Entropy-Based Parameter Estimation in Hydrology by Vijay P. Singh (auth.)

By Vijay P. Singh (auth.)

Since the pioneering paintings of Shannon within the past due 1940's at the improvement of the speculation of entropy and the landmark contributions of Jaynes a decade later resulting in the advance of the main of extreme entropy (POME), the concept that of entropy has been more and more utilized in a large spectrum of components, together with chemistry, electronics and communications engineering, facts acquisition and garage and retreival, facts tracking community layout, ecology, economics, environmental engineering, earth sciences, fluid mechanics, genetics, geology, geomorphology, geophysics, geotechnical engineering, hydraulics, hydrology, photograph processing, administration sciences, operations examine, trend popularity and id, photogrammetry, psychology, physics and quantum mechanics, reliability research, reservoir engineering, statistical mechanics, thermodynamics, topology, transportation engineering, turbulence modeling, etc. New components discovering program of entropy have seeing that endured to spread. The entropy thought is certainly flexible and its applicability frequent. within the region of hydrology and water assets, more than a few functions of entropy were said in the past 3 a long time or so. This ebook specializes in parameter estimation utilizing entropy for a couple of distributions often utilized in hydrology. within the entropy-based parameter estimation the distribution parameters are expressed by way of the given info, known as constraints. hence, the strategy lends itself to a actual interpretation of the parameters. as the details to be specific frequently constitutes enough records for the distribution into account, the entropy strategy offers a quantitative approach to exhibit the knowledge inside the distribution.

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Estimating resilience by canonical analysis. Water Resources Research, Vol. 18, No. I, pp. 51-57. C. , 1979. Probability-weighted moments: definition and relation to parameters of several distributions expressible in inverse form. Water Resources Research, Vol. 15, pp. 1049-1054. K. , 1983. Uniqueness and observability of conceptual rainfall-runoff model parameters: The percolation process examined. Water Resources Research, Vol. 1, pp. 29-276. K. , 1985. The rel;ationship between data and the precision of parameter estimates of hydrologic models.

Lmoments are analogous to ordinary moments and are expressed as linear combinations of order statistics. They can also be expressed by linear combinations of probability-weighted moments. Thus, the ordinary moments, the probability weighted moments and L-moments are related to each other. L-moments are known to have several important advantages over ordinary moments. L-moments have less bias than ordinary moments because they are always linear combinations of ranked observations. As an example, variance (second moment) and skewness (third moment) involve squaring and cubing of observations, respectively, which compel them to give greater weight to the observations far from the mean.

52, pp. 229-237. Singh, V. , 1988. Hydrologic Systems, Vol. 1: Rainfall-Runoff Modeling. Prentice hall, Englewood Cliffs, New Jersey. , 1972. Fitting of distribution functions by nonlinear least squares. Water Resources Research, Vol. 6, pp. 1423-1432. Sorooshian, S. , 1983. Automatic calibration of conceptual rainfall-runoff models: the question of parameter observability and uniqueness. Water Resources Research, Vol. 1, pp. 260-268. K. , 1983. Evaluation of maximum likelihood parameter estimation techniques for conceptual rainfall-runof models: Influence of calibration data variability and length on model credibility.

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