Ommolbanin Bazrafshan; Ali Salajegheh; Ahmad Fatehi; Abolghasem Mahdavi; Javad Bazrafshan; Somayeh Hejabi
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), ...
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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.
Kazem Nosrati
Abstract
The term hydrological drought is applied to represent low water levels in streams, reservoirs and lakes as well as a low groundwater level. Base flow index (BFI) as one of low flow indices gives the ratio of base flow to total flow and investigates basin’s ability to store and release of water in drought ...
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The term hydrological drought is applied to represent low water levels in streams, reservoirs and lakes as well as a low groundwater level. Base flow index (BFI) as one of low flow indices gives the ratio of base flow to total flow and investigates basin’s ability to store and release of water in drought periods. The objectives of this study were to determine BFI and to validate this index in drought studies of Sefidrood Drainage Basin. To view of this, first three homogenous regions were identified based on the threshold level using cluster analysis. Then, daily BFI was calculated in 28 gauging stations of the homogenous regions. The results showed that the regional mean of BFI with value of 0.65 (SD=0.19) is stable during long-period data. BFI ranged between 0.17 and 0.86 and also based on the 25, 50 and 75 percentiles, river flow regime in the study area is divided into four categories that show more than 50 percent of the catchments in the study area have low or unstable regime. So it would be hard that the catchments able to provide river flow during drought periods. Therefore the results of this study can be used in assessment of groundwater recharge, water supply system, irrigation management, and hydrological drought monitoring as well as regional modeling of water resources storage and hydrological drought in ungauged areas