Geology Reference
In-Depth Information
3.2.2 Application of Entropy Theory
Wider and major applications of entropy statistics emerged during the late 1960s
and the 1970s. This section provides a brief review on entropy statistics based upon
empirical research in industrial organizations, regional science, economics, and
environmental science. A well-known economist, Henri Theil, developed several
applications of information theory in economics and compiled them in his two
famous topics entitled Economics and Information Theory (1967) and Statistical
Decomposition Analysis (1972). In the late 1960s, entropy formulae and statistics
were popularly used in industrial organizations for empirical studies of industrial
concentration and distribution of market shares [
24
,
34
,
74
]. Later, the decompo-
sition property of the entropy formula was also widely used to examine corporate
diversi
uence on growth [
27
,
35
,
37
,
38
,
58
]. Harmancioglu and
Alpaslan [
31
] claimed that the entropy method allowed for quantitatively measuring
network ef
cation and its in
ciency in terms of the information produced by the network.
find some studies in the hydro-geological literature which have
employed entropy in the context of model optimization. Amorocho and Espildora
[
6
] used entropy to measure the information gained by the application of a
hydrologic model. Chapman [
16
] studied the application of entropy in various cases
involving the use of different assumed distribution functions, different types of
One can
ow
data, and also considered different units of entropy. Harmancioglu and Yevjevich
[
32
] used entropy to measure the information transmission among stations on the
same river. Krstanovic and Singh [
47
] investigated information transfer between
selected drought or
flood sequences, using marginal entropy, joint entropy, and
transinformation in long-term monthly rainfall series. Yang and Burn [
83
] pre-
sented an entropy-based methodology for design of data collection systems. Using
marginal entropy, Maruyama and Kawachi [
51
] investigated the characteristics of
local rainfall in Japan. Harmancioglu and Singh [
33
] have applied entropy methods
to assess quantitatively uncertainties of hydrologic variables, and models of water
resources systems and their parameters. Caselton and Husain [
14
] introduced the
entropy concept into a hydrometric network study. They computed the information
transmissions, based on the entropy concept, and selected stations with the maxi-
mum information transmission. Amorocho and Espildora [
6
] used entropy to
measure the information gained by application of a hydrologic model.
Husain [
36
] used an entropy-based methodology for selecting the optimum
number of stations from a dense network and expanding a network using data from
an existing sparse network by interpolation of information and identi
cation of
zones with minimum hydrologic information. Krstanovic and Singh [
47
] investi-
gated information transfer between selected drought or
flood sequences, using
marginal entropy, joint entropy, and transinformation in long-term monthly rainfall
series. Yang and Burn [
83
] presented an entropy-based methodology for design.
However,
elds, this topic explores new
dimensions of entropy theory to identify the best model structure, training data
interval, and better data frequency. Remesan et al. [
62
] used entropy theory as an
in a plethora of studies in related
