Entropy Theory for Streamflow Forecasting

Streamflow forecasting is used in river training and management, river restoration, reservoir operation, power generation, irrigation, and navigation. In hydrology, streamflow forecasting is often done using time series analysis. Although monthly streamflow time series are stochastic, they exhibit seasonal and periodic patterns. Therefore, streamflow forecasting entails modeling two main aspects: seasonality and correlation structure. Spectral analysis can be employed to characterize patterns of streamflow variation and identify the periodicity of streamflow. That is, it permits to extract significant information for understanding the streamflow process and prediction thereof. For forecasting streamflow, spectral analysis has, however, not yet been widely applied. Streamflow spectra can be determined using entropy theory. There are three ways to employ entropy theory: (1) Burg entropy, (2) configurational entropy, and (3) relative entropy. In either way, the methodology involves determination of spectral density, determination of parameters, and extension of autocorrelation function. This paper reviews the methods of spectral analysis using the entropy theory and tests them using streamflow data.