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Applications of Information Theory in the Geosciences II

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

Deadline for manuscript submissions: closed (28 February 2019) | Viewed by 40887

Special Issue Editor

School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA
Interests: complex systems; information theory; climate and urban microclimate; ecohydrology; water resources; water policy; statistics; engineering ethics; environmental data informatics; engineering education
Special Issues, Collections and Topics in MDPI journals

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

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Keywords

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

Published Papers (10 papers)

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Research

17 pages, 4347 KiB  
Article
Shannon Entropy for Measuring Spatial Complexity Associated with Mean Annual Runoff of Tertiary Catchments of the Middle Vaal Basin in South Africa
by Masengo Ilunga
Entropy 2019, 21(4), 366; https://doi.org/10.3390/e21040366 - 04 Apr 2019
Cited by 5 | Viewed by 2797
Abstract
This study evaluates essentially mean annual runoff (MAR) information gain/loss for tertiary catchments (TCs) in the Middle Vaal basin. Data sets from surface water resources (WR) of South Africa 1990 (WR90), 2005 (WR2005) and 2012 (WR2012) referred in this study as hydrological phases, [...] Read more.
This study evaluates essentially mean annual runoff (MAR) information gain/loss for tertiary catchments (TCs) in the Middle Vaal basin. Data sets from surface water resources (WR) of South Africa 1990 (WR90), 2005 (WR2005) and 2012 (WR2012) referred in this study as hydrological phases, are used in this evaluation. The spatial complexity level or information redundancy associated with MAR of TCs is derived as well as the relative change in entropy of TCs between hydrological phases. Redundancy and relative change in entropy are shown to coincide under specific conditions. Finally, the spatial distributions of MAR iso-information transmission (i.e., gain or loss) and MAR iso-information redundancy are established for the Middle Vaal basin. Full article
(This article belongs to the Special Issue Applications of Information Theory in the Geosciences II)
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13 pages, 1192 KiB  
Article
On the Diversity-Based Weighting Method for Risk Assessment and Decision-Making about Natural Hazards
by Pengyu Chen
Entropy 2019, 21(3), 269; https://doi.org/10.3390/e21030269 - 11 Mar 2019
Cited by 31 | Viewed by 3273
Abstract
The entropy-weighting method (EWM) and variation coefficient method (VCM) are two typical diversity-based weighting methods, which are widely used in risk assessment and decision-making for natural hazards. However, for the attributes with a specific range of values (RV), the weights calculated by EWM [...] Read more.
The entropy-weighting method (EWM) and variation coefficient method (VCM) are two typical diversity-based weighting methods, which are widely used in risk assessment and decision-making for natural hazards. However, for the attributes with a specific range of values (RV), the weights calculated by EWM and VCM (abbreviated as WE and WV) may be irrational. To solve this problem, a new indicator representing the dipartite degree is proposed, which is called the coefficient of dipartite degree (CDD), and the corresponding weighting method is called the dipartite coefficient method (DCM). Firstly, based on a large amount of statistical data, a comparison between the EWM and VCM is carried out. It is found that there is a strong correlation between the weights calculated by the EWM and VCM (abbreviated as WE and WV); however, in some cases the difference between WE and WV is big. Especially when the diversity of attributes is high, WE may be much larger than WV. Then, a comparison of the DCM, EWM and VCM is carried out based on two case studies. The results indicate that DCM is preferred for determining the weights of the attributes with a specific RV, and if the values of attributes are large enough, the EWM and VCM are both available. The EWM is more suitable for distinguishing the alternatives, but prudence is required when the diversity of an attribute is high. Finally, the applications of the diversity-based weighting method in natural hazards are discussed. Full article
(This article belongs to the Special Issue Applications of Information Theory in the Geosciences II)
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26 pages, 3868 KiB  
Article
Assessing Water Resources Vulnerability by Using a Rough Set Cloud Model: A Case Study of the Huai River Basin, China
by Yan Chen, Yazhong Feng, Fan Zhang and Lei Wang
Entropy 2019, 21(1), 14; https://doi.org/10.3390/e21010014 - 24 Dec 2018
Cited by 21 | Viewed by 3812
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 [...] Read more.
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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16 pages, 8481 KiB  
Article
Anomaly Detection in Paleoclimate Records Using Permutation Entropy
by Joshua Garland, Tyler R. Jones, Michael Neuder, Valerie Morris, James W. C. White and Elizabeth Bradley
Entropy 2018, 20(12), 931; https://doi.org/10.3390/e20120931 - 05 Dec 2018
Cited by 27 | Viewed by 6594
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 [...] Read more.
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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22 pages, 4759 KiB  
Article
Quantitative Assessment of Landslide Susceptibility Comparing Statistical Index, Index of Entropy, and Weights of Evidence in the Shangnan Area, China
by Jie Liu and Zhao Duan
Entropy 2018, 20(11), 868; https://doi.org/10.3390/e20110868 - 10 Nov 2018
Cited by 43 | Viewed by 4010
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 [...] Read more.
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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10 pages, 1191 KiB  
Article
Optimization on theBuried Depth of Subsurface Drainage under Greenhouse Condition Based on Entropy Evaluation Method
by Maomao Hou, Zhiyuan Lin, Jingnan Chen, Yaming Zhai, Qiu Jin and Fenglin Zhong
Entropy 2018, 20(11), 859; https://doi.org/10.3390/e20110859 - 08 Nov 2018
Cited by 14 | Viewed by 2524
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, [...] Read more.
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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12 pages, 836 KiB  
Article
Evaluating Sustainability of Regional Water Resources Based on Improved Generalized Entropy Method
by Ming Zhang, Jinghong Zhou and Runjuan Zhou
Entropy 2018, 20(9), 715; https://doi.org/10.3390/e20090715 - 18 Sep 2018
Cited by 13 | Viewed by 3307
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 [...] Read more.
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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19 pages, 3812 KiB  
Article
Analysis of Solar Irradiation Time Series Complexity and Predictability by Combining Kolmogorov Measures and Hamming Distance for La Reunion (France)
by Dragutin T. Mihailović, Miloud Bessafi, Sara Marković, Ilija Arsenić, Slavica Malinović-Milićević, Patrick Jeanty, Mathieu Delsaut, Jean-Pierre Chabriat, Nusret Drešković and Anja Mihailović
Entropy 2018, 20(8), 570; https://doi.org/10.3390/e20080570 - 01 Aug 2018
Cited by 13 | Viewed by 4109
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 [...] Read more.
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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20 pages, 1785 KiB  
Article
Water Resources Carrying Capacity Evaluation and Diagnosis Based on Set Pair Analysis and Improved the Entropy Weight Method
by Yi Cui, Ping Feng, Juliang Jin and Li Liu
Entropy 2018, 20(5), 359; https://doi.org/10.3390/e20050359 - 11 May 2018
Cited by 111 | Viewed by 6010
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 [...] Read more.
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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854 KiB  
Article
On the Statistical Mechanics of Alien Species Distribution
by Michael G. Bowler and Colleen K. Kelly
Entropy 2017, 19(12), 674; https://doi.org/10.3390/e19120674 - 09 Dec 2017
Cited by 2 | Viewed by 3769
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 [...] Read more.
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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