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Uncertainty Analysis of Monthly Stream flow Forecasting

Majid Dehghan1 * , Bahram Saghafian1 , Firoozeh Rivaz1 and Ahmad Khodadadi3

1 Technical and Engineering Department, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Department of Mathematics, Shahid Beheshti University, Tehran, Iran

DOI: http://dx.doi.org/10.12944/CWE.9.3.40

Stream flow 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 stream flow 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.

Streaflow; PCA, ANN; Uncertainty

Copy the following to cite this article:

Dehghan M, Saghafian B, Rivaz F, Khodadadi A. Uncertainty Analysis of Monthly Stream flow Forecasting. Curr World Environ 2014;9 (3) DOI:http://dx.doi.org/10.12944/CWE.9.3.40

Copy the following to cite this URL:

Dehghan M, Saghafian B, Rivaz F, Khodadadi A. Uncertainty Analysis of Monthly Stream flow Forecasting. Curr World Environ 2014;9(3). Available from: http://www.cwejournal.org/?p=7385

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