Prediction of Water Level Fluctuations of Chahnimeh Reservoirs in Zabol Using ANN, ANFIS and Cuckoo Optimization Algorithm - Jamshid Piri; Mohammad Reza Rezaei Kahkha (Iranian Journal of Health, Safety and Environment, 2017)
Forecasting changes in level of the reservoir are important in Construction, design and estimate the volume of reservoirs and also in managing of supplying water. In this study, we have used different models such as Artificial Neutral Network (ANN), Adaptive Neuro Fuzzy Inference System (ANFIS) and Cuckoo Optimization Algorithm (COA) for forecasting fluctuations in water level of Chahnimeh reservoirs in south-east of Iran. For this purpose, we applied three most important variables in water levels of the reservoir including evaporation, wind speed and daily temperature average to prepare the best entering variables for models. In addition, none accuracy of error in estimation of hydrologic variables and none assurance of exiting models are the result of their sensitivity to the educational complex for teaching of models and also preliminary decoration before beginning general education has been estimated. After comparing exiting and confidence interval of the ANN and ANFIS has been found that the result of ANFIS model is better described than other model because it was more accurate and does have lesser assurance.