Comparison of stochastic models and conceptual models in hydrological drought forecast (case study: Karkheh River Basin) | ||
| نشریه علمی - پژوهشی مرتع و آبخیزداری | ||
| Article 2, Volume 66, Issue 4, March 2014, Pages 493-508 PDF (1.89 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.22059/jrwm.2014.50026 | ||
| Authors | ||
| Ommolbanin Bazrafshan1; Ali Salajegheh* 2; Ahmad Fatehi3; Abolghasem Mahdavi4; Javad Bazrafshan5; Somayeh Hejabi6 | ||
| 1Assistant Professor. Agricultural and Natural Resources Faculty, University of Hormozgan, Iran. | ||
| 2Associate Professor, College of Agriculture & Natural Resources, University of Tehran, Iran. | ||
| 3Assistant Professor, Center of Water Shortage and Drought Research in Agriculture and Natural Resources, Iran. | ||
| 4Professor, College of Agriculture & Natural Resources, University of Tehran, Iran. | ||
| 5Assistant Professor, College of Agriculture & Natural Resources, University of Tehran, Iran. | ||
| 6PhD. 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 | ||
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