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Masoud Karbasi

مهران رستملو، حسن اوجاقلو و مسعود کرباسی
ارزیابی عملکرد سامانه استنتاج فازی- عصبی به منظور تخمین ضریب یکنواختی پخش آب در سامانه های آبیاری بارانی کلاسیک
Evaluation performance of Adaptive Neuro-Fuzzy Inference System (ANFIS) to estimate water distribution uniformity coefficient in classic sprinkle irrigation systems

One of the most important performance evaluation criteria in the design of pressurized irrigation systems and especially classic irrigation systems is water distribution uniformity index. Field measurements of water distribution uniformity index in different climatic conditions, hydraulic and projects executive specifications requires spending time and too much costs and so use of indirect methods such as intelligent models can be useful. In this research, performance of Adaptive Neuro- Fuzzy Inference system to estimate water distribution uniformity coefficient in solid classic sprinkle irrigation systems in different conditions in terms of wind speed, sprinklers arrangements, volumetric flow rate and types of sprinklers was evaluated. For this purpose, a solid classic sprinkle irrigation system with considering of different arrangements of pipes and sprinklers were designed and performed. Then 54 field experiment to evaluate the performance of a solid classic sprinkle irrigation system were performed. Least amount of mean absolute error for neuro- fuzzy inference method was obtained as 6.2 % and the highest amount of correlation coefficient as 0.77. Sensitivity of model showed, the temperature and wind speed had the lowest and the most effect on water distribution uniformity coefficient changes, respectively. Estimated amounts of checking the water distribution uniformity coefficient showed that intelligent model as well as factors effect such as wind speed and sprinklers distances have been able to simulate the reducing amount of water distribution uniformity.



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