Background and Objective: Surveying of Balekhloo River’ s water is necessary because it is one of the main branches of Gharasoo River in Ardabil and supplying water for Yamchi dam Due to the presence of contaminant sources such as wastewater, residential areas, and industrial, agricultural and mineral water, river morphology. Today evaluation of water quality based on macro invertebrates variety has completed chemical methods of pollution recognizes. Method: In this study water quality examination of Balekhloo River in Ardebil, macro invertebrates were sampled in 5 stations along the river in 2 times of year (low water season and high water season). For results’ simulation measuring of some physico chemical parametres were perfomed. Macro invertebrates were identified and evaluated with Shannon winner variety index, Margalef biotic index and Pielou similarity index. Findings: In this study, ten orders of invertebrates in 23 categories were identified and counted and result simulation laboratory measured of physico chemical parameters like DO, Nitrate, PH, Debi and velocity. Discussion and Conclusion: Results showed that there are similarities between physical, chemical and biological indexes and Margalef biotic index was known more suitable bio assessment index to determine water quality in Balekhloo River. Finally, by comparison of physical, chemical and bio index parameters of Balekhloo River’ s water quality was classified into three region low, moderate and high pollution.

Introduction Erosion and deposition cause irreparable damage to water construction projects. Among these damages, we can mention the accumulation of sediment behind the dams and the reduction of their useful volume, the destruction of structures, the reduction of capacity and the increase in the maintenance cost of irrigation canals. On the other hand, sediment transport has an effect on water quality indicators in terms of drinking and agriculture. Accurate estimation of river suspended sediment load from various aspects of water resources engineering, environmental issues and water quality is of particular importance. In this regard, the hydrological models of the basin have not shown proper efficiency in estimating the amount of suspended sediment, due to the many influencing factors. Most of the simulation studies of suspended sediment estimation are based only on the flow rate of the basin outlet, and the obtained results are proof of their inefficiency. In the meantime, the development and use of new sediment estimation methods that are easy to use in addition to sufficient accuracy will play an important role. Nowadays, the fuzzy and neural intelligent conductor system has found many applications in various water engineering problems, including sedimentation, due to its ability to solve complex and nonlinear phenomena. Due to the great importance of sediment transport in the optimal use of water resources and the design of dams, it is very necessary to obtain an accurate method for estimating the amount of suspended sediment load in rivers.Materials and Methods The purpose of this research is to evaluate and compare the two methods of support vector machine models (SVM) and group data control method (GMDH) in estimating the suspended sediment load of the Pol Doab station of Qarachai River in Markazi Province and comparing it with the results of the measuring curve. is for this purpose, the daily data of discharge, sediment, temperature and rainfall parameters of Shazand Pol Doab station were used. For this work, 13 scenarios and different combinations of parameters were defined. Then the results of the two methods were compared with each other and with the results of the measuring curve. Finally, the best method was suggested. For this purpose, library and field studies and review of related sources, statistics and information were collected. The statistics of temperature, rainfall, daily average discharge of stream and sediment measured daily during a long-term statistical period of 40 years at Pol Doab Hydrometry Station were received from the Meteorology and Regional Water Department of Markazi Province. The received data were categorized and converted into the input format of the models. According to the discharge and corresponding sediment, the curve of the sediment gauge was drawn and its equation was obtained. Appropriate patterns of input variables were selected based on trial and error. 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). Then the model was taken for each input and output pattern. In the next step, the most appropriate time delay of the input parameters in the modeling, which had a higher R2 determination coefficient and a lower root mean square error (RMSE) was selected. In this research, 70% of research data was used as training and 30% for validation and testing. Finally, two data-mining methods were compared with each other and also with gauge curve and observational data.Results and Discussion The results obtained from this research indicated the acceptable performance of the methods used in predicting suspended sediment amounts. Comparing the results of GMDH, SVR and SRC models shows the superiority of GMDH and SVR models in predicting suspended sediment values compared to Verdi model number 6. The results showed the acceptable performance of the GMDH model with the highest R2 determination coefficient of 0.99 and the lowest root mean square error of 83 tons per day. According to the obtained results, it can be said that the GMDH model as a powerful and high-speed model can be used to model suspended sediment. The results of the research showed that both data-mining methods have far better efficiency and accuracy in estimating the suspended load of river sediment than the sediment gauge curve. Data mining-based methods can be used as an alternative to estimate the river's suspended load. It should be noted that due to climate change and droughts, industrial development, colonization of land use and changes in the morphology of watersheds, the obtained results cannot be used forever at any time, but should be used whenever the models need to be updated. Another weakness of the models is that with the increase in the number of developed layers, the accuracy of the produced answers increases, but the produced relationships between the input and output variables become very complicated. Conclusions The results of modeling with (SVM) showed that scenario number 6, which includes discharge at the current time and discharge and sediment with a time delay step, with the highest determination coefficient R2 with a value of 0.98 and the lowest root mean square error RMSE equal to 185 kg per day. It performs better in other scenarios. In the next step, the best model selection model (SVM) was used as the input of the GMDH model. The results were compared with the model (SVM) and sediment gauge curve. The results show the acceptable performance of the GMDH model with the highest R2 determination coefficient of 0.99 and the lowest root mean square error of 83 kg per day compared to the other two methods. The obtained results showed that both investigated data mining methods provide much better results than the sediment gauge curve. The coefficients and functions used to calibrate the intelligent models used in this research can be very useful for estimating the suspended sediments of nearby stations without statistics having the same geological and hydrological conditions at the regional level.

One of the structures for regulating the water level in the irrigation and drainage ducts is the lopac gates, which are proposed as a structure for regulating and controlling the flow level. In this study, a new design of this type of structure has been proposed in which the gates are placed next to each other in pairs, and they are called multiple lopac gates. The objective of this research is to investigate the effective hydraulic parameters of the proposed structure and compare it in a case where a gate is used under the same conditions. All the simulations were modeled with 3 amounts of opening 30, 45, and 60 degrees and at 3 flow rates of 20, 40, and 60 liters per second and using Flow3d software, in these simulations, the number of mesh cells is 1000000 and RNG turbulence model is used. The results showed that the maximum shear stress was reduced by an average of 38% compared to the single gate mode in most tests at different openings and flow rates using multiple lopac gates, and the largest amount of this reduction was related to the opening of 45 degrees, and the flow rate is 40 liters per second with a value of 76%. Also, the forces acting on the gate at different flow rates and openings will be reduced by 150% on average. In the qualitative investigation of flow vortices, the investigations also showed that vortex range, length, and strength are reduced compared to the single gate mode when two gates are used, and the number of vortices increases compared to when a single valve.