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Controlling the water level in irrigation channels is important for the efficient management of irrigation and water delivery. In this study, the proportional–integral–derivative (PID) controller was implemented in both the HEC-RAS boundary condition, as an internal model, and MATLAB, as an external model. In the latter, the Hydrologic Engineering Center's (HEC) River Analysis System (HEC-RAS) model was automated for irrigation canals by coding in the MATLAB script. To test the new models, E1R1 (first right bank branch of the first eastern canal in the Dez irrigation network, Khuzestan Province, Iran) irrigation canal data were prepared in HEC-RAS. A flow pattern was provided to simulate the canal water levels. The results showed efficient control of the water level for both models. The maximum and average water depth deviations from the target value were 13% and 4%, respectively, which fall in the good agreement range. The fewer these indicators, the better the performance is. The efficiency and adequacy were close to the ideal value and in the good agreement classes. The equity indicator was 0.013, which is very close to its ideal value of zero, showing efficient water distribution in the tested system. According to the literature for the equity indicator, a range of 0–0.10 is good, a range of 0.11–0.25 is fair, and a range of greater than 0.25 is poor. The results showed that simple and fast implementation is the main advantage of the internal model; however, it is not suitable for implementing complex controllers. Conversely, the external model can be implemented for complicated algorithms without any limitations. [ABSTRACT FROM AUTHOR]

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Archimedean turbines have recently been installed in watercourses to convert the hydraulic power of water to electricity. As a new technology, they are under development now. Its geometric parameters' optimization with heuristic optimizers hasn't been considered so far. These parameters include the ratio of the inner to the outer diameter (diameter ratio), the pitch to the outer diameter ratio (pitch ratio), the tilt angle, and the fill factor which is the ratio of water depth in the screw's bucket to the outer diameter. In this research, the efficiency prediction model of the Archimedean turbine was coded in MATLAB and validated with laboratory and field Archimedean turbine data. Then, particle swarm optimizer (PSO) and grey wolves optimizer (GWO) were developed and linked with the validated prediction model. The optimizers' convergence was investigated; then, the turbine parameters were optimized to obtain the maximum efficiency. The best value of the tilt angle was found in the range 20–22.5°, and the best value of the fill factor was 1. The maximum mechanical efficiency of about 90% was found in this research. The overall results of PSO were better than GWO. [ABSTRACT FROM AUTHOR]

Precise prediction of inflow has been an important challenge for researchers during last decades. Considering the inflow prediction complexity, many prediction models have been used. Recently, ANN and ANFIS have been widely used to find the relationship between inputs and outputs without considering the physical mechanism of the phenomena. In this study, using ANN and ANFIS and based on recorded discharge, temperature, and rainfall data of Latyan hydrometric station, five different prediction models were developed to predict the monthly inflow to Latyan dam reservoir using recorded discharge, temperature, and rainfall data of Latyan hydrometric station. The best network model and its parameters were distinguished using some performance indices including CORR (Correlation coefficient), RMSE (Root Mean Square Error), and MAPE (Mean Absolute Percentage Error). The CORR, RMSE, and MAPE indices were obtained as 0.808, 5.71 and 192.58 for the best model of ANN; and as 0.825, 5.39 and 158.18, for the best model of ANFIS. Comparing the ANN and ANFIS results showed the superiority of ANFIS to ANN in modeling and predicting the inflow. Considering the results ANFIS could be used as a reliable approach for inflow prediction in the cases with high complexity and low possibility for measuring data directly. [ABSTRACT FROM AUTHOR]

Copyright of Irrigation & Drainage is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)