@article { author = {Moeeni, H and Bonakdari, H and Fatemi, ُSE and Ebtehaj, I}, title = {Modeling the Monthly Inflow to Jamishan Dam Reservoir Using Autoregressive Integrated Moving Average and Adaptive Neuro- Fuzzy Inference System Models}, journal = {Water and Soil Science}, volume = {26}, number = {شماره1 بخش 2}, pages = {273-285}, year = {2016}, publisher = {University of Tabriz}, issn = {2008-5133}, eissn = {2717-3755}, doi = {}, abstract = {Hydrological time series modeling is one of the most important issues in water resourcemanagement. In this paper monthly inflow to Jamishan dam reservoir in Kermanshah province(west of Iran) is modeled by AutoRegressive Integrated Moving Average (ARIMA) and AdaptiveNeuro-Fuzzy Inference System (ANFIS) models. These models are based on stochastic andArtificial Intelligence (AI) methods, respectively. For modeling up to five parameters in theARIMA model were used and produced 1296 models which were fitted on the time series. InANFIS model 14 input combinations were defined using the discharges with different lags. Twostates of Grid Partitioning (GP) and Subtractive Clustering (SC) were used in Fuzzy InterfaceSystem (FIS) generation. Also, in training network Back Propagation (BP) and hybrid algorithmswere used. Monthly modeled discharges were compared in the ARIMA and ANFIS models bysome indexes such as Mean Absolute Relative Error (MARE) index which was obtained 0.398 and0.8 for each model, respectively. The result showed that the ARIMA model was much moreaccurate than ANFIS model in modeling low discharges and also in short and long times modeling.}, keywords = {ANFIS,ARIMA,Inflow,Modeling,Stochastic}, title_fa = {مدل سازی دبی ماهانه ورودی به مخزن سد جامیشان با مدلهای خودهمبسته با میانگین متحرک تجمعی و سامانه استنتاج فازی -عصبی انطباقی}, abstract_fa = {}, keywords_fa = {استوکستیک,دبی,مدلسازی}, url = {https://water-soil.tabrizu.ac.ir/article_4894.html}, eprint = {https://water-soil.tabrizu.ac.ir/article_4894_b69b9b7e16bb2f3f01ed06ce6234c986.pdf} }