Entropy Theory and its Application in Environmental and by Vijay P. Singh

By Vijay P. Singh

Entropy conception and its software in Environmental and Water Engineering responds to the necessity for a publication that bargains with uncomplicated suggestions of entropy idea from a hydrologic and water engineering point of view after which for a publication that bargains with functions of those ideas to a number of water engineering difficulties. the diversity of functions of entropy is continually increasing and new components discovering a use for the speculation are continuously rising. The purposes of recommendations and methods differ throughout assorted topic parts and this booklet goals to narrate them on to useful difficulties of environmental and water engineering.

The ebook provides and explains the main of utmost Entropy (POME) and the primary of minimal go Entropy (POMCE) and their functions to types of likelihood distributions. Spatial and inverse spatial entropy are very important for city making plans and are provided with readability. greatest entropy spectral research and minimal pass entropy spectral research are strong suggestions for addressing quite a few difficulties confronted via environmental and water scientists and engineers and are defined the following with illustrative examples.

Giving an intensive advent to using entropy to degree the unpredictability in environmental and water platforms this booklet will upload a necessary statistical option to the toolkit of postgraduates, researchers and educational hydrologists, water source managers, environmental scientists and engineers.  it is going to additionally provide a useful source for pros within the similar components, governmental firms, inner most businesses in addition to scholars in earth sciences, civil and agricultural engineering, and agricultural and rangeland sciences.

This book:

  • Provides an intensive advent to entropy for rookies and more matured users
  • Uses various examples to demonstrate the functions of the theoretical principles
  • Allows the reader to use entropy thought to the answer of functional problems
  • Assumes minimum current mathematical knowledge
  • Discusses the speculation and its quite a few facets in either univariate and bivariate cases
  • Covers newly increasing parts together with neural networks from an entropy point of view and destiny developments.

Chapter 1 advent (pages 1–32):
Chapter 2 Entropy thought (pages 33–141):
Chapter three precept of extreme Entropy (pages 142–171):
Chapter four Derivation of Pome?Based Distributions (pages 172–212):
Chapter five Multivariate likelihood Distributions (pages 213–269):
Chapter 6 precept of minimal Cross?Entropy (pages 270–289):
Chapter 7 Derivation of POME?Based Distributions (pages 290–309):
Chapter eight Parameter Estimation (pages 310–334):
Chapter nine Spatial Entropy (pages 335–397):
Chapter 10 Inverse Spatial Entropy (pages 398–435):
Chapter eleven Entropy Spectral Analyses (pages 436–491):
Chapter 12 minimal pass Entropy Spectral research (pages 492–516):
Chapter thirteen assessment and layout of Sampling and dimension Networks (pages 517–558):
Chapter 14 choice of Variables and types (pages 559–580):
Chapter 15 Neural Networks (pages 581–604):
Chapter sixteen procedure Complexity (pages 605–632):

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Additional resources for Entropy Theory and its Application in Environmental and Water Engineering

Example text

Sugawara (1971) used negentropy as a measure of order in discussing problems in water resources. For example, in the case of hydropower generation, the water falls down and its potential energy is converted into heat energy and then into electrical energy. The hydropower station utilizes the negentropy of water. Another example is river discharge, which, with large fluctuations, has low negentropy or the smaller the fluctuation the higher the negentropy. In the case of a water treatment plant, input water is dirty and output water is clear or clean, meaning an increase in negentropy.

Compute the maximum entropy of each distribution in bits. Compare these two distributions by determining the difference in the information contents of these distributions. Is there a gain of information with the decrease in the number of possible outcomes? 7 Consider that a discrete random variable X takes on 10 values with probability distribution P : P : {p1 , p2 , . . , p10 } corresponding to X : {xi , i = 1, 2, . . , 10). What distribution P will yield the maximum and minimum values of the Shannon entropy?

An interesting point to be noted here is that the information is the sum of information associated with flipping each individual coin, that is, log2 2 + log2 2 + log2 2. This shows that it does not matter whether the coins are flipped simultaneously or separately. In all of this discussion the underlying assumption is that outcomes are equally likely. Can one extend the above concept of information to the outcomes of an experiment where they are not equally likely? To keep things simple, consider an experiment having N equally likely outcomes which are divided into two groups of N 1 and N 2 outcomes: N = N 1 + N 2 .

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