pathways in an estuarine channel. Physics and Chemistry of the
Earth, Part B: Hydrology, Oceans and Atmosphere, 26,
747–752.
Fenster, M. S., Fitzgerald, D. M., Bohlen, W. F., Lewis, R. S., and
Baldwin, C. T., 1990. Stability of giant sand waves in Eastern
Long Island Sound, U.S.A. Marine Geology, 91, 207–225.
Gómez, E. A., Cuadrado, D. G., and Pierini, J. O., 2010. Sand transport on an estuarine submarine dune field. Geomorphology, 121,
257–265.
Harbor, D. J., 1998. Dynamics of bedforms in the lower Mississippi
River. Journal of Sedimentary Research, 68, 750–762.
Kuijpers, A., Hansen, B., Hühnerbach, V., Larsen, B., Nielsen, T.,
and Werner, F., 2002. Norwegian Sea overflow through the
Faroe–Shetland gateway as documented by its bedforms.
Marine Geology, 188, 147–164.
Morales, J. A., Delgado, I., and Gutierrez-Mas, J. M., 2006.
Sedimentary characterization of bed types along the Guadiana
Estuary (SW Europe) before the construction of the Alqueva
dam. Estuarine, Coastal and Shelf Science, 70, 117–131.
Sherwood, C. R., and Creager, J. S., 1990. Sedimentary geology of
the Columbia River Estuary. Progress in Oceanography, 25,
15–79.
Van Lancker, V., Lanckneus, J., Moerkerke, G., Hearn, S., Hoekstra,
P., and Levoy, F., 2004. Coastal and nearshore morphology,
bedforms and sediment transport pathways at Teignmouth
(UK). Continental Shelf Research, 24, 1171–1202.
Woodruff, J. D., Geyer, W. R., Sommerfield, C. K., and Driscoll,
N. W., 2001. Seasonal variation of sediment deposition in the
Hudson River estuary. Marine Geology, 179, 105–119.
Wright, L. D., Prior, D. B., Hobbs, C. H., Byrne, R. J., Boon, J. D.,
Schaffner, L. C., and Green, M. O., 1987. Spatial variability of
bottom types in the lower Chesapeake Bay and adjoining estuaries and inner shelf. Estuarine, Coastal and Shelf Science, 24,
765–784.
Cross-references
Estuarine Sediment Composition
Mass Physical Sediment Properties
Sediment Transport
Sedimentary Structures
SOFT COMPUTING
M. C. Deo
Department of Civil Engineering, Indian Institute of
Technology Bombay, Mumbai, India
Synonyms
Artificial intelligence; Computational intelligence;
Evolutionary algorithms; Fuzzy logic; Machine learning;
Neural networks
Definition
Soft computing refers to those computational methods that
are inherently soft toward inexactness of data as well as
that of problem specification. It is defined as the technique
that targets the tolerance of the real world to uncertainty,
imprecision, inaccuracy, and partial truths in data as well
as problem specification in order to arrive at meaningful
solutions (Zadeh, 1994).
Soft Computing
Following a series of works by Professor Lotfi Zadeh since
the mid-1960s, usage of the term “soft computing” became
prominent in the early 1990s (Zadeh, 1994). The main tools
of soft computing are neural networks, fuzzy logic, evolutionary algorithms (genetic algorithms, differential evolution), and probability embedded fuzzy tools. Support
vector machines, meta-heuristic and swarm intelligence,
and colony optimization, particle swarm optimizations,
and chaos theory also fall under the purview of soft computing (Karray and de Silva, 2004). Apart from the use of
a single tool like neural network or fuzzy logic, their combinations such as neuro-fuzzy inference systems have also
been beneficial (Azmathullah et al., 2008). Traditional logic
recognizes only two crisp values (e.g., true or false, yes or
no) and accordingly assigns the value of 0 or 1 to the variables in binary descriptions. On the contrary, the
multivalued fuzzy logic assigns a range of values in
between (0, 1) to the variables depending on the uncertainty
and can thus account for imprecision and partial truths in
data and system specification. Subjectivity can thus be
replaced by objectivity in computations (Ross, 2004).
Applications of soft computing tools in estuarine and
coastal fields have targeted function approximation, optimization, control, system modeling, and pattern recognition. A large number of applications pertain to the use of
neural networks, fuzzy logic, and genetic algorithms to
estimate and forecast waves, tides, currents, storm surge,
sediment transport, salinity intrusion, foundation scour,
structural optimization, control, and effects (Jain and
Deo, 2006; Deo, 2010).
Bibliography
Azmathullah, H. M., Deo, M. C., and Deolalikar, P. B., 2008. Alternative neural networks to estimate scour below spillways.
Advances in Engineering Software, 39(2008), 689–698.
Deo, M. C., 2010. Artificial neural networks in coastal and ocean engineering. Indian Journal of Geo-Marine Sciences, 39(4), 589–596.
Jain, P., and Deo, M. C., 2006. Neural networks in ocean engineering. International Journal of Ships and Offshore Structures,
1(1), 25–35.
Karray, F., and de Silva, C., 2004. Soft Computing and Intelligent
Systems Design – Theory, Tools and Applications. Boston: Addison Wesley.
Ross, T., 2004. Fuzzy Logic with Engineering Applications.
Hoboken: Wiley.
Zadeh, L. A., 1994. Fuzzy logic, neural networks and soft computing. Communication of the ACM, 37(3), 77–84.
Cross-references
Sediment Transport
Storm Surges
Tides
SOFT COMPUTING
605
Earth, Part B: Hydrology, Oceans and Atmosphere, 26,
747–752.
Fenster, M. S., Fitzgerald, D. M., Bohlen, W. F., Lewis, R. S., and
Baldwin, C. T., 1990. Stability of giant sand waves in Eastern
Long Island Sound, U.S.A. Marine Geology, 91, 207–225.
Gómez, E. A., Cuadrado, D. G., and Pierini, J. O., 2010. Sand transport on an estuarine submarine dune field. Geomorphology, 121,
257–265.
Harbor, D. J., 1998. Dynamics of bedforms in the lower Mississippi
River. Journal of Sedimentary Research, 68, 750–762.
Kuijpers, A., Hansen, B., Hühnerbach, V., Larsen, B., Nielsen, T.,
and Werner, F., 2002. Norwegian Sea overflow through the
Faroe–Shetland gateway as documented by its bedforms.
Marine Geology, 188, 147–164.
Morales, J. A., Delgado, I., and Gutierrez-Mas, J. M., 2006.
Sedimentary characterization of bed types along the Guadiana
Estuary (SW Europe) before the construction of the Alqueva
dam. Estuarine, Coastal and Shelf Science, 70, 117–131.
Sherwood, C. R., and Creager, J. S., 1990. Sedimentary geology of
the Columbia River Estuary. Progress in Oceanography, 25,
15–79.
Van Lancker, V., Lanckneus, J., Moerkerke, G., Hearn, S., Hoekstra,
P., and Levoy, F., 2004. Coastal and nearshore morphology,
bedforms and sediment transport pathways at Teignmouth
(UK). Continental Shelf Research, 24, 1171–1202.
Woodruff, J. D., Geyer, W. R., Sommerfield, C. K., and Driscoll,
N. W., 2001. Seasonal variation of sediment deposition in the
Hudson River estuary. Marine Geology, 179, 105–119.
Wright, L. D., Prior, D. B., Hobbs, C. H., Byrne, R. J., Boon, J. D.,
Schaffner, L. C., and Green, M. O., 1987. Spatial variability of
bottom types in the lower Chesapeake Bay and adjoining estuaries and inner shelf. Estuarine, Coastal and Shelf Science, 24,
765–784.
Cross-references
Estuarine Sediment Composition
Mass Physical Sediment Properties
Sediment Transport
Sedimentary Structures
SOFT COMPUTING
M. C. Deo
Department of Civil Engineering, Indian Institute of
Technology Bombay, Mumbai, India
Synonyms
Artificial intelligence; Computational intelligence;
Evolutionary algorithms; Fuzzy logic; Machine learning;
Neural networks
Definition
Soft computing refers to those computational methods that
are inherently soft toward inexactness of data as well as
that of problem specification. It is defined as the technique
that targets the tolerance of the real world to uncertainty,
imprecision, inaccuracy, and partial truths in data as well
as problem specification in order to arrive at meaningful
solutions (Zadeh, 1994).
Soft Computing
Following a series of works by Professor Lotfi Zadeh since
the mid-1960s, usage of the term “soft computing” became
prominent in the early 1990s (Zadeh, 1994). The main tools
of soft computing are neural networks, fuzzy logic, evolutionary algorithms (genetic algorithms, differential evolution), and probability embedded fuzzy tools. Support
vector machines, meta-heuristic and swarm intelligence,
and colony optimization, particle swarm optimizations,
and chaos theory also fall under the purview of soft computing (Karray and de Silva, 2004). Apart from the use of
a single tool like neural network or fuzzy logic, their combinations such as neuro-fuzzy inference systems have also
been beneficial (Azmathullah et al., 2008). Traditional logic
recognizes only two crisp values (e.g., true or false, yes or
no) and accordingly assigns the value of 0 or 1 to the variables in binary descriptions. On the contrary, the
multivalued fuzzy logic assigns a range of values in
between (0, 1) to the variables depending on the uncertainty
and can thus account for imprecision and partial truths in
data and system specification. Subjectivity can thus be
replaced by objectivity in computations (Ross, 2004).
Applications of soft computing tools in estuarine and
coastal fields have targeted function approximation, optimization, control, system modeling, and pattern recognition. A large number of applications pertain to the use of
neural networks, fuzzy logic, and genetic algorithms to
estimate and forecast waves, tides, currents, storm surge,
sediment transport, salinity intrusion, foundation scour,
structural optimization, control, and effects (Jain and
Deo, 2006; Deo, 2010).
Bibliography
Azmathullah, H. M., Deo, M. C., and Deolalikar, P. B., 2008. Alternative neural networks to estimate scour below spillways.
Advances in Engineering Software, 39(2008), 689–698.
Deo, M. C., 2010. Artificial neural networks in coastal and ocean engineering. Indian Journal of Geo-Marine Sciences, 39(4), 589–596.
Jain, P., and Deo, M. C., 2006. Neural networks in ocean engineering. International Journal of Ships and Offshore Structures,
1(1), 25–35.
Karray, F., and de Silva, C., 2004. Soft Computing and Intelligent
Systems Design – Theory, Tools and Applications. Boston: Addison Wesley.
Ross, T., 2004. Fuzzy Logic with Engineering Applications.
Hoboken: Wiley.
Zadeh, L. A., 1994. Fuzzy logic, neural networks and soft computing. Communication of the ACM, 37(3), 77–84.
Cross-references
Sediment Transport
Storm Surges
Tides
SOFT COMPUTING
605