Background and Aim: The aim is first to express the differences and identify three models, namely, Gene Expression Programming (GEP), Neural-Fuzzy Network (ANFIS), and Bayesian Network (BN), and compare them with each other. Furthermore, the research's central question is whether the superior simulation meta-modal in this study can be a suitable alternative to conceptual models in the conditions of lack of data and information. Methods: The data used for this study are the daily rainfall and flow data of the Gamasiab Nahavand RIVER in 10 years from 2002 to 2012. For the prediction or simulation stage, the data of the blue year 2012-2011 have been used. Results: In the training phase and according to the coefficient of explanation and the square root of the mean squares error and the AIC criterion, it is observed that in all three models, both in the training phase and in the test phase, we see a minimal difference in the amount of these parameters. Moreover, all three models' results are close to each other with almost a minimal difference, and almost the relative superiority of the GEP model can be seen. Discussion & Conclusion: The results indicate that the simulator meta-model of gene expression has an excellent ability to simulate and predict the RIVER's daily flow, this simulation meta-model can be a suitable alternative to models in the absence of data and information. Be conceptual. Also, the speed of implementation of the gene expression programming model was faster than other models and was able to provide results in a short time.

Flood is natural phenomena. That human social accept it, is inevitable. But the reason of occurrence, size, and repetition is multifactor that change condition region of climatology, natural and geographical. Obviously the reason of flood is precipitation. But studies show there is not liner relation with this factor. As such disruptive this relation in additional to geographical condition has physical properties of basin and is properties of hydrologic. The case study of this research is located northeast of Tehran. It located in the east longitude from 51o 22’ to 51o 52’ and north latitude from 35o 45to36, 50. In this research for MODELING of flood flow of Jajrod RIVER basin first effective indexes in flooding with use of experience past study and special traits of basin recognized that superlative indicator effective in flooding region has been characterized and proportion of each sub basin was characterized. Ultimately, with the use of multiple regression method (enter method) the flooding model of basin was produced. Required layer for evolution with use of GIS software was produced. Conclusion of research shows in physiographic properties of studied, invoices of ratio of figure, ratio of density, time concentration, basin surrounding, sum of length of RIVER, length of first class RIVER MODELING of flooding has maximum importance.

Dam removal is one of the methods of restoring the RIVER ecosystem. The decision to remove a dam is based on the multidisciplinary indicators and the type of dam body and its location, as well as the downstream conditions and the type of dam removal method. In the present study, the removal of the Voshmgir Dam on the Gorganrud RIVER, Iran, was considered, because of the high volume of sediment deposition in the reservoir and the loss of its useful life. Three scenarios were selected for the removal of the dam: 1- complete removal; 2- stepped removal of the spillway; and 3- removal with stable sediments. In the first step, numerical MODELING for RIVER morphological changes was performed using the HEC-RAS model for unsteady flows with sediment transport. The erosion and sedimentation processes were simulated in six reaches (from the Voshmgir Dam to the Caspian Sea), with total length of 128 km. The results indicated that the second scenario (stepped removal of the spillway) is the best alternative due to the gradual processes of RIVER-bed changes. In the present research, the two-dimensional hydrodynamic and water quality model CE-QUAL-W2 is used for qualitative MODELING of Gorganrud RIVER in three scenarios of dam removal, within 128 km downstream of Vashmgir dam to the Caspian Sea Estuary. Qualitative MODELING was performed for the existing conditions of Gorganrud (without removing the Vashmgir dam) at two hydrometric stations of Aqqala (68 km downstream of the Vashmgir dam) and and Basirabad (112 km downstream of Vashmgir Dam), where a set of water level and water-quality data was available for model calibration. Model results are presented in the period of rapid RIVER changes after dam removal up to 60 days. Based on the results of qualitative MODELING, the amount of dissolved oxygen (DO) has increased in three scenarios of dam removal in two sections of the RIVER (Aqqala and Basirabad). The amount of biochemical oxygen demand (BOD) in the first scenario (complete removal) and the second (stepping removal) has increased slightly (less than 10% of the RIVER conditions before removal). In the third scenario (removal with stable deposition), no significant change in BOD occurred. In three scenarios and in both study periods, the pH changed very little in the two periods, before and after the removal of the dam. The total amount of soluble solids (TDS) in the early days of the simulation is equal to or greater than that before removal. The TDS rate is to be decreased by time. The overall results indicated that the second scenario of the dam removal (i.e. stepped removal of the spillway) is the best alternative due to the less impacts on the RIVER-bed changes, and the gradual processes of RIVER-ecosystem rehabilitation.

Having predicted RIVER flow, we can predict and control natural disasters such as flood and drought in addition to managing utilization of water resources. New models in this domain can help correct management and planning. In this study, three models are evaluated: Gene Expression Planning (GEP), Bayesian Network (BN), and Support Vector Machine (SVM). The data used for this research is precipitation data and daily flow of Gamasiab RIVER in Nahavand during 10 years period (1381-1391). Results indicated that the relative superiority of the gene expression planning model to other models and better performance of SVM model in comparison with BN in daily RIVER flow MODELING. In addition, implementing gene expression planning model was faster than other models and could provide results in a short time. The SVM model is also more fitted to estimate the final minimum values. Finally, GEP model with coefficient of determination of 0. 9230 and root mean square of 0. 5867 in the training phase and coefficient of determination of 0. 9025 and root mean square of 0. 4936 in the test phase was selected as the superior model.

The objective of this research is to investigate the accuracy of Qual2kw software in simulating the quality parameters of Sefidroud RIVER at the bottom of the dam. For this purpose, sampling and conducting laboratory studies for 12 stations in the range of 110 km in two summer seasons (August) and autumn (November) in 1398 has been done. In this study, Qual2kw software to simulate the eight main parameters of water quality including water temperature, pH, electrical conductivity, total suspended solids, soluble oxygen, biochemical oxygen demand, total nitrogen and total phosphorus, for August and November 1398, respectively, measured and verified Is. The results showed that the highest and lowest accuracy of the model with average NRMSE values in two stages is equal to 3. 3 and 47. 5% for pH and total nitrogen parameters, respectively. The highest correlation values between the measured and simulated values for the parameters of total solids and electrical conductivity were 0. 97 and 0. 96, respectively, in the model calibration stage. The results of the present study indicate the appropriate capability of Qual2kw model to model water quality parameters of Sefidroud RIVER. The results of the present article indicate the appropriate accuracy of the Qual2kw model in order to simulate water quality parameters in the Sefidrood RIVER, which is in accordance with the results of Aryan, Nejad et al. (2015). It should be noted that the hydraulic properties of the flow affect the amount of dissolved oxygen in the Sefidrood RIVER more than changes in the quality of other parameters.

In this research, in order to study and evaluate the accuracy of single-variable time series models, multivariables time series and hybrid models in MODELING the RIVER flow quality values in the Peygale hydrometric station located in the west of Lake Urmia from data The qualitative flow of the RIVER and the flow RIVER has been observed on an annual and monthly scale and during the statistical period of 1975-2016. The qualitative parameters studied in this study are EC, TDS and SAR values. In this study, EC and TDS values were modeled and studied. The ARMA model (1. 0) was selected for EC and TDS values of the hydrometric station used as the superior model. The results showed that the accuracy of single-variable linear time series models in MODELING the EC and TDS parameters of the studied station was not satisfactory, but acceptable. Similar results were obtained for monthly single-variable models. After studying the time series single-valued model, multi-variable time series models were investigated on the monthly and annual scale. In this model, the EC, TDS, SAR and flow data of the RIVER were considered at the Peylgale hydrometric station as inputs of the model. The results of the error analysis of CARMA and MPAR models for EC values showed that, on average, the error values were 25 and 21 percent lower than the ARMA and PARMA models, which resulted in an improvement in the error values for the data TDS is 34% and 33% respectively. Overall, the results showed that in all stations studied, EC and TDS values are estimated to be better than single-variable models of ArmA family on annual and monthly basis using multiple time series models. Let's say Finally, applying the time series hybrid models, the error rate resulting from MODELING of EC values in the monthly and annual scale was 46% and 10%, respectively, and for the TDS values it was 40% and 12%, respectively.

The purpose of this study was to estimate the amount of sediment of Vanai RIVER in Borujerd. In this research, the characteristics of the sub-basins of this RIVER have been extracted first. These specifications include the physical characteristics of the sub-basins, including the area, the environment and length of the waterways, and the characteristics of the RIVER flow, and its sediment content. In the following, multivariate linear regression, multilevel prefabricated neural network (MLP) and radial function-based neural network (RBF) models are used to model sediment estimation. After estimating the model, the mean square error index (RMSE) was used to compare the models and select the best model. Evidence has shown that initially the MLPchr('39')s neural network model had the best estimate with the lowest error rate (90. 44) and then the RBF model (151. 44) among the three models. The linear regression model has the highest error rate because only linear relationships between variables are considered.

Background and Aim: In patients with complete denture, some clinicians have used modelling plastic impression compound (MPIC) along tissue conditioner (TC) materials simultaneously. Little information is available on the composition of these materials and the interaction between them. The purpose of this study was to evaluate the influence of two components of MPIC on the structure and chemical composition of TC.Materials and Methods: In this experimental study, MPIC specimens were provided in 25×2 mm discs. Specimens were randomly divided into three groups and were immersed in ethanol 70%, plasticizer (dibutyl phthalate) and a mixture of them (ethanol 70% and dibutyl phthalate). All of the discs were weighed with a digital balance before and 2, 4, 6 and 24 hours after immersion. Values were analyzed by non parametric Kruskal-Wallis (a= 0.05) and SPSS 16 for Windows (SPSS Inc., Chicago, IL) was used for statistical analysis.Results: Statistical analysis indicated significant differences among all groups (p>.05).Conclusion: Dibutyl phthalate (DBP) had high impact on the solubility of MP, while the mixture of dibutyl phthalate (DBP) and ethanol demonstrated the highest impact.

The aim of this study was to develop a model for estimation of major THMs produced as a result of RIVER water disinfection. Experiments were designed for this purpose and different ratios of Cl2 to DOC were created in water samples to which were then added different concentrations of bromide ions. After the incubation period, analyzers were used to measure natural organic compounds, TOC, and DOC in the samples and the data thus obtained were fed into the SPSS software. Moreover, multivariate regression analysis was performed to extract the relevant statistical model. Four different formulas were obtained for predicting the rate of each of the THMs produced. Reaction time and bromide ion concentration were identified as the main factors involved in the production of THMs in the water supply investigated. In addition, the carcinogenicity of THMs in water samples reached its maximum with bromide concentrations between 207 and 246mg/l. The formulas obtained in this study can be used as a simple and accurate tool for the initial estimation of THMs production in different water supplies.