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Special Issue "Entropy Applications in Environmental and Water Engineering II"

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

Deadline for manuscript submissions: 31 October 2019

Special Issue Editors

Guest Editor
Prof. Dr. Bellie Sivakumar

School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW 2052, Australia
Website | E-Mail
Phone: +61-2-93855072
Fax: +61 2 9385 6139
Interests: hydrology; water resources engineering; climate change impacts; complexity; nonlinear dynamics; chaos; fractals; complex networks
Guest Editor
Prof. Vijay P. Singh

Department of Biological and Agricultural Engineering & Zachry Department of Civil Engineering, Texas A and M University, College Station, Texas 77843-2117, USA
E-Mail
Phone: +1-979-845-7028
Fax: +1-979-862-3442
Interests: Hydrology; Water resources engineering; Water quality modeling; Environmental management; Climate change impacts; Entropy-based modeling; Copula-based modeling
Guest Editor
Dr. Huijuan Cui

Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
E-Mail
Phone: +86‐10‐64888895
Fax: +86-10-64872274
Interests: Streamflow forecasting; Hydrological time series analysis; Entropy-based modeling; Velocity distribution; Sediment discharge

Special Issue Information

Dear Colleagues,

Environmental and water resource systems are complex nonlinear dynamic systems, often exhibiting significant spatial and temporal variability in their dynamics. Unraveling the variability of these systems and the associated phenomena has always been a tremendous challenge. The original entropy theory in thermodynamics is useful to determine the dynamics of these systems, while entropy in information theory offers a reliable means to study their variability and disorder. The past few decades have witnessed numerous applications of entropy theory for studying a wide range of problems encountered in the field 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, ecosystem modeling, water distribution networks, environmental and water resource management, and parameter estimation. Such studies have also used several different entropy formulations, such as the Shannon, Tsallis, Rényi, Burg, Kolmogorov, Kapur, configurational, and relative entropies, which can be derived in the time, space or frequency domain. More recently, entropy-based concepts have been coupled with several other popular theories, including copulas, wavelets, chaos, fuzzy logic, support vector machines, and ensemble filters. The outcomes of such studies clearly indicate the enormous potential of entropy theory in various areas of environmental and water engineering.

Following up on the enormous success of the first volume of the Special Issue on “Entropy Applications in Environmental and Water Engineering”, this second volume aims to compile key current research on the applications of entropy or information theory in environmental and water engineering. Manuscripts that address any and all aspects associated with entropy theory applications in environmental and water engineering are welcome. Manuscripts attempting the integration of entropy theory with other concepts and addressing the role of entropy theory in interdisciplinary research in environmental and water engineering, and even beyond in the broader field of geosciences, are particularly encouraged.

Prof. Vijay P. Singh
Prof. Bellie Sivakumar
Dr. Huijuan Cui
Guest Editors

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

  • Entropy theory
  • Complex systems
  • Environmental engineering
  • Water engineering
  • Hydraulics
  • Hydrology
  • Geomorphology

Published Papers (8 papers)

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Research

Open AccessArticle
Quantitative Assessment and Diagnosis for Regional Agricultural Drought Resilience Based on Set Pair Analysis and Connection Entropy
Entropy 2019, 21(4), 373; https://doi.org/10.3390/e21040373
Received: 27 February 2019 / Revised: 26 March 2019 / Accepted: 2 April 2019 / Published: 5 April 2019
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Abstract
Assessment and diagnosis of regional agricultural drought resilience (RADR) is an important groundwork to identify the shortcomings of regional agriculture to resist drought disasters accurately. In order to quantitatively assess the capacity of regional agriculture system to reduce losses from drought disasters under [...] Read more.
Assessment and diagnosis of regional agricultural drought resilience (RADR) is an important groundwork to identify the shortcomings of regional agriculture to resist drought disasters accurately. In order to quantitatively assess the capacity of regional agriculture system to reduce losses from drought disasters under complex conditions and to identify vulnerability indexes, an assessment and diagnosis model for RADR was established. Firstly, this model used the improved fuzzy analytic hierarchy process to determine the index weights, then proposed an assessment method based on connection number and an improved connection entropy. Furthermore, the set pair potential based on subtraction was used to diagnose the vulnerability indexes. In addition, a practical application had been carried out in the region of the Huaibei Plain in Anhui Province. The evaluation results showed that the RADR in this area from 2005 to 2014 as a whole was in a relatively weak situation. However, the average grade values had decreased from 3.144 to 2.790 during these 10 years and the RADR had an enhanced tendency. Moreover, the possibility of RADR enhancement for six cities in this region decreased from east to west, and the drought emergency condition was the weak link of the RADR in the Huaibei Plain. Full article
(This article belongs to the Special Issue Entropy Applications in Environmental and Water Engineering II)
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Open AccessArticle
The Influence of Different Knowledge-Driven Methods on Landslide Susceptibility Mapping: A Case Study in the Changbai Mountain Area, Northeast China
Entropy 2019, 21(4), 372; https://doi.org/10.3390/e21040372
Received: 1 March 2019 / Revised: 28 March 2019 / Accepted: 2 April 2019 / Published: 5 April 2019
Cited by 1 | PDF Full-text (15865 KB) | HTML Full-text | XML Full-text
Abstract
Landslides are one of the most frequent geomorphic hazards, and they often result in the loss of property and human life in the Changbai Mountain area (CMA), Northeast China. The objective of this study was to produce and compare landslide susceptibility maps for [...] Read more.
Landslides are one of the most frequent geomorphic hazards, and they often result in the loss of property and human life in the Changbai Mountain area (CMA), Northeast China. The objective of this study was to produce and compare landslide susceptibility maps for the CMA using an information content model (ICM) with three knowledge-driven methods (the artificial hierarchy process with the ICM (AHP-ICM), the entropy weight method with the ICM (EWM-ICM), and the rough set with the ICM (RS-ICM)) and to explore the influence of different knowledge-driven methods for a series of parameters on the accuracy of landslide susceptibility mapping (LSM). In this research, the landslide inventory data (145 landslides) were randomly divided into a training dataset: 70% (81 landslides) were used for training the models and 30% (35 landslides) were used for validation. In addition, 13 layers of landslide conditioning factors, namely, altitude, slope gradient, slope aspect, lithology, distance to faults, distance to roads, distance to rivers, annual precipitation, land type, normalized difference vegetation index (NDVI), topographic wetness index (TWI), plan curvature, and profile curvature, were taken as independent, causal predictors. Landslide susceptibility maps were developed using the ICM, RS-ICM, AHP-ICM, and EWM-ICM, in which weights were assigned to every conditioning factor. The resultant susceptibility was validated using the area under the ROC curve (AUC) method. The success accuracies of the landslide susceptibility maps produced by the ICM, RS-ICM, AHP-ICM, and EWM-ICM methods were 0.931, 0.939, 0.912, and 0.883, respectively, with prediction accuracy rates of 0.926, 0.927, 0.917, and 0.878 for the ICM, RS-ICM, AHP-ICM, and EWM-ICM, respectively. Hence, it can be concluded that the four models used in this study gave close results, with the RS-ICM exhibiting the best performance in landslide susceptibility mapping. Full article
(This article belongs to the Special Issue Entropy Applications in Environmental and Water Engineering II)
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Open AccessArticle
Agricultural Water Resources Management Using Maximum Entropy and Entropy-Weight-Based TOPSIS Methods
Entropy 2019, 21(4), 364; https://doi.org/10.3390/e21040364
Received: 1 March 2019 / Revised: 24 March 2019 / Accepted: 3 April 2019 / Published: 4 April 2019
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Abstract
Allocation and management of agricultural water resources is an emerging concern due to diminishing water supplies and increasing water demands. To achieve economic, social, and environmental goals in a specific irrigation district, decisions should be made subject to the changing water supply and [...] Read more.
Allocation and management of agricultural water resources is an emerging concern due to diminishing water supplies and increasing water demands. To achieve economic, social, and environmental goals in a specific irrigation district, decisions should be made subject to the changing water supply and water demand—the two critical random parameters in agricultural water resources management. This paper presents the foundations of a systematic framework for agricultural water resources management, including determination of distribution functions, joint probability of water supply and water demand, optimal allocation of agricultural water resources, and evaluation of various schemes according to agricultural water resources carrying capacity. The maximum entropy method is used to estimate parameters of probability distributions of water supply and demand, which is the basic for the other parts of the framework. The entropy-weight-based TOPSIS method is applied to evaluate agricultural water resources allocation schemes, because it avoids the subjectivity of weight determination and reflects the dynamic changing trend of agricultural water resources carrying capacity. A case study using an irrigation district in Northeast China is used to demonstrate the feasibility and applicability of the framework. It is found that the framework works effectively to balance multiple objectives and provides alternative schemes, considering the combinatorial variety of water supply and water demand, which are conducive to agricultural water resources planning. Full article
(This article belongs to the Special Issue Entropy Applications in Environmental and Water Engineering II)
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Open AccessArticle
Application of Entropy Spectral Method for Streamflow Forecasting in Northwest China
Entropy 2019, 21(2), 132; https://doi.org/10.3390/e21020132
Received: 5 November 2018 / Revised: 20 January 2019 / Accepted: 21 January 2019 / Published: 1 February 2019
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Abstract
Streamflow forecasting is vital for reservoir operation, flood control, power generation, river ecological restoration, irrigation and navigation. Although monthly streamflow time series are statistic, they also exhibit seasonal and periodic patterns. Using maximum Burg entropy, maximum configurational entropy and minimum relative entropy, the [...] Read more.
Streamflow forecasting is vital for reservoir operation, flood control, power generation, river ecological restoration, irrigation and navigation. Although monthly streamflow time series are statistic, they also exhibit seasonal and periodic patterns. Using maximum Burg entropy, maximum configurational entropy and minimum relative entropy, the forecasting models for monthly streamflow series were constructed for five hydrological stations in northwest China. The evaluation criteria of average relative error (RE), root mean square error (RMSE), correlation coefficient (R) and determination coefficient (DC) were selected as performance metrics. Results indicated that the RESA model had the highest forecasting accuracy, followed by the CESA model. However, the BESA model had the highest forecasting accuracy in a low-flow period, and the prediction accuracies of RESA and CESA models in the flood season were relatively higher. In future research, these entropy spectral analysis methods can further be applied to other rivers to verify the applicability in the forecasting of monthly streamflow in China. Full article
(This article belongs to the Special Issue Entropy Applications in Environmental and Water Engineering II)
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Open AccessArticle
Estimating the Bed-Load Layer Thickness in Open Channels by Tsallis Entropy
Entropy 2019, 21(2), 123; https://doi.org/10.3390/e21020123
Received: 20 December 2018 / Revised: 23 January 2019 / Accepted: 28 January 2019 / Published: 29 January 2019
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Abstract
In the research field of river dynamics, the thickness of bed-load is an important parameter in determining sediment discharge in open channels. Some studies have estimated the bed-load thickness from theoretical and/or experimental perspectives. This study attempts to propose the mathematical formula for [...] Read more.
In the research field of river dynamics, the thickness of bed-load is an important parameter in determining sediment discharge in open channels. Some studies have estimated the bed-load thickness from theoretical and/or experimental perspectives. This study attempts to propose the mathematical formula for the bed-load thickness by using the Tsallis entropy theory. Assuming the bed-load thickness is a random variable and using the method for the maximization of the entropy function, the present study derives an explicit expression for the thickness of the bed-load layer as a function with non-dimensional shear stress, by adopting a hypothesis regarding the cumulative distribution function of the bed-load thickness. This expression is verified against six experimental datasets and are also compared with existing deterministic models and the Shannon entropy-based expression. It has been found that there is good agreement between the derived expression and the experimental data, and the derived expression has a better fitting accuracy than some existing deterministic models. It has been also found that the derived Tsallis entropy-based expression has a comparable prediction ability for experimental data to the Shannon entropy-based expression. Finally, the impacts of the mass density of the particle and particle diameter on the bed-load thickness in open channels are also discussed based on this derived expression. Full article
(This article belongs to the Special Issue Entropy Applications in Environmental and Water Engineering II)
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Open AccessArticle
Estimation of Soil Depth Using Bayesian Maximum Entropy Method
Entropy 2019, 21(1), 69; https://doi.org/10.3390/e21010069
Received: 6 November 2018 / Revised: 4 January 2019 / Accepted: 11 January 2019 / Published: 15 January 2019
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Abstract
Soil depth plays an important role in landslide disaster prevention and is a key factor in slopeland development and management. Existing soil depth maps are outdated and incomplete in Taiwan. There is a need to improve the accuracy of the map. The Kriging [...] Read more.
Soil depth plays an important role in landslide disaster prevention and is a key factor in slopeland development and management. Existing soil depth maps are outdated and incomplete in Taiwan. There is a need to improve the accuracy of the map. The Kriging method, one of the most frequently adopted estimation approaches for soil depth, has room for accuracy improvements. An appropriate soil depth estimation method is proposed, in which soil depth is estimated using Bayesian Maximum Entropy method (BME) considering space distribution of measured soil depth and impact of physiographic factors. BME divides analysis data into groups of deterministic and probabilistic data. The deterministic part are soil depth measurements in a given area and the probabilistic part contains soil depth estimated by a machine learning-based soil depth estimation model based on physiographic factors including slope, aspect, profile curvature, plan curvature, and topographic wetness index. Accuracy of estimates calculated by soil depth grading, very shallow (<20 cm), shallow (20–50 cm), deep (50–90 cm), and very deep (>90 cm), suggests that BME is superior to the Kriging method with estimation accuracy up to 82.94%. The soil depth distribution map of Hsinchu, Taiwan made by BME with a soil depth error of ±5.62 cm provides a promising outcome which is useful in future applications, especially for locations without soil depth data. Full article
(This article belongs to the Special Issue Entropy Applications in Environmental and Water Engineering II)
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Open AccessArticle
Precipitation Complexity and its Spatial Difference in the Taihu Lake Basin, China
Entropy 2019, 21(1), 48; https://doi.org/10.3390/e21010048
Received: 25 October 2018 / Revised: 25 December 2018 / Accepted: 8 January 2019 / Published: 10 January 2019
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Abstract
Due to the rapid urbanization development, the precipitation variability in the Taihu Lake basin (TLB) in East China has become highly complex over the last decades. However, there is limited understanding of the spatiotemporal variability of precipitation complexity and its relationship with the [...] Read more.
Due to the rapid urbanization development, the precipitation variability in the Taihu Lake basin (TLB) in East China has become highly complex over the last decades. However, there is limited understanding of the spatiotemporal variability of precipitation complexity and its relationship with the urbanization development in the region. In this article, by considering the whole urbanization process, we use the SampEn index to investigate the precipitation complexity and its spatial differences in different urbanization areas (old urban area, new urban area and suburbs) in TLB. Results indicate that the precipitation complexity and its changes accord well with the urbanization development process in TLB. Higher urbanization degrees correspond to greater complexity degrees of precipitation. Precipitation in old urban areas shows the greatest complexity compared with that in new urban areas and suburbs, not only for the entire precipitation process but also the precipitation extremes. There is a significant negative correlation between the annual precipitation and its SampEn value, and the same change of precipitation can cause a greater complexity change in old urbanization areas compared with the new urban areas and suburbs. It is noted that the enhanced precipitation complexity in a new urban area during recent decades cannot be ignored facing the expanding urbanization. Full article
(This article belongs to the Special Issue Entropy Applications in Environmental and Water Engineering II)
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Open AccessArticle
Noise Reduction Method of Underwater Acoustic Signals Based on CEEMDAN, Effort-To-Compress Complexity, Refined Composite Multiscale Dispersion Entropy and Wavelet Threshold Denoising
Entropy 2019, 21(1), 11; https://doi.org/10.3390/e21010011
Received: 28 November 2018 / Revised: 19 December 2018 / Accepted: 20 December 2018 / Published: 24 December 2018
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Abstract
Owing to the problems that imperfect decomposition process of empirical mode decomposition (EMD) denoising algorithm and poor self-adaptability, it will be extremely difficult to reduce the noise of signal. In this paper, a noise reduction method of underwater acoustic signal denoising based on [...] Read more.
Owing to the problems that imperfect decomposition process of empirical mode decomposition (EMD) denoising algorithm and poor self-adaptability, it will be extremely difficult to reduce the noise of signal. In this paper, a noise reduction method of underwater acoustic signal denoising based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), effort-to-compress complexity (ETC), refined composite multiscale dispersion entropy (RCMDE) and wavelet threshold denoising is proposed. Firstly, the original signal is decomposed into several IMFs by CEEMDAN and noise IMFs can be identified according to the ETC of IMFs. Then, calculating the RCMDE of remaining IMFs, these IMFs are divided into three kinds of IMFs by RCMDE, namely noise-dominant IMFs, real signal-dominant IMFs, real IMFs. Finally, noise IMFs are removed, wavelet soft threshold denoising is applied to noise-dominant IMFs and real signal-dominant IMFs. The denoised signal can be obtained by combining the real IMFs with the denoised IMFs after wavelet soft threshold denoising. Chaotic signals with different signal-to-noise ratio (SNR) are used for denoising experiments by comparing with EMD_MSE_WSTD and EEMD_DE_WSTD, it shows that the proposed algorithm has higher SNR and smaller root mean square error (RMSE). In order to further verify the effectiveness of the proposed method, which is applied to noise reduction of real underwater acoustic signals. The results show that the denoised underwater acoustic signals not only eliminate noise interference also restore the topological structure of the chaotic attractors more clearly, which lays a foundation for the further processing of underwater acoustic signals. Full article
(This article belongs to the Special Issue Entropy Applications in Environmental and Water Engineering II)
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