8.5 scenario and the monthly temperature and precipitation parameters of Jamshan dam were produced for the period 2021-2050. To evaluate the effect of climate change on runoff in the region, SVM, GEP and IHACRES models were studied and compared. The results of climate model indicate an increase in temperature between 0.1 to 1.4 degrees of Celsius for both FLO_ESM and CNRM_CM5 models. Also, the results of simulated precipitation in FLO_ESM and CNRM_CM5 models show that the monthly long-term average under the RCP8.5 scenario in the next period decreased 1.1 and 5.8%, respectively, as compared to the baseline period. In general, the results show a reduction in runoff in all three models (SVM, GEP and IHACRES), which the highest reduction (28.9%) is corresponded to SVM in FLO_ESM model and the lowest reduction (14.1%) is corresponded to GEP in CNRM_CM5 model. In this study, GEP and IHACRES models are more accurate than the SVM model.

Compound weirs, as an appropriate solution, can be used for measuring flow discharges with reasonable sensitivity and accuracy in a wide range of flows. However, sediment laden flows will cause problems in their functions. This problem can be coped with trough combining gates and compound weirs. In this research, based on dimensionless parameters obtained from dimensional analysis and using a smart and new method named Gene-Expression Programming (GEP), an equation has been proposed for prediction of discharge coefficient in compound curved weir-gate structures. The aim of this research is investigation of hydraulic characteristics of compound curved weir-gate as well as providing a relationship for prediction of discharge coefficient of these structures, based on the hydraulic and geometric parameters.

The experiments were done in a rectangular flume by 9 meter of length and 0.4 meter of width. Then, the obtained data were used to estimate the discharge coefficient. This study has been assessed by using of variations of flow discharge, gate opening, weir opening and angle of curve as parameters of experimental channel. Also, the effects of hydraulic of flow and the geometry of duct were considered in the discharge coefficient estimation. After these, Gene Expression Programming (GEP) was used to set an equation to determine the discharge coefficient with a high accuracy.

The discharge coefficient were calculated by analyzing the obtained experimental data. Next, an equation was determined by method of dimensional analysis and gene expression programming tools to estimate the discharge coefficient by correlation coefficient (R2) of 0.992 and 0.662 for training and testing data, respectively. Furthermore, the findings showed that the most discharge coefficient occurred in the experiment by a_1/b_1 =4/15, where a1 and b1 denoted on gate height and gate length, respectively. Also, increasing trend in discharge coefficient was seen by increasing the Froude number and h/P .

The results show that (GEP) intelligence approach is an adequate model to predict assessment of discharge coefficient of curved compound weir-gate. Also, the results of traditional regression equations were improved using this method. In the other words, the results implied on the ability of the gene expression programming (GEP) to assess the discharge coefficient of this structure compared with regression method. Also, according to the results, the geometric parameters have the main effect on the discharge coefficient of curved compound weir-gate. So, use of the gene expression programming (GEP) is suggested in future studies related to this topic.

Climate change and its impact on the status of water resources can endanger the various aspects of life and human life on Earth. In this study, single and hybrid-wavelet artificial neural network, Adaptive neuro-fuzzy inference system and gene expression programming were used modeling flow parameter. For this purpose, monthly climatic data with 21-year (1996-2016) statistical period of flow, temperature and precipitation of Tapik station in the Nazluchay River of West Azerbaijan province has been used. In this study, the effects of delayed flow parameters, precipitation, temperature and periodic effect (monthly coefficient) in models have been investigated. The results show the superior performance of wavelet hybrid models compared to single models of soft computing and the positive effect of applying periodic effects on river flow modeling. Also, wavelet transformation by analyzing data and separating the noise enables the ability to upgrade the performance of hybrid models as compared to single models. For the optimal model (i.e. hybrid wavelet-gene expression programing model, the values of correlation coefficient and root mean square error indices were obtained as 0.98 (maximum) and 326.2 (m3/s) (minimum), respectively.

River water temperature has both economic and ecological significance when considering issues such as water quality and biotic conditions in rivers. This parameter affects directly other water quality parameters and plays a major role in the quality of aquatic life and habitats. Consequently, with wide application of water temperature for conducting environmental impact assessments as well as for effective fisheries management, it is important to understand the thermal behavior of rivers and related heat exchange processes. Numerous deterministic and statistical models have been used for prediction of river water temperature by researchers. These modeling were generally based on the air temperature, yet. However, the river hydraulics and metrological parameters may have their special effects on river water temperature. Furthermore, there are limited researches undertaken by novel and intelligent algorithms. Hence, in this study, gene expression programming has been used for estimation of the water temperature of the MohammadAbad river located in Golestan province. In addition to the air temperature, the river hydraulics and metrological parameters were also accounted for modeling river temperature.

Gene Expression Programming (GEP) is an evolutionary algorithm that uses a population of individuals and selects individuals according to fitness, and introduces genetic variation using one or more genetic operators. For the water temperature modeling, the river hydraulics and meteorological parameters including river flow discharge, flow velocity, air temperature, humidity, wind speed and cloud cover during 7-year statistical period (2006-2012) were considered as input parameters and river water temperature was selected as output parameter.

Based on the comparison of the results of different GEP models with 1 to 6 input variables, it was concluded that the GEP model with 6-parameters has the highest accuracy in terms of the coefficient of determination and the root-mean-square error. These values were obtained 0.92 and 1.8˚C for training data and 0.90 and 2.3˚C for the testing data. The mean absolute errors of this model were obtained as 14.67% and 12.80% for training and testing phases, respectively, while the error of linear regression model was obtained greater than 38%. Results showed that in comparison with the multiple-linear regression model, the GEP model has better performance for river water temperature estimation.

According to the results obtained in this paper, one can use the GEP model for prediction of river water temperature with acceptable accuracy. It is concluded that in addition to the air temperature which has the highest impact on the river temperature, the river flow discharge also has considerable impact.

Knowledge of the exact amount of daily suspended sediment loads can be used to identify the erosion and sedimentation status of the watersheds. In this research, intelligent artificial neural network and Gene Expression Programming was models used to estimate the daily suspended sediment load. Also, due to the importance of the watershed response to the input variables of the models, in addition to the flow discharge variable, the dynamic precipitation variable was also selected for entering the models due to its influential role in creating erosion and sediment production. The results of this study showed that all the models that used the precipitation variable along with the flow discharge had higher NSE and R2 statistic and lower RMSE and MAE statistics compared to the models that only the flow discharge variable to estimate the suspended sediment load. Also, GEP model with input variable combinations including instantaneous flow, average, average daily flow discharge with a delay time of three days, average daily precipitation and average daily precipitation with a delay time of three days, the most efficient model for estimating the daily suspended sediment load with the highest amount of statistics NSE was 0.90 and R2 was 0.92 and the lowest value of RMSE was 2282.42 (ton/day) and MAE was 750.38 (ton/day) compared to artificial neural network models. In general, the results of this research showed that the flow discharge variable, alone could not properly explain the variance of river sediment. Using precipitation variable as input variable to intelligent models played a significant role in increasing the precision of estimation of suspended sediment load and using precipitation variables In addition to the flow discharge variable, during the modeling process, the efficiency of the models was increased.