Document Type : Research Paper

Authors

• Ommolbanin Bazrafshan ¹
• Ali Salajegheh ²
• Ahmad Fatehi ³
• Abolghasem Mahdavi ⁴
• Javad Bazrafshan ⁵
• Somayeh Hejabi ⁶

¹ Assistant Professor. Agricultural and Natural Resources Faculty, University of Hormozgan, Iran.

² Associate Professor, College of Agriculture & Natural Resources, University of Tehran, Iran.

³ Assistant Professor, Center of Water Shortage and Drought Research in Agriculture and Natural Resources, Iran.

⁴ Professor, College of Agriculture & Natural Resources, University of Tehran, Iran.

⁵ Assistant Professor, College of Agriculture & Natural Resources, University of Tehran, Iran.

⁶ PhD. Student, College of Agriculture & Natural Resources, University of Tehran, Iran.

Abstract

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.

Keywords

• Artificial Neural Networks
• Hybrid models
• forecasting
• Hydrological drought
• stochastic models