Browsing by Author "Demirel, Mehmet C."

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    Medium-range low flow forecasts in the lobith, River Rhine
    (Uludağ Üniversitesi, 2017-07-30) Bouwma, Pieter; Demirel, Mehmet C.
    The aim of this study is to predict low flows 14 days in advance using a data-driven model. First, we apply correlation analysis to select appropriate temporal scales of pre-selected inputs that are precipitation, potential evapotranspiration, discharge, groundwater, snow height and lake levels. The forecasted rainfall has also been used as model input to forecast low flows in the River Rhine at Lobith. The correlation analysis analysis between low flows and basin indicators show stronger correlations for the Alpine sub-basins than the rainfed sub-basins. The Middle and Lower Rhine are downstream channel areas and they do not contribute to the discharge. Therefore, they are excluded from the entire analysis. The low flow predictions for the Alpine sub-basins and the Mosel are reasonable during the validation period, whereas the ANN for Lobith shows low performance for a different test period. The results for the training and the validation period are more encouraging than the test period for Lobith, i.e. Nash-Sutcliffe (NS) efficiency of 0.75 and 0.73 respectively.
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    On the calibration of multigene genetic programming to simulate low flows in the Moselle River
    (Uludağ Üniversitesi, 2016-11-27) Mehr, Ali Danandeh; Demirel, Mehmet C.
    The aim of this paper is to calibrate a data-driven model to simulate Moselle River flows and compare the performance with three different hydrologic models from a previous study. For consistency a similar set up and error metric are used to evaluate the model results. Precipitation, potential evapotranspiration and streamflow from previous day have been used as inputs. Based on the calibration and validation results, the proposed multigene genetic programming model is the best performing model among four models. The timing and the magnitude of extreme low flow events could be captured even when we use root mean squared error as the objective function for model calibration. Although the model is developed and calibrated for Moselle River flows, the multigene genetic algorithm offers a great opportunity for hydrologic prediction and forecast problems in the river basins with scarce data issues.