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Entropy
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Entropy and Space-Time Analysis in Environment and Health
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Entropy and Space-Time Analysis in Environment and Health

A special issue of Entropy (ISSN 1099-4300).

Deadline for manuscript submissions: closed (15 January 2015) | Viewed by 41725

Special Issue Editor

Prof. Dr. Hwa-Lung Yu

E-Mail Website
Guest Editor
Department of Bioenvironmental System Engineering, National Taiwan University, Taipei 10617, Taiwan
Interests: spatiotemporal stochastics and geostatistics; GIS; water resources; groundwater
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleague,

Stochastic nature is considered to be inherent in the space-time variations of complex natural and social systems, e.g., environmental processes and infectious disease. The stochastic uncertainties can result from the limited understandings of the (1) underlying dynamics, (2) external forcing, (3) initial and boundary conditions, as well as the limited observations across space and time. Entropy and its related methods can provide ways to characterize and formulate the uncertainties of the complex space-time processes. This special issue aims to present approaches and applications of entropy and related methods for the space-time analysis and modeling of the complex environmental systems and their associations with public health, e.g., disease dynamics.

Dr. Hwa-Lung Yu
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • space-time analysis
  • geostatistics
  • environmental modeling
  • disease modeling

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Published Papers (7 papers)

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Research

410 KiB  
Article
Multifractal Dimensional Dependence Assessment Based on Tsallis Mutual Information
by José M. Angulo and Francisco J. Esquivel
Entropy 2015, 17(8), 5382-5401; https://doi.org/10.3390/e17085382 - 29 Jul 2015
Cited by 21 | Viewed by 4929
Abstract
Entropy-based tools are commonly used to describe the dynamics of complex systems. In the last few decades, non-extensive statistics, based on Tsallis entropy, and multifractal techniques have shown to be useful to characterize long-range interaction and scaling behavior. In this paper, an approach [...] Read more.
Entropy-based tools are commonly used to describe the dynamics of complex systems. In the last few decades, non-extensive statistics, based on Tsallis entropy, and multifractal techniques have shown to be useful to characterize long-range interaction and scaling behavior. In this paper, an approach based on generalized Tsallis dimensions is used for the formulation of mutual-information-related dependence coefficients in the multifractal domain. Different versions according to the normalizing factor, as well as to the inclusion of the non-extensivity correction term are considered and discussed. An application to the assessment of dimensional interaction in the structural dynamics of a seismic real series is carried out to illustrate the usefulness and comparative performance of the measures introduced. Full article
(This article belongs to the Special Issue Entropy and Space-Time Analysis in Environment and Health)
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2419 KiB  
Communication
Dimensional Upgrade Approach for Spatial-Temporal Fusion of Trend Series in Subsidence Evaluation
by Shih-Jung Wang
Entropy 2015, 17(5), 3035-3052; https://doi.org/10.3390/e17053035 - 11 May 2015
Cited by 4 | Viewed by 5131
Abstract
Physical models and grey system models (GSMs) are commonly used to evaluate and predict physical behavior. A physical model avoids the incorrect trend series of a GSM, whereas a GSM avoids the assumptions and uncertainty of a physical model. A technique that combines [...] Read more.
Physical models and grey system models (GSMs) are commonly used to evaluate and predict physical behavior. A physical model avoids the incorrect trend series of a GSM, whereas a GSM avoids the assumptions and uncertainty of a physical model. A technique that combines the results of physical models and GSMs would make prediction more reasonable and reliable. This study proposes a fusion method for combining two trend series, calculated using two one-dimensional models, respectively, that uses a slope criterion and a distance weighting factor in the temporal and spatial domains. The independent one-dimensional evaluations are upgraded to a spatially and temporally connected two-dimensional distribution. The proposed technique was applied to a subsidence problem in Jhuoshuei River Alluvial Fan, Taiwan. The fusion results show dramatic decreases of subsidence quantity and rate compared to those estimated by the GSM. The subsidence behavior estimated using the proposed method is physically reasonable due to a convergent trend of subsidence under the assumption of constant discharge of groundwater. The technique proposed in this study can be used in fields that require a combination of two trend series from physical and nonphysical models. Full article
(This article belongs to the Special Issue Entropy and Space-Time Analysis in Environment and Health)
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2195 KiB  
Article
Detection of Changes in Ground-Level Ozone Concentrations via Entropy
by Yuehua Wu, Baisuo Jin and Elton Chan
Entropy 2015, 17(5), 2749-2763; https://doi.org/10.3390/e17052749 - 30 Apr 2015
Cited by 1 | Viewed by 4846
Abstract
Ground-level ozone concentration is a key indicator of air quality. Theremay exist sudden changes in ozone concentration data over a long time horizon, which may be caused by the implementation of government regulations and policies, such as establishing exhaust emission limits for on-road [...] Read more.
Ground-level ozone concentration is a key indicator of air quality. Theremay exist sudden changes in ozone concentration data over a long time horizon, which may be caused by the implementation of government regulations and policies, such as establishing exhaust emission limits for on-road vehicles. To monitor and assess the efficacy of these policies, we propose a methodology for detecting changes in ground-level ozone concentrations, which consists of three major steps: data transformation, simultaneous autoregressive modelling and change-point detection on the estimated entropy. To show the effectiveness of the proposed methodology, the methodology is applied to detect changes in ground-level ozone concentration data collected in the Toronto region of Canada between June and September for the years from 1988 to 2009. The proposed methodology is also applicable to other climate data. Full article
(This article belongs to the Special Issue Entropy and Space-Time Analysis in Environment and Health)
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9219 KiB  
Article
High Recharge Areas in the Choushui River Alluvial Fan (Taiwan) Assessed from Recharge Potential Analysis and Average Storage Variation Indexes
by Jui-Pin Tsai, Yu-Wen Chen, Liang-Cheng Chang, Yi-Ming Kuo, Yu-Hsuan Tu and Chen-Che Pan
Entropy 2015, 17(4), 1558-1580; https://doi.org/10.3390/e17041558 - 24 Mar 2015
Cited by 7 | Viewed by 6019
Abstract
High recharge areas significantly influence the groundwater quality and quantity in regional groundwater systems. Many studies have applied recharge potential analysis (RPA) to estimate groundwater recharge potential (GRP) and have delineated high recharge areas based on the estimated GRP. However, most of these [...] Read more.
High recharge areas significantly influence the groundwater quality and quantity in regional groundwater systems. Many studies have applied recharge potential analysis (RPA) to estimate groundwater recharge potential (GRP) and have delineated high recharge areas based on the estimated GRP. However, most of these studies define the RPA parameters with supposition, and this represents a major source of uncertainty for applying RPA. To objectively define the RPA parameter values without supposition, this study proposes a systematic method based on the theory of parameter identification. A surrogate variable, namely the average storage variation (ASV) index, is developed to calibrate the RPA parameters, because of the lack of direct GRP observations. The study results show that the correlations between the ASV indexes and computed GRP values improved from 0.67 before calibration to 0.85 after calibration, thus indicating that the calibrated RPA parameters represent the recharge characteristics of the study area well; these data also highlight how defining the RPA parameters with ASV indexes can help to improve the accuracy. The calibrated RPA parameters were used to estimate the GRP distribution of the study area, and the GRP values were graded into five levels. High and excellent level areas are defined as high recharge areas, which composed 7.92% of the study area. Overall, this study demonstrates that the developed approach can objectively define the RPA parameters and high recharge areas of the Choushui River alluvial fan, and the results should serve as valuable references for the Taiwanese government in their efforts to conserve the groundwater quality and quantity of the study area. Full article
(This article belongs to the Special Issue Entropy and Space-Time Analysis in Environment and Health)
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1546 KiB  
Article
Mining Informative Hydrologic Data by Using Support Vector Machines and Elucidating Mined Data according to Information Entropy
by Shien-Tsung Chen
Entropy 2015, 17(3), 1023-1041; https://doi.org/10.3390/e17031023 - 2 Mar 2015
Cited by 16 | Viewed by 5485
Abstract
The support vector machine is used as a data mining technique to extract informative hydrologic data on the basis of a strong relationship between error tolerance and the number of support vectors. Hydrologic data of flash flood events in the Lan-Yang River basin [...] Read more.
The support vector machine is used as a data mining technique to extract informative hydrologic data on the basis of a strong relationship between error tolerance and the number of support vectors. Hydrologic data of flash flood events in the Lan-Yang River basin in Taiwan were used for the case study. Various percentages (from 50% to 10%) of hydrologic data, including those for flood stage and rainfall data, were mined and used as informative data to characterize a flood hydrograph. Information on these mined hydrologic data sets was quantified using entropy indices, namely marginal entropy, joint entropy, transinformation, and conditional entropy. Analytical results obtained using the entropy indices proved that the mined informative data could be hydrologically interpreted and have a meaningful explanation based on information entropy. Estimates of marginal and joint entropies showed that, in view of flood forecasting, the flood stage was a more informative variable than rainfall. In addition, hydrologic models with variables containing more total information were preferable to variables containing less total information. Analysis results of transinformation explained that approximately 30% of information on the flood stage could be derived from the upstream flood stage and 10% to 20% from the rainfall. Elucidating the mined hydrologic data by applying information theory enabled using the entropy indices to interpret various hydrologic processes. Full article
(This article belongs to the Special Issue Entropy and Space-Time Analysis in Environment and Health)
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5812 KiB  
Article
Phytotoponyms, Geographical Features and Vegetation Coverage in Western Hubei, China
by Guanghui Shi, Fu Ren, Qingyun Du and Nan Gao
Entropy 2015, 17(3), 984-1006; https://doi.org/10.3390/e17030984 - 2 Mar 2015
Cited by 8 | Viewed by 6294
Abstract
The purpose of this paper is to present and exploit fundamental information, such as semantic meanings and geographical features, of phytotoponyms (a type of toponym that includes plant names) in Western Hubei (China). Long-term vegetation degradation is also estimated. Toponym data for this [...] Read more.
The purpose of this paper is to present and exploit fundamental information, such as semantic meanings and geographical features, of phytotoponyms (a type of toponym that includes plant names) in Western Hubei (China). Long-term vegetation degradation is also estimated. Toponym data for this study were obtained from the place names database of Hubei Province at the Civil Affairs Department of Hubei. In total, 1259 instances of phytotoponyms were recognised; 898 (71.3%) were woody plant toponyms, and 361 (28.7%) were herbaceous plant toponyms. Subsequently, we randomly selected a similar number (1250) of non-phytotoponyms to compare with the phytotoponyms. All toponyms were localised and geo-referenced. The results showed that the most common plant names recognisable in place names are common plants that have a close connection with daily life and positive morals in Chinese culture and literature. The occurrence of plant names can reflect the characteristic plants of a city. The vegetation coverage rate where phytotoponyms are located is higher than that in non-phytotoponym areas. Altitude has a stronger correlation with the number of phytotoponyms than slope and vegetation coverage degree. The identification of long-term vegetation degradation based on phytotoponyms is presented for reference only, and other methods and materials are needed to validate these results. Full article
(This article belongs to the Special Issue Entropy and Space-Time Analysis in Environment and Health)
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6384 KiB  
Article
Landscape Analysis of Geographical Names in Hubei Province, China
by Xixi Chen, Tao Hu, Fu Ren, Deng Chen, Lan Li and Nan Gao
Entropy 2014, 16(12), 6313-6337; https://doi.org/10.3390/e16126313 - 1 Dec 2014
Cited by 14 | Viewed by 8430
Abstract
Hubei Province is the hub of communications in central China, which directly determines its strategic position in the country’s development. Additionally, Hubei Province is well-known for its diverse landforms, including mountains, hills, mounds and plains. This area is called “The Province of Thousand [...] Read more.
Hubei Province is the hub of communications in central China, which directly determines its strategic position in the country’s development. Additionally, Hubei Province is well-known for its diverse landforms, including mountains, hills, mounds and plains. This area is called “The Province of Thousand Lakes” due to the abundance of water resources. Geographical names are exclusive names given to physical or anthropogenic geographic entities at specific spatial locations and are important signs by which humans understand natural and human activities. In this study, geographic information systems (GIS) technology is adopted to establish a geodatabase of geographical names with particular characteristics in Hubei Province and extract certain geomorphologic and environmental factors. We carry out landscape analysis of mountain-related geographical names and water-related geographical names respectively. In the end, we calculate the information entropy of geographical names of each county to describe the diversity and inhomogeneity of place names in Hubei province. Our study demonstrates that geographical names represent responses to the cultural landscape and physical environment. The geographical names are more interesting in specific landscapes, such as mountains and rivers. Full article
(This article belongs to the Special Issue Entropy and Space-Time Analysis in Environment and Health)
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