چکیده مقاله
Time series modeling for either data generation or forecasting of hydrologic variables is an important step in the planning and operational analysis of water resources The capability of Artificial Neural Networks ANN in modeling of daily reservoir inflow forecasting was examined in a small tropical catchment Cross validation and pre processing of data was considered as alternatives in modeling process The model inputs were extracted using auto , cross , and partial auto correlation functions The results showed that the feed forward back propagation neural networks are able to forecast extremely changeable daily reservoir inflows Cross validation of data improved the model performance indices Transforming the data to normal distribution prior to training confirmed increasing significantly the model persistency and generalization in simulating an independence data set
کلیدواژهها
نویسندگان
Shahram Karimi-Googhari
Assistant Professor, Department of Water Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
شیوه ارجاع
Karimi-Googhari, Shahram,1388,Data Pre-processing Concern in Hydrological Time Series Modeling Using Artificial Neural Networks,8th International Congress on Civil Engineering,Shiraz
ارائهشده در
مجموعه مقالات هشتمین کنگره بین المللی مهندسی عمران21 اردیبهشت 1388 · شیراز