Estimation of Drought by Streamflow Drought Index (SDI) and Artificial Neural Networks (ANNs) in Ankara-Nallihan Region
Turkish Journal of Agriculture: Food Science and Technology
In this study, it is aimed to predict drought in Nallihan region by using streamflow drought index and artificial neural network method which is a part of artificial intelligence approaches. The measured data of some meteorological stations (Nallihan, Beypazari, Mihaliccik, Catacik, Goynuk, Mudurnu, Seben and Eskisehir) in the Sakarya Basin and the Nallihan streamflow observation station between 1996 and 2015 were used to forecast 2015-2030 streamflow values. The correlation coefficient in the
... ducation and test stages of the ANN model was realized with a high consistency of 0.990 and 0.967, respectively. According to the mean absolute error method, the error performance values of ANN model are 0.19 for the training phase and 0.26 for the test phase. Cumulative streamflow series were created for the reference periods (k1, October-December; k2, October-March; k3, October-June; k4, October-September) and the streamflow drought index values were obtained using measured and predicted values. According to these values, mild droughts were more frequent between 1997-2015 and 2016-2030, but the number of moderate and severe droughts increased gradually. It is predicted that in the future, it may be seen in extreme arid periods in the region. Drought in the 6-month period between October and March is similar to the average of all periods for 1997-2015 and 2016-2030. The use of 6-month drought data for the streamflow drought index is expected to be useful in predicting future drought.