فصلنامه مهندسی منابع آب
پیاپی 21 (تابستان 1393)

The current and sediment transport patterns are important factors affecting coastline morphology. Subsequently، these factors are influenced by waves and currents. Currents are generated by waves breaking on the coastal area. A simultaneous presence of a long shore and tidal currents makes the inlet. A complex hydrodynamic area. The effects of tidal currents on sediment transport pattern and sedimentary features of inlets were studied by numerical modeling. The junction of a small bay to an open sea was modeled using the MIKE21 software. Different conditions of tide amplitudes (0. 5، 1. 5، 2. 5 meters) were investigated. The results clearly indicated the formation of two vortexes during transformation from flow to ebb tide and vice versa. These vortexes، in association with the tidal flow and ebb currents، produce some shallow areas at the sides of the inlet. The radius of the tidal vortexes، and the width of sedimentation area، are directly dependent on the tide amplitude. Using an analysis of Keulegan، when tide amplitudes were 1. 5 and 2. 5 meters، there was flow dominance in sediment transport capability (i. e. net bay-ward transport). when the tide amplitude was 0. 5 meter، there was the ebb dominance of sediment transport capability.

Sudden expansion can be used as energy dissipater in the pressurized pipe flows. As the induced cavitation bubbles will cause large structural damage، estimating the amount of maximum developed under-atmospheric pressure (MDUAP) in sudden expansions is of particular interest. Pressure fluctuations، domain and intensity of cavitation depend on the amount of MDUAP. Therefore، estimating the amount of MDUAP is a vital task in the design stage. To carry out this study، various sudden expansions were developed by changing the geometry and flow properties. The developed sudden expansions were modeled using the CFD modeling technique. The obtained CFD based results were successful in analyzing the experimental observations. Therefore، the simulation model is capable of predicting the MDUAP under different conditions. In this paper، the M5P data mining model is used for the first time for establishing a fast، accurate، and cost-effective linkage between the geometry and flow properties of a sudden expansion and the amount of MDUAP. The results show that the developed M5P based simple rules can accurately predict the amount of MDUAP.

The relationship between soil moisture and soil suction is called soil moisture characteristic curve، and its measurement is time-consuming and expensive. One of the estimation methods of soil moisture characteristic curve is using the soil particle size distribution curve and bulk density، which contains a scaling parameter (). In this study، 9 soil samples with silty clay and silty clay loam textures were selected from the Marvdasht Region in the Province of Fars، and soil moisture characteristic curve of each soil was determined. Then، the soil particle size distribution curve of each soil was estimated based on the Fooladmand and Sepaskhah model (FS) and the Fooladmand and Mansuri model (FM). Also، the local void ratio was considered for different segments of soil particle size distribution curve. The linear procedure with local void ratio (LN) and Logistic procedure with local void ratio were used for determining the scaling parameter. Therefore، the soil moisture characteristic curve of each soil was estimated with four different methods، and all of them were compared with the determined soil moisture characteristic curve. The results showed that using the FM model for estimating the soil particle size distribution curve to estimate the soil moisture characteristic curve was better than that of the FS model. In general، the results indicated that the procedure of FM-LN was appropriate for estimating the soil moisture characteristic curve of the studied soils in this study.

Hydrological drought (HD) is the most devastating natural phenomena that are falls the inhabitants of dry lands as its dive consequences may not be compensated. Therefore، its prediction has been puzzling researchers from time immemorial. As this phenomenon is directly related to the meteorological drought (MD) we decided to establish a relationship between the two for the Sufi-Chai Catchment، the Province of East Azarbaijan، benefitting from 40 years (1950-1989) of monthly precipitation and stream flow data. Using the normal monthly regime method، the drought periods data relating to each series of precipitation and stream flow were extracted and their relationship were established using the GP and ANFIS artificial intelligence techniques in order to predict the occurrence of drought. The accuracy and precision of these 2 techniques were evaluated using the correlation coefficient (r2)، root mean square error (RMSE) and mean absolute deviation (MAD). The very high r (0. 99) indicated that both techniques correctly predicted the temporal occurrence of HD from MD. The results indicated that ANFIS with the RMSE=4. 98 and MAD of 3. 83 was superior to GP with the RMSE=6. 437 and MAD of 5. 021.

In the recent year، because the increasing growth of water including demands، using limited surface and groundwater resources and، in some regions، conjunctive use have increased. Contemporary with this growth، modeling include simulation and optimization has been developed especially in how to deal with the optimization of pumping groundwater. Operational rules are appropriate tools in water resources planning that estimate the value use from various sources at the previous and present time. In this research، real time operation of different resources and water allocation to the different demands in the Karaj region has been considered. To materialize this objective، genetic algorithm (GA) performance has been compared with the fixed lenght gene genetic programming (FLGGP) to extract mathematical equations without any predefined linear and nonlinear forms considering objective of minimization of the sum of the squared deviation between demand and allocated water. Results indicated that the achieved objective employing the FLGGP is 32. 58 percent smaller (better) than the same value obtained by the GA. Thus، the FLGGP performance and flexibility due to the use of functions and variables in the search space is better than The GA in conjunctive system.

As the SAR of irrigation water is a major determinant of the sustainability of agriculture and development on groundwater، the foreknowledge of its value is of utmost importance. As this parameter is highly related to the geological settings، and this in a region maybe somehow dependent on geographical coordinates، the longitude and latitude of 69 wells، along with the pH، electrical conductivity (EC) and total dissolved solids (TDS) of their water were determined. To correlate these data to the SAR of water، this parameter was assigned as the dependent variable and the other 5 as the independent variables. The linear multiple regression (LMR) and artificial neural network (ANN) methods were applied to establish their correlations، and the sensitivity analysis was performed for the ANN to single out the most important independent variable. It was observed that the LMR and ANN explain 23. 9 % and 80. 0 % of the variations of the SAR، respectively. The sensivity analysis indicated that the water pH was the strong predictor of the groundwater’s SAR value.

The use of stepped spillways has been increasingly on the rise due to their low cost and high efficiency. Hence، growing attention has been paid to the studies in this field by dam engineers; however، most of these studies have focused on the stepped spillways with uniform step height at the cost of ignoring non-uniform step heights، which also demand particular attention. In this study، a physical model of Herat Dam for a 1. 3-m high stepped spillway with a slope of 19. 2 ° was used to verify the numerical model. Then، three types of stepped configurations were tested using computational fluid dynamics in the ANSYS CFX software for the flow range of. Further، a two-phase flow simulation was carried out for each configuration and all discharge ranges. The results were compared in terms of flow pattern، energy dissipation and aeration point. The results showed minor difference in terms of energy dissipation for nappe regime flow and marked difference in terms of skimming regime flow.

Flood routing is one of the most important issues in river engineering because of prediction of the ascent and descent of flood hydrograph. As flood is a variable and unsteady flow، its routing requires accurate and detailed data collection at hydrometry stations. The Muskingum models very offer a useful procedure among the flood routing methods. Moreover، application of the artificial intelligence methods have grown substantially in the different water engineering and watershed modeling endeavors in recent decades. In the present study، the data collected by Wilson، Wu et al.، and veiss man Jr. and Lewis for three different rivers in the U. S. A were used for the flood routing processes using the Muskingum، artificial neural network، adaptive neuro-fuzzy inference system، and genetic programming. Simulation results of flood routing process using the mentioned methods were compared using the statistical indicators of R2، RMSE and MBE. The results indicated that the artificial intelligence methods were superior to the Muskingum method due to their lower RMSE. The RMSE value for the artificial intelligence techniques was 0. 00174 and for the Muskingum method it was 28. 727. The Muskingum method was not successful in flood hydrograph simulation with multi peaks. Despite the slight differences in accuracy estimation and error values ​​in the models، the artificial neural networks proved their superiority with the highest R2، and lowest RMSE and MBE. The adaptive neuro-fuzzy inference system and genetics programming were placed in next levels. Based on the ease of use and more accurate results، the use of artificial intelligence methods is recommended for further studies in this region.