مشخصات پژوهش

عنوان	A comparison between artificial neural network method and nonlinear regressionmethod to estimate the missing hydrometric data
نوع پژوهش	مقاله چاپ‌شده در مجلات علمی
کلیدواژه‌ها	artificial neural network, missing hydrometric data, nonlinear regression
چکیده	Missing values are a common problem faced in the analysis of hydrometric data. The need for complete hydrological data, especially hydrometric data for planning, development and designing hydraulic structures, has become increasingly important. Reasonably estimating these missing values is significant for the complete analysis and modeling of the hydrological cycle. The major objective of this paper is to estimate the missing annual maximum hydrometric data by using artificial neural networks (ANN). Sixteen stations, with 28 years of measurements, in the catchment area of the Sefidroud watershed in the north of Iran were selected for this investigation. Comparison between the results of ANN and the nonlinear regression method (NLR) illustrated the efficiency of artificial neural networks and their ability to rebuild the missing data. According to the coefficient of determination (R 2) and the root mean squared value of error (RMSE), it was concluded that ANN provides a better estimation of the missing data.
پژوهشگران	جمیل بهرامی (نفر اول)، محمدشاهرخ عبدی (نفر سوم)، عبدالرسول تلوری (نفر چهارم)، بهزاد روزخش (نفر ششم به بعد)، کریم عباسپور (نفر پنجم)، محمدرضا کاویانپور (نفر دوم)