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Special Issue "Applications of Information Theory in the Geosciences II"

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

Deadline for manuscript submissions: 28 February 2019

Special Issue Editor

Guest Editor
Prof. Dr. Benjamin L. Ruddell

School of Informatics, Computing, and Cyber Systems, Northern Arizona University, USA
Website | E-Mail
Phone: 001-928-523-3124
Interests: complex systems; information theory; climate and urban microclimate; ecohydrology; water resources; water policy; statistics; engineering ethics; environmental data informatics; engineering education

Special Issue Information

Dear Colleagues,

Information Theory is creating many new applications in broad areas of science, particularly the in the domain of Complex Adaptive Systems. These new applications often blend theoretical developments of Information Theory with innovative applications to complex-systems problems in the geosciences. This Special Issue specifically emphasizes research that addresses geoscience problems using Information Theory approaches, by introducing a novel development of Information Theory for specific applications, and/or by solving a new geoscience problem using the tools of Information Theory. Submissions focused on software tools and datasets, or on topics at the boundaries of Information Theory, the geosciences, and other disciplines are also welcome

Prof. Dr. Benjamin Ruddell
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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 1600 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

  • Geoscience
  • Life science
  • Physics
  • Complex adaptive systems
  • Shannon entropy
  • Information theory
  • Nonlinearity
  • Statistics
  • Applications

Published Papers (8 papers)

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Research

Open AccessArticle Assessing Water Resources Vulnerability by Using a Rough Set Cloud Model: A Case Study of the Huai River Basin, China
Entropy 2019, 21(1), 14; https://doi.org/10.3390/e21010014
Received: 7 November 2018 / Revised: 14 December 2018 / Accepted: 21 December 2018 / Published: 24 December 2018
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Abstract
Assessing water resources vulnerability is the foundation of local water resources management. However, as one of the major water systems in China, there is no existing evaluation index system that can effectively assess water resource vulnerability for the Huai River basin. To address
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Assessing water resources vulnerability is the foundation of local water resources management. However, as one of the major water systems in China, there is no existing evaluation index system that can effectively assess water resource vulnerability for the Huai River basin. To address this issue, we identified key vulnerability factors, constructed an evaluation index system, and applied such system to evaluate water resources vulnerability for the Huai River basin empirically in this paper. Specifically, our evaluation index system consists of 18 indexes selected from three different aspects: water shortage, water pollution, and water-related natural disaster. Then, the improved blind deletion rough set method was used to reduce the size of the evaluation index while keep the evaluation power. In addition, the improved conditional information entropy rough set method was employed to calculate the weights of evaluation indexes. Based on the reduced index system and calculated weights, a rough set cloud model was applied to carry out the vulnerability evaluation. The empirical results show that the Huai River basin water resources were under severe vulnerability conditions for most of the time between 2000 and 2016, and the Most Stringent Water Resources Management System (MS-WRMS) established in 2012 did not work effectively as expected. Full article
(This article belongs to the Special Issue Applications of Information Theory in the Geosciences II)
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Open AccessArticle Anomaly Detection in Paleoclimate Records Using Permutation Entropy
Entropy 2018, 20(12), 931; https://doi.org/10.3390/e20120931
Received: 3 November 2018 / Revised: 29 November 2018 / Accepted: 4 December 2018 / Published: 5 December 2018
Cited by 1 | PDF Full-text (8481 KB) | HTML Full-text | XML Full-text
Abstract
Permutation entropy techniques can be useful for identifying anomalies in paleoclimate data records, including noise, outliers, and post-processing issues. We demonstrate this using weighted and unweighted permutation entropy with water-isotope records containing data from a deep polar ice core. In one region of
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Permutation entropy techniques can be useful for identifying anomalies in paleoclimate data records, including noise, outliers, and post-processing issues. We demonstrate this using weighted and unweighted permutation entropy with water-isotope records containing data from a deep polar ice core. In one region of these isotope records, our previous calculations (See Garland et al. 2018) revealed an abrupt change in the complexity of the traces: specifically, in the amount of new information that appeared at every time step. We conjectured that this effect was due to noise introduced by an older laboratory instrument. In this paper, we validate that conjecture by reanalyzing a section of the ice core using a more advanced version of the laboratory instrument. The anomalous noise levels are absent from the permutation entropy traces of the new data. In other sections of the core, we show that permutation entropy techniques can be used to identify anomalies in the data that are not associated with climatic or glaciological processes, but rather effects occurring during field work, laboratory analysis, or data post-processing. These examples make it clear that permutation entropy is a useful forensic tool for identifying sections of data that require targeted reanalysis—and can even be useful for guiding that analysis. Full article
(This article belongs to the Special Issue Applications of Information Theory in the Geosciences II)
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Open AccessArticle Quantitative Assessment of Landslide Susceptibility Comparing Statistical Index, Index of Entropy, and Weights of Evidence in the Shangnan Area, China
Entropy 2018, 20(11), 868; https://doi.org/10.3390/e20110868
Received: 12 October 2018 / Revised: 6 November 2018 / Accepted: 8 November 2018 / Published: 10 November 2018
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Abstract
In this study, a comparative analysis of the statistical index (SI), index of entropy (IOE) and weights of evidence (WOE) models was introduced to landslide susceptibility mapping, and the performance of the three models was validated and systematically compared. As one of the
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In this study, a comparative analysis of the statistical index (SI), index of entropy (IOE) and weights of evidence (WOE) models was introduced to landslide susceptibility mapping, and the performance of the three models was validated and systematically compared. As one of the most landslide-prone areas in Shaanxi Province, China, Shangnan County was selected as the study area. Firstly, a series of reports, remote sensing images and geological maps were collected, and field surveys were carried out to prepare a landslide inventory map. A total of 348 landslides were identified in study area, and they were reclassified as a training dataset (70% = 244 landslides) and testing dataset (30% = 104 landslides) by random selection. Thirteen conditioning factors were then employed. Corresponding thematic data layers and landslide susceptibility maps were generated based on ArcGIS software. Finally, the area under the curve (AUC) values were calculated for the training dataset and the testing dataset in order to validate and compare the performance of the three models. For the training dataset, the AUC plots showed that the WOE model had the highest accuracy rate of 76.05%, followed by the SI model (74.67%) and the IOE model (71.12%). In the case of the testing dataset, the prediction accuracy rates for the SI, IOE and WOE models were 73.75%, 63.89%, and 75.10%, respectively. It can be concluded that the WOE model had the best prediction capacity for landslide susceptibility mapping in Shangnan County. The landslide susceptibility map produced by the WOE model had a profound geological and engineering significance in terms of landslide hazard prevention and control in the study area and other similar areas. Full article
(This article belongs to the Special Issue Applications of Information Theory in the Geosciences II)
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Open AccessArticle Optimization on theBuried Depth of Subsurface Drainage under Greenhouse Condition Based on Entropy Evaluation Method
Entropy 2018, 20(11), 859; https://doi.org/10.3390/e20110859
Received: 11 September 2018 / Revised: 16 October 2018 / Accepted: 17 October 2018 / Published: 8 November 2018
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Abstract
Numerous indicators under the plant-soil system should be taken into consideration when developing an appropriate agricultural water conservancy project. Entropy evaluation method offers excellent prospects in optimizing agricultural management schemes. To investigate the impact of different buried depths (30, 45, 60, 75, 90,
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Numerous indicators under the plant-soil system should be taken into consideration when developing an appropriate agricultural water conservancy project. Entropy evaluation method offers excellent prospects in optimizing agricultural management schemes. To investigate the impact of different buried depths (30, 45, 60, 75, 90, and 105 cm) of subsurface drainage pipes on greenhouse plant-soil systems, the tomato was employed as plant material, and the marketable yield, fruit sugar to acid ratio, soil electrical conductivity, nitrogen loss rate, as well as crop water and fertilizer use efficiency were observed. Based on these indicators, the entropy evaluation method was used to select the optimal buried depth of subsurface drainage pipes. Both the calculation results of objective and subjective weights indicated that tomato yield and soil electrical conductivity were relatively more crucial than other indexes, and their comprehensive weights were 0.43 and 0.34, respectively. The 45 cm buried depth possessed the optimal comprehensive benefits, with entropy evaluation value of 0.94. Under 45 cm buried depth, the loss rate of soil available nitrogen was 13.9%, the decrease rate of soil salinity was 49.2%, and the tomato yield, sugar to acid ratio, nitrogen use efficiency, and water use efficiency were 112 kg·ha−1, 8.3, 39.7%, and 42.0%, respectively. Full article
(This article belongs to the Special Issue Applications of Information Theory in the Geosciences II)
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Open AccessArticle Evaluating Sustainability of Regional Water Resources Based on Improved Generalized Entropy Method
Entropy 2018, 20(9), 715; https://doi.org/10.3390/e20090715
Received: 2 August 2018 / Revised: 14 September 2018 / Accepted: 15 September 2018 / Published: 18 September 2018
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Abstract
The sustainability of regional water resources has important supporting data needed for establishing policies on the sustainable development of the social economy. The purpose of this paper is to propose an assessment method to accurately reflect the sustainability of regional water resources in
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The sustainability of regional water resources has important supporting data needed for establishing policies on the sustainable development of the social economy. The purpose of this paper is to propose an assessment method to accurately reflect the sustainability of regional water resources in various areas. The method is based on the relative entropy of the information entropy theory. The steps are as follows. Firstly, the pretreatment of the evaluation sample data is required, before the relative entropy of each standard evaluation sample and evaluation grade (SEG) is calculated to obtain the entropy weight of each evaluation index. After this, the entropy weighted comprehensive index (WCI) of the standard evaluation grade sample is obtained. The function relation between WCI and SEG can be fitted by the cubic polynomial to construct the evaluation function. Using the above steps, a generalized entropy method (GEM) for the sustainable assessment of regional water resources is established and it is used to evaluate the sustainability of water resources in the Pingba and Huai River areas in China. The results show that the proposed GEM model can accurately reflect the sustainable water resources in the two regions. Compared with the other evaluation models, such as the Shepherd method, Artificial Neural Network and Fuzzy comprehensive evaluation, the GEM model has larger differences in its evaluation results, which are more reasonable. Thus, the proposed GEM model can provide scientific data support for coordinating the relationship between the sustainable development and utilization of regional water resources in order to improve the development of regional population, society and economy. Full article
(This article belongs to the Special Issue Applications of Information Theory in the Geosciences II)
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Open AccessArticle Analysis of Solar Irradiation Time Series Complexity and Predictability by Combining Kolmogorov Measures and Hamming Distance for La Reunion (France)
Entropy 2018, 20(8), 570; https://doi.org/10.3390/e20080570
Received: 30 June 2018 / Revised: 28 July 2018 / Accepted: 30 July 2018 / Published: 1 August 2018
Cited by 2 | PDF Full-text (3812 KB) | HTML Full-text | XML Full-text
Abstract
Analysis of daily solar irradiation variability and predictability in space and time is important for energy resources planning, development, and management. The natural variability of solar irradiation is being complicated by atmospheric conditions (in particular cloudiness) and orography, which introduce additional complexity into
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Analysis of daily solar irradiation variability and predictability in space and time is important for energy resources planning, development, and management. The natural variability of solar irradiation is being complicated by atmospheric conditions (in particular cloudiness) and orography, which introduce additional complexity into the phenomenological records. To address this question for daily solar irradiation data recorded during the years 2013, 2014 and 2015 at 11 stations measuring solar irradiance on La Reunion French tropical Indian Ocean Island, we use a set of novel quantitative tools: Kolmogorov complexity (KC) with its derivative associated measures and Hamming distance (HAM) and their combination to assess complexity and corresponding predictability. We find that all half-day (from sunrise to sunset) solar irradiation series exhibit high complexity. However, all of them can be classified into three groups strongly influenced by trade winds that circulate in a “flow around” regime: the windward side (trade winds slow down), the leeward side (diurnal thermally-induced circulations dominate) and the coast parallel to trade winds (winds are accelerated due to Venturi effect). We introduce Kolmogorov time (KT) that quantifies the time span beyond which randomness significantly influences predictability. Full article
(This article belongs to the Special Issue Applications of Information Theory in the Geosciences II)
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Open AccessArticle Water Resources Carrying Capacity Evaluation and Diagnosis Based on Set Pair Analysis and Improved the Entropy Weight Method
Entropy 2018, 20(5), 359; https://doi.org/10.3390/e20050359
Received: 10 April 2018 / Revised: 7 May 2018 / Accepted: 9 May 2018 / Published: 11 May 2018
Cited by 2 | PDF Full-text (1785 KB) | HTML Full-text | XML Full-text
Abstract
To quantitatively evaluate and diagnose the carrying capacity of regional water resources under uncertain conditions, an index system and corresponding grade criteria were constructed from the perspective of carrying subsystem. Meanwhile, an improved entropy weight method was used to determine the objective weight
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To quantitatively evaluate and diagnose the carrying capacity of regional water resources under uncertain conditions, an index system and corresponding grade criteria were constructed from the perspective of carrying subsystem. Meanwhile, an improved entropy weight method was used to determine the objective weight of the index. Then, an evaluation model was built by applying set pair analysis, and a set pair potential based on subtraction was proposed to identify the carrying vulnerability factors. Finally, an empirical study was carried out in Anhui Province. The results showed that the consistency among objective weights of each index was considered, and the uncertainty between the index and grade criterion was reasonably dealt with. Furthermore, although the carrying situation in Anhui was severe, the development tended to be improved. The status in Southern Anhui was superior to that in the middle area, and that in the northern part was relatively grim. In addition, for Northern Anhui, the fewer water resources chiefly caused its long-term overloaded status. The improvement of capacity in the middle area was mainly hindered by its deficient ecological water consumption and limited water-saving irrigation area. Moreover, the long-term loadable condition in the southern part was due largely to its relatively abundant water resources and small population size. This evaluation and diagnosis method can be widely applied to carrying issues in other resources and environment fields. Full article
(This article belongs to the Special Issue Applications of Information Theory in the Geosciences II)
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Open AccessArticle On the Statistical Mechanics of Alien Species Distribution
Entropy 2017, 19(12), 674; https://doi.org/10.3390/e19120674
Received: 20 November 2017 / Revised: 6 December 2017 / Accepted: 8 December 2017 / Published: 9 December 2017
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Abstract
Many species of plants are found in regions to which they are alien. Their global distributions are characterised by a family of exponential functions of the kind that arise in elementary statistical mechanics (an example in ecology is MacArthur’s broken stick). We show
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Many species of plants are found in regions to which they are alien. Their global distributions are characterised by a family of exponential functions of the kind that arise in elementary statistical mechanics (an example in ecology is MacArthur’s broken stick). We show here that all these functions are quantitatively reproduced by a model containing a single parameter—some global resource partitioned at random on the two axes of species number and site number. A dynamical model generating this equilibrium is a two-fold stochastic process and suggests a curious and interesting biological interpretation in terms of niche structures fluctuating with time and productivity, with sites and species highly idiosyncratic. Idiosyncrasy implies that attempts to identify a priori those species likely to become naturalised are unlikely to be successful. Although this paper is primarily concerned with a particular problem in population biology, the two-fold stochastic process may be of more general interest. Full article
(This article belongs to the Special Issue Applications of Information Theory in the Geosciences II)
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