Entropy Applications in Environmental and Water Engineering
Entropy theory has wide applications to a range of problems in the fields of environmental and water engineering, including river hydraulic geometry, fluvial hydraulics, water monitoring network design, river flow forecasting, floods and droughts, river network analysis, infiltration, soil moisture,...
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MDPI - Multidisciplinary Digital Publishing Institute
2019
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| Online Access: | Get Fullteks DOAB: description of the publication |
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| LEADER | 07415naaaa2201873uu 4500 | ||
|---|---|---|---|
| 001 | doab_20_500_12854_46556 | ||
| 005 | 20210211 | ||
| 020 | |a books978-3-03897-223-5 | ||
| 020 | |a 9783038972228 | ||
| 024 | 7 | |a 10.3390/books978-3-03897-223-5 |c doi | |
| 041 | 0 | |a English | |
| 042 | |a dc | ||
| 100 | 1 | |a Cui, Huijuan |4 auth | |
| 700 | 1 | |a Sivakumar, Bellie |4 auth | |
| 700 | 1 | |a Singh, Vijay P. |4 auth | |
| 245 | 1 | 0 | |a Entropy Applications in Environmental and Water Engineering |
| 260 | |b MDPI - Multidisciplinary Digital Publishing Institute |c 2019 | ||
| 300 | |a 1 electronic resource (512 p.) | ||
| 506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
| 520 | |a Entropy theory has wide applications to a range of problems in the fields of environmental and water engineering, including river hydraulic geometry, fluvial hydraulics, water monitoring network design, river flow forecasting, floods and droughts, river network analysis, infiltration, soil moisture, sediment transport, surface water and groundwater quality modeling, ecosystems modeling, water distribution networks, environmental and water resources management, and parameter estimation. Such applications have used several different entropy formulations, such as Shannon, Tsallis, Reacutenyi Burg, Kolmogorov, Kapur, configurational, and relative entropies, which can be derived in time, space, or frequency domains. More recently, entropy-based concepts have been coupled with other theories, including copula and wavelets, to study various issues associated with environmental and water resources systems. Recent studies indicate the enormous scope and potential of entropy theory in advancing research in the fields of environmental and water engineering, including establishing and explaining physical connections between theory and reality. The objective of this Special Issue is to provide a platform for compiling important recent and current research on the applications of entropy theory in environmental and water engineering. The contributions to this Special Issue have addressed many aspects associated with entropy theory applications and have shown the enormous scope and potential of entropy theory in advancing research in the fields of environmental and water engineering. | ||
| 540 | |a Creative Commons |f https://creativecommons.org/licenses/by-nc-nd/4.0/ |2 cc |4 https://creativecommons.org/licenses/by-nc-nd/4.0/ | ||
| 546 | |a English | ||
| 653 | |a hydrological risk analysis | ||
| 653 | |a modeling | ||
| 653 | |a water level | ||
| 653 | |a Poyang Lake basin | ||
| 653 | |a trend | ||
| 653 | |a composite multiscale sample entropy | ||
| 653 | |a flood frequency analysis | ||
| 653 | |a canopy flow | ||
| 653 | |a precipitation | ||
| 653 | |a water resources | ||
| 653 | |a complex systems | ||
| 653 | |a frequency analysis | ||
| 653 | |a optimization | ||
| 653 | |a combined forecast | ||
| 653 | |a neural network forecast | ||
| 653 | |a entropy spectral analysis time series analysis | ||
| 653 | |a environmental engineering | ||
| 653 | |a hydrometric network | ||
| 653 | |a sea surface temperature | ||
| 653 | |a kernel density estimation | ||
| 653 | |a robustness | ||
| 653 | |a turbulent flow | ||
| 653 | |a entropy production | ||
| 653 | |a connection entropy | ||
| 653 | |a flux concentration relation | ||
| 653 | |a turbulence | ||
| 653 | |a tropical rainfall | ||
| 653 | |a generalized gamma (GG) distribution | ||
| 653 | |a multi-events | ||
| 653 | |a El Niño | ||
| 653 | |a joint entropy | ||
| 653 | |a entropy weighting method | ||
| 653 | |a Anhui Province | ||
| 653 | |a changing environment | ||
| 653 | |a complexity | ||
| 653 | |a multiplicative cascades | ||
| 653 | |a Tsallis entropy | ||
| 653 | |a Hexi corridor | ||
| 653 | |a coherent structures | ||
| 653 | |a water resources vulnerability | ||
| 653 | |a uncertainty | ||
| 653 | |a variability | ||
| 653 | |a flow entropy | ||
| 653 | |a Hei River basin | ||
| 653 | |a fuzzy analytic hierarchy process | ||
| 653 | |a substitute | ||
| 653 | |a crop yield | ||
| 653 | |a conditional entropy production | ||
| 653 | |a entropy | ||
| 653 | |a flow duration curve | ||
| 653 | |a mean annual runoff | ||
| 653 | |a temperature | ||
| 653 | |a hydrometeorological extremes | ||
| 653 | |a resilience | ||
| 653 | |a Loess Plateau | ||
| 653 | |a information entropy | ||
| 653 | |a scaling | ||
| 653 | |a water distribution networks | ||
| 653 | |a cross entropy | ||
| 653 | |a randomness | ||
| 653 | |a forewarning model | ||
| 653 | |a entropy applications | ||
| 653 | |a quaternary catchment | ||
| 653 | |a spatio-temporal variability | ||
| 653 | |a probability distribution function | ||
| 653 | |a ant colony fuzzy clustering | ||
| 653 | |a radar | ||
| 653 | |a continuous probability distribution functions | ||
| 653 | |a Shannon entropy | ||
| 653 | |a informational entropy | ||
| 653 | |a information | ||
| 653 | |a confidence intervals | ||
| 653 | |a marginal entropy | ||
| 653 | |a rainfall forecast | ||
| 653 | |a entropy of information | ||
| 653 | |a streamflow | ||
| 653 | |a power laws | ||
| 653 | |a bootstrap aggregating | ||
| 653 | |a maximum entropy-copula method | ||
| 653 | |a spatial and dynamics characteristic | ||
| 653 | |a projection pursuit | ||
| 653 | |a set pair analysis | ||
| 653 | |a entropy theory | ||
| 653 | |a water resource carrying capacity | ||
| 653 | |a entropy parameter | ||
| 653 | |a precipitation frequency analysis | ||
| 653 | |a principle of maximum entropy | ||
| 653 | |a information theory | ||
| 653 | |a stochastic processes | ||
| 653 | |a network design | ||
| 653 | |a complement | ||
| 653 | |a cross elasticity | ||
| 653 | |a climacogram | ||
| 653 | |a methods of moments | ||
| 653 | |a hydrology | ||
| 653 | |a bagging | ||
| 653 | |a principle of maximum entropy (POME) | ||
| 653 | |a rainfall network | ||
| 653 | |a entropy ensemble filter | ||
| 653 | |a ensemble model simulation criterion | ||
| 653 | |a Lagrangian function | ||
| 653 | |a Beta-Lognormal model | ||
| 653 | |a cross-entropy minimization | ||
| 653 | |a ANN | ||
| 653 | |a configurational entropy | ||
| 653 | |a variation of information | ||
| 653 | |a statistical scaling | ||
| 653 | |a EEF method | ||
| 653 | |a water monitoring | ||
| 653 | |a maximum likelihood estimation | ||
| 653 | |a GB2 distribution | ||
| 653 | |a NDVI | ||
| 653 | |a four-parameter exponential gamma distribution | ||
| 653 | |a hydraulics | ||
| 653 | |a spatial optimization | ||
| 653 | |a Kolmogorov complexity | ||
| 653 | |a bootstrap neural networks | ||
| 653 | |a mutual information | ||
| 653 | |a accelerating genetic algorithm | ||
| 653 | |a groundwater depth | ||
| 653 | |a rainfall | ||
| 653 | |a tropical Pacific | ||
| 653 | |a water engineering | ||
| 653 | |a monthly streamflow forecasting | ||
| 653 | |a ENSO | ||
| 653 | |a nonlinear relation | ||
| 653 | |a Bayesian technique | ||
| 653 | |a non-point source pollution | ||
| 653 | |a Burg entropy | ||
| 653 | |a data-scarce | ||
| 653 | |a scaling laws | ||
| 653 | |a soil water content | ||
| 653 | |a arid region | ||
| 653 | |a land suitability evaluation | ||
| 653 | |a information transfer | ||
| 856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/1160 |7 0 |z Get Fullteks |
| 856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/46556 |7 0 |z DOAB: description of the publication |