A prediction model capable of aggregation with recursive unbiased minimum variance estimation algorithms based on the Kalman filter technique has been formulated and applied for predicting monthly flows such that their summation is equal to annual flow in the same year. The model represents a discrete linear stochastic system where the states are defined as monthly flows in addition to the measurement equation with time invariant measurement matrix and annual flow measurement. Provided that observed or generated annual flows are available then the proposed model can be employed to predict monthly flows so that their aggregation yields the total annual flow.
Zekâi Şen; Predictive Aggregation Models in Hydrology. Hydrology Research 1 February 1983; 14 (1): 19–32. doi: https://doi.org/10.2166/nh.1983.0002
Download citation file:
Impact Factor 2.012
Cite Score 4.1 • Q1