Uncertainty analysis of streamflow drought forecast using artificial neural networks and Monte-Carlo simulation



In this research, two scenarios of drought forecast were studied. In the first scenario, the time series of monthly streamflow were converted into the Standardized Hydrological Drought Index (SHDI), a similar index to the well-known Standardized Precipitation Index (SPI). Multi-layer feed-forward artificial neural network (FFANN) was trained with the SHDI time series to forecast the hydrological drought of Karoon River in southwestern Iran. In the second scenario, the time series of monthly streamflow discharge was forecasted directly and then converted to the SHDI. Principal component analysis (PCA) and forward selection (FS) techniques were applied to remove dependency of inputs and reduce the number of input variables, respectively. Moreover, uncertainty of SHDI and monthly streamflow discharge forecasts were investigated using a Monte-Carlo simulation approach. Findings indicated that the results of the first scenario were considerably better than the second scenario and that the SHDI adequately forecasted hydrological drought. The Monte-Carlo simulations demonstrated that all of forecasted values lie within the 95% confidence intervals.