Comparison of stochastic models and conceptual models in hydrological drought forecast (case study: Karkheh River Basin)

Drought is random and nonlinear phenomenon and using linear stochastic models، nonlinear artificial neural network and hybrid models is advantaged for drought forecasting. This paper presents the performances of autoregressive integrated moving average (ARIMA)، Direct multi-step neural network (DMSNN)، Recursive multi-step neural network (RMSNN)، Hybrid stochastic neural network of directive approach (HSNNDM) and Hybrid stochastic neural network of recursive approach (HSNNRM) with time scale monthly and seasonally for hydrology drought forecasting and SDI selected as predictor in the Karkheh river basin. The results shown performances of HNNDA was found to forecast hydrological drought with greater accuracy for SDI forecasting، so performances model in monthly scale was greater accuracy to seasonality scale.