Uncertainty Analysis of Monthly Streamflow Forecasting

Abstract

Streamflow forecasting is an important factor in water resources planning and management. In this study Feed Forward Artificial Neural Network (FFANN) was used for monthly streamflow forecasting. Three scenarios were considered for modeling. Principal Component Analysis (PCA) is used for reducing the model architecture complexity and input data reduction. Twelve statistical criteria were used to evaluate the model performance. Also for quantifying the accuracy of forecast, uncertainty analysis was conducted using Monte Carlo simulation. Results indicated that the model in general is capable to forecast monthly streamflow time series satisfactorily. However the model is underestimated in extreme values. Also, uncertainty analysis shows that the model forecasted monthly streamflow time series properly in the first two scenarios while in the third scenario most of the forecasted values lie out of the upper confidence interval.