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  1. Book ; Online: Entropy Applications in Environmental and Water Engineering

    Singh, Vijay P. / Sivakumar, Bellie / Cui, Huijuan

    2019  

    Abstract: 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 ... ...

    Abstract 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
    Keywords Environmental engineering ; Engineering (General). Civil engineering (General) ; Technology (General)
    Size 1 electronic resource (512 p.)
    Publisher MDPI - Multidisciplinary Digital Publishing Institute
    Document type Book ; Online
    Note eng ; Open Access
    HBZ-ID HT020102474
    ISBN 9783038972228 ; 3038972223
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  2. Article ; Online: A Nonlinear Local Approximation Approach for Catchment Classification.

    Khan, Shakera K / Sivakumar, Bellie

    Entropy (Basel, Switzerland)

    2024  Volume 26, Issue 3

    Abstract: Catchment classification plays an important role in many applications associated with water resources and environment. In recent years, several studies have applied the concepts of nonlinear dynamics and chaos for catchment classification, mainly using ... ...

    Abstract Catchment classification plays an important role in many applications associated with water resources and environment. In recent years, several studies have applied the concepts of nonlinear dynamics and chaos for catchment classification, mainly using dimensionality measures. The present study explores prediction as a measure for catchment classification, through application of a nonlinear local approximation prediction method. The method uses the concept of phase-space reconstruction of a time series to represent the underlying system dynamics and identifies nearest neighbors in the phase space for system evolution and prediction. The prediction accuracy measures, as well as the optimum values of the parameters involved in the method (e.g., phase space or embedding dimension, number of neighbors), are used for classification. For implementation, the method is applied to daily streamflow data from 218 catchments in Australia, and predictions are made for different embedding dimensions and number of neighbors. The prediction results suggest that phase-space reconstruction using streamflow alone can provide good predictions. The results also indicate that better predictions are achieved for lower embedding dimensions and smaller numbers of neighbors, suggesting possible low dimensionality of the streamflow dynamics. The classification results based on prediction accuracy are found to be useful for identification of regions/stations with higher predictability, which has important implications for interpolation or extrapolation of streamflow data.
    Language English
    Publishing date 2024-02-29
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2014734-X
    ISSN 1099-4300 ; 1099-4300
    ISSN (online) 1099-4300
    ISSN 1099-4300
    DOI 10.3390/e26030218
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: COVID-19 and water.

    Sivakumar, Bellie

    Stochastic environmental research and risk assessment : research journal

    2020  Volume 35, Issue 3, Page(s) 531–534

    Abstract: The 2019 coronavirus disease, called COVID-19, is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Since it was first identified in China in December 2019, COVID-19 has spread to almost all countries and territories and caused over ...

    Abstract The 2019 coronavirus disease, called COVID-19, is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Since it was first identified in China in December 2019, COVID-19 has spread to almost all countries and territories and caused over 310,000 deaths, as on May 16, 2020. The impacts of the COVID-19 pandemic are now seen in almost every sector of our society. In this article, I discuss the impacts of COVID-19 on the water sector. I point out that our efforts to control the spread of COVID-19 will increase the water demand and worsen the water quality, leading to additional challenges in water planning and management. In view of the impacts of COVID-19 and other global-scale phenomena influencing water resources (e.g., global climate change), I highlight the urgent need for interdisciplinary collaborations among researchers studying water and new strategies to address water issues.
    Keywords covid19
    Language English
    Publishing date 2020-07-09
    Publishing country Germany
    Document type Editorial
    ZDB-ID 1481263-0
    ISSN 1436-3259 ; 1435-151X ; 1436-3240 ; 0931-1955
    ISSN (online) 1436-3259 ; 1435-151X
    ISSN 1436-3240 ; 0931-1955
    DOI 10.1007/s00477-020-01837-6
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: COVID-19 and water

    Sivakumar, Bellie

    Stochastic Environmental Research and Risk Assessment ; ISSN 1436-3240 1436-3259

    2020  

    Keywords Environmental Engineering ; General Environmental Science ; Safety, Risk, Reliability and Quality ; Water Science and Technology ; Environmental Chemistry ; covid19
    Language English
    Publisher Springer Science and Business Media LLC
    Publishing country us
    Document type Article ; Online
    DOI 10.1007/s00477-020-01837-6
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: Complexity of COVID-19 Dynamics

    Bellie Sivakumar / Bhadran Deepthi

    Entropy, Vol 24, Iss 50, p

    2022  Volume 50

    Abstract: With population explosion and globalization, the spread of infectious diseases has been a major concern. In 2019, a newly identified type of Coronavirus caused an outbreak of respiratory illness, popularly known as COVID-19, and became a pandemic. ... ...

    Abstract With population explosion and globalization, the spread of infectious diseases has been a major concern. In 2019, a newly identified type of Coronavirus caused an outbreak of respiratory illness, popularly known as COVID-19, and became a pandemic. Although enormous efforts have been made to understand the spread of COVID-19, our knowledge of the COVID-19 dynamics still remains limited. The present study employs the concepts of chaos theory to examine the temporal dynamic complexity of COVID-19 around the world. The false nearest neighbor (FNN) method is applied to determine the dimensionality and, hence, the complexity of the COVID-19 dynamics. The methodology involves: (1) reconstruction of a single-variable COVID-19 time series in a multi-dimensional phase space to represent the underlying dynamics; and (2) identification of “false” neighbors in the reconstructed phase space and estimation of the dimension of the COVID-19 series. For implementation, COVID-19 data from 40 countries/regions around the world are studied. Two types of COVID-19 data are analyzed: (1) daily COVID-19 cases; and (2) daily COVID-19 deaths. The results for the 40 countries/regions indicate that: (1) the dynamics of COVID-19 cases exhibit low- to medium-level complexity, with dimensionality in the range 3 to 7; and (2) the dynamics of COVID-19 deaths exhibit complexity anywhere from low to high, with dimensionality ranging from 3 to 13. The results also suggest that the complexity of the dynamics of COVID-19 deaths is greater than or at least equal to that of the dynamics of COVID-19 cases for most (three-fourths) of the countries/regions. These results have important implications for modeling and predicting the spread of COVID-19 (and other infectious diseases), especially in the identification of the appropriate complexity of models.
    Keywords infectious diseases ; coronavirus ; COVID-19 ; nonlinear dynamics ; chaos theory ; phase space reconstruction ; Science ; Q ; Astrophysics ; QB460-466 ; Physics ; QC1-999
    Subject code 330
    Language English
    Publishing date 2022-12-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article: COVID-19 and water

    Sivakumar, Bellie

    Stoch Environ Res Risk Assess

    Abstract: The 2019 coronavirus disease, called COVID-19, is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Since it was first identified in China in December 2019, COVID-19 has spread to almost all countries and territories and caused over ...

    Abstract The 2019 coronavirus disease, called COVID-19, is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Since it was first identified in China in December 2019, COVID-19 has spread to almost all countries and territories and caused over 310,000 deaths, as on May 16, 2020. The impacts of the COVID-19 pandemic are now seen in almost every sector of our society. In this article, I discuss the impacts of COVID-19 on the water sector. I point out that our efforts to control the spread of COVID-19 will increase the water demand and worsen the water quality, leading to additional challenges in water planning and management. In view of the impacts of COVID-19 and other global-scale phenomena influencing water resources (e.g., global climate change), I highlight the urgent need for interdisciplinary collaborations among researchers studying water and new strategies to address water issues.
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #636437
    Database COVID19

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  7. Article ; Online: Community structure concept for catchment classification

    Siti Aisyah Tumiran / Bellie Sivakumar

    Ecological Indicators, Vol 124, Iss , Pp 107346- (2021)

    A modularity density-based edge betweenness (MDEB) method

    2021  

    Abstract: Catchment classification is useful for a variety of purposes in hydrologic, environmental, and ecosystem studies. In the context of classification, the concept of community structure, within the realm of complex networks, is particularly attractive and ... ...

    Abstract Catchment classification is useful for a variety of purposes in hydrologic, environmental, and ecosystem studies. In the context of classification, the concept of community structure, within the realm of complex networks, is particularly attractive and gaining attention in catchment classification studies. Among the many community structure methods, the edge betweenness (EB) method, which applies a hierarchical clustering concept, is one of the most widely used. The EB method, however, is susceptible to the issue of scale (or size) of the network, essentially due to the modularity function that is used to measure the strength of the community structure. To overcome this limitation, the present study proposes an improvement to the EB method. The proposed method, termed as the Modularity Density-based Edge Betweenness (MDEB) method, uses a modularity density function (or D value) by maximization, to obtain the best split of the network. The effectiveness of the MDEB method is evaluated through its application for catchment classification using streamflow data from two large networks: 218 stations from Australia and 639 stations from the United States (US). For each network, three different scenarios in network sizes are studied: (1) the entire network; (2) smaller network sizes based on 100 random realizations, with each realization having 100 and 300 stations for Australia and the US, respectively; and (3) smaller network sizes based on nine different drainage division regions in Australia and 18 different hydrologic units in the US. The classification outcomes from the MDEB method for these three scenarios are compared with those from the EB method. The results indicate that the MDEB method generally performs better than the EB method, for both Australia and the US. The superiority of the MDEB method is evaluated in terms of the number of communities identified and the number of stations that change communities when different network sizes are considered. The catchment communities are also interpreted in terms ...
    Keywords Catchment classification ; Streamflow ; Complex networks ; Community structure ; Edge betweenness method ; Modularity density function ; Ecology ; QH540-549.5
    Subject code 910
    Language English
    Publishing date 2021-05-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: Complexity of COVID-19 Dynamics.

    Sivakumar, Bellie / Deepthi, Bhadran

    Entropy (Basel, Switzerland)

    2021  Volume 24, Issue 1

    Abstract: With population explosion and globalization, the spread of infectious diseases has been a major concern. In 2019, a newly identified type of Coronavirus caused an outbreak of respiratory illness, popularly known as COVID-19, and became a pandemic. ... ...

    Abstract With population explosion and globalization, the spread of infectious diseases has been a major concern. In 2019, a newly identified type of Coronavirus caused an outbreak of respiratory illness, popularly known as COVID-19, and became a pandemic. Although enormous efforts have been made to understand the spread of COVID-19, our knowledge of the COVID-19 dynamics still remains limited. The present study employs the concepts of chaos theory to examine the temporal dynamic complexity of COVID-19 around the world. The false nearest neighbor (FNN) method is applied to determine the dimensionality and, hence, the complexity of the COVID-19 dynamics. The methodology involves: (1) reconstruction of a single-variable COVID-19 time series in a multi-dimensional phase space to represent the underlying dynamics; and (2) identification of "false" neighbors in the reconstructed phase space and estimation of the dimension of the COVID-19 series. For implementation, COVID-19 data from 40 countries/regions around the world are studied. Two types of COVID-19 data are analyzed: (1) daily COVID-19 cases; and (2) daily COVID-19 deaths. The results for the 40 countries/regions indicate that: (1) the dynamics of COVID-19 cases exhibit low- to medium-level complexity, with dimensionality in the range 3 to 7; and (2) the dynamics of COVID-19 deaths exhibit complexity anywhere from low to high, with dimensionality ranging from 3 to 13. The results also suggest that the complexity of the dynamics of COVID-19 deaths is greater than or at least equal to that of the dynamics of COVID-19 cases for most (three-fourths) of the countries/regions. These results have important implications for modeling and predicting the spread of COVID-19 (and other infectious diseases), especially in the identification of the appropriate complexity of models.
    Language English
    Publishing date 2021-12-27
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2014734-X
    ISSN 1099-4300 ; 1099-4300
    ISSN (online) 1099-4300
    ISSN 1099-4300
    DOI 10.3390/e24010050
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Transfer entropy coupled directed–weighted complex network analysis of rainfall dynamics

    Tongal, Hakan / Sivakumar, Bellie

    Stoch Environ Res Risk Assess. 2022 Mar., v. 36, no. 3 p.851-867

    2022  

    Abstract: Applications of complex networks-based concepts in hydrology are gaining momentum at the current time. One of the most critical limitations in such studies is the use of linear correlation between the nodes (e.g. rainfall stations) for some assumed ... ...

    Abstract Applications of complex networks-based concepts in hydrology are gaining momentum at the current time. One of the most critical limitations in such studies is the use of linear correlation between the nodes (e.g. rainfall stations) for some assumed threshold levels to identify possible relationships/links. In this regard, entropy theory can be useful to better identify the information flow between the nodes. This study demonstrates the concept of transfer entropy for the directed–weighted complex network to study rainfall dynamics, especially to establish the statistically significant information flow between the nodes. The methodology is applied to a rainfall network of 218 stations across Australia, and total monthly rainfall data observed over the period 1981–2006 are analysed. The nature of the network is studied by determining the in-clustering, out-clustering, and cyclic-clustering coefficients. The highest number of in-clustering values are obtained for the northern parts of Northern Territory and Queensland in addition to the eastern parts of Queensland and New South Wales. Further, the highest number of out-clustering values are also obtained for the northern parts of Northern Territory and Queensland. It can be concluded that while the stations in the northern parts of Australia affect other stations, they are also influenced by others in a reciprocal relationship as shown by the high cyclic-clustering values for these regions. The stations in Western Australia and Victoria have relatively lower in- and out-clustering values, indicating that these stations have lower tendency to make a cluster with other stations. However, while the stations in Western Australia have the lowest clustering coefficients, they have also the highest out-strength values among the stations. These stations constitute a low number of triangles (or groups) with other stations but are significantly influential over other stations, especially located in Victoria (in the southeast). Therefore, the proposed methodology can be useful for determining the tendencies of the nodes in a network to make a cluster with strong or weak relationships with other nodes.
    Keywords entropy ; hydrology ; meteorological data ; rain ; risk ; Northern Territory ; Queensland ; Western Australia
    Language English
    Dates of publication 2022-03
    Size p. 851-867.
    Publishing place Springer Berlin Heidelberg
    Document type Article ; Online
    ZDB-ID 1481263-0
    ISSN 1436-3259 ; 1435-151X ; 1436-3240 ; 0931-1955
    ISSN (online) 1436-3259 ; 1435-151X
    ISSN 1436-3240 ; 0931-1955
    DOI 10.1007/s00477-021-02091-0
    Database NAL-Catalogue (AGRICOLA)

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  10. Article: General circulation models for rainfall simulations: Performance assessment using complex networks

    Deepthi, B. / Sivakumar, Bellie

    Atmospheric research. 2022 Nov., v. 278

    2022  

    Abstract: Reliable assessment of the impact of climate change on hydrology in a region requires proper selection of General Circulation Models (GCMs). The present study introduces the concepts of complex networks for evaluating the performance of GCMs in ... ...

    Abstract Reliable assessment of the impact of climate change on hydrology in a region requires proper selection of General Circulation Models (GCMs). The present study introduces the concepts of complex networks for evaluating the performance of GCMs in simulating rainfall and selecting an ensemble of the best-performing GCMs. The performance of 49 GCMs from the Coupled Model Intercomparison Project phase 6 (CMIP6) in simulating monthly rainfall over India is assessed. The observed (interpolated and gridded rainfall provided by the India Meteorological Department) rainfall data and GCM-simulated rainfall data over the period 1961–2014 at a spatial resolution of 1° x 1° (a total of 288 grids) are studied. The GCMs are evaluated for two cases: (1) Case 1 – whole year rainfall; and (2) Case 2 – summer monsoon rainfall (June–September). The clustering coefficient of the rainfall network is used as a network measure to evaluate the ability of the GCMs in simulating rainfall. For rainfall network construction, each grid is considered as a network. The phase space reconstruction concept is used to reconstruct the single-variable rainfall time series in a multi-dimensional phase space to represent the rainfall dynamics. The optimal dimension for reconstruction is determined using the false nearest neighbor (FNN) algorithm. For network construction, each reconstructed vector is considered as a node, and the connections between them, identified using a distance threshold for the reconstructed vectors in the phase space, serve as the links. For each of the 288 grids, the GCMs are ranked based on the difference in the clustering coefficient between the observed and GCM-simulated rainfall networks. The group decision-making (GDM) approach is employed to identify the ensemble of the best-performing GCMs for the entire study area considering all the grids. The results suggest different models perform well for the whole-year rainfall and summer monsoon rainfall. For the whole-year rainfall, the models CMCC-CM2-HR4, GFDL-ESM4, CMCC-ESM2, EC-Earth3-AerChem, and CMCC-CM2-SR5 rank as the top five. For the summer monsoon rainfall, FIO-ESM-2-0, E3SM-1-1, CESM2-FV2, CMCC-CM2-HR4, and CMCC-CM2-SR5 perform the best. Considering the rank of each model for both the cases, the best-performing models are identified to be CMCC-CM2-HR4, CMCC-CM2-SR5, CMCC-ESM2, FIOESM-2-0, and E3SM-1-1. The rainfall simulated from these models also show close resemblance to the observed rainfall, especially in terms of monthly mean rainfall. Therefore, even among the many GCMs that show good agreement with the monthly mean observed rainfall, the clustering coefficient-based analysis helps to narrow down the models for the ensemble of best-performing GCMs.
    Keywords algorithms ; climate change ; decision making ; hydrology ; meteorological data ; monsoon season ; rain ; research ; summer ; time series analysis ; India
    Language English
    Dates of publication 2022-11
    Publishing place Elsevier B.V.
    Document type Article
    ISSN 0169-8095
    DOI 10.1016/j.atmosres.2022.106333
    Database NAL-Catalogue (AGRICOLA)

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