Publications by authors named "Bellie Sivakumar"

6 Publications

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COVID-19 and water.

Authors:
Bellie Sivakumar

Stoch Environ Res Risk Assess 2020 Jul 9:1-4. Epub 2020 Jul 9.

Department of Civil Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, 400 076 India.

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.
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http://dx.doi.org/10.1007/s00477-020-01837-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7346856PMC
July 2020

Continuous monitoring of suspended sediment concentrations using image analytics and deriving inherent correlations by machine learning.

Sci Rep 2020 05 22;10(1):8589. Epub 2020 May 22.

Department of Civil Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran.

The barriers for the development of continuous monitoring of Suspended Sediment Concentration (SSC) in channels/rivers include costs and technological gaps but this paper shows that a solution is feasible by: (i) using readily available high-resolution images; (ii) transforming the images into image analytics to form a modelling dataset; and (iii) constructing predictive models by learning inherent correlation between observed SSC values and their image analytics. High-resolution images were taken of water containing a series of SSC values using an exploratory flume. Machine learning is processed by dividing the dataset into training and testing sets and the paper uses the following models: Generalized Linear Machine (GLM) and Distributed Random Forest (DRF). Results show that each model is capable of reliable predictions but the errors at higher SSC are not fully explained by modelling alone. Here we offer sufficient evidence for the feasibility of a continuous SSC monitoring capability in channels before the next phase of the study with the goal of producing practice guidelines.
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http://dx.doi.org/10.1038/s41598-020-64707-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7244478PMC
May 2020

Entropy Applications in Environmental and Water Engineering.

Entropy (Basel) 2018 Aug 10;20(8). Epub 2018 Aug 10.

Department of Biological and Agricultural Engineering & Zachry Department of Civil Engineering, Texas A and M University, College Station, TX 77843-2117, USA.

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http://dx.doi.org/10.3390/e20080598DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7513122PMC
August 2018

Quantitative design of emergency monitoring network for river chemical spills based on discrete entropy theory.

Water Res 2018 05 3;134:140-152. Epub 2018 Feb 3.

School of Environment, Harbin Institute of Technology, Harbin 150090, China; State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China. Electronic address:

Field monitoring strategy is critical for disaster preparedness and watershed emergency environmental management. However, development of such is also highly challenging. Despite the efforts and progress thus far, no definitive guidelines or solutions are available worldwide for quantitatively designing a monitoring network in response to river chemical spill incidents, except general rules based on administrative divisions or arbitrary interpolation on routine monitoring sections. To address this gap, a novel framework for spatial-temporal network design was proposed in this study. The framework combines contaminant transport modelling with discrete entropy theory and spectral analysis. The water quality model was applied to forecast the spatio-temporal distribution of contaminant after spills and then corresponding information transfer indexes (ITIs) and Fourier approximation periodic functions were estimated as critical measures for setting sampling locations and times. The results indicate that the framework can produce scientific preparedness plans of emergency monitoring based on scenario analysis of spill risks as well as rapid design as soon as the incident happened but not prepared. The framework was applied to a hypothetical spill case based on tracer experiment and a real nitrobenzene spill incident case to demonstrate its suitability and effectiveness. The newly-designed temporal-spatial monitoring network captured major pollution information at relatively low costs. It showed obvious benefits for follow-up early-warning and treatment as well as for aftermath recovery and assessment. The underlying drivers of ITIs as well as the limitations and uncertainty of the approach were analyzed based on the case studies. Comparison with existing monitoring network design approaches, management implications, and generalized applicability were also discussed.
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http://dx.doi.org/10.1016/j.watres.2018.01.057DOI Listing
May 2018

Symplectic geometry spectrum regression for prediction of noisy time series.

Phys Rev E 2016 May 20;93(5):052217. Epub 2016 May 20.

ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane QLD 4000, Australia.

We present the symplectic geometry spectrum regression (SGSR) technique as well as a regularized method based on SGSR for prediction of nonlinear time series. The main tool of analysis is the symplectic geometry spectrum analysis, which decomposes a time series into the sum of a small number of independent and interpretable components. The key to successful regularization is to damp higher order symplectic geometry spectrum components. The effectiveness of SGSR and its superiority over local approximation using ordinary least squares are demonstrated through prediction of two noisy synthetic chaotic time series (Lorenz and Rössler series), and then tested for prediction of three real-world data sets (Mississippi River flow data and electromyographic and mechanomyographic signal recorded from human body).
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http://dx.doi.org/10.1103/PhysRevE.93.052217DOI Listing
May 2016

A global assessment of climate-water quality relationships in large rivers: an elasticity perspective.

Sci Total Environ 2014 Jan 28;468-469:877-91. Epub 2013 Sep 28.

School of Municipal and Environmental Engineering, Harbin Institute of Technology, Harbin 150090, China; School of Civil and Environmental Engineering, The University of New South Wales, Sydney, NSW 2052, Australia. Electronic address:

To uncover climate-water quality relationships in large rivers on a global scale, the present study investigates the climate elasticity of river water quality (CEWQ) using long-term monthly records observed at 14 large rivers. Temperature and precipitation elasticities of 12 water quality parameters, highlighted by N- and P-nutrients, are assessed. General observations on elasticity values show the usefulness of this approach to describe the magnitude of stream water quality responses to climate change, which improves that of simple statistical correlation. Sensitivity type, intensity and variability rank of CEWQ are reported and specific characteristics and mechanism of elasticity of nutrient parameters are also revealed. Among them, the performance of ammonia, total phosphorus-air temperature models, and nitrite, orthophosphorus-precipitation models are the best. Spatial and temporal assessment shows that precipitation elasticity is more variable in space than temperature elasticity and that seasonal variation is more evident for precipitation elasticity than for temperature elasticity. Moreover, both anthropogenic activities and environmental factors are found to impact CEWQ for select variables. The major relationships that can be inferred include: (1) human population has a strong linear correlation with temperature elasticity of turbidity and total phosphorus; and (2) latitude has a strong linear correlation with precipitation elasticity of turbidity and N nutrients. As this work improves our understanding of the relation between climate factors and surface water quality, it is potentially helpful for investigating the effect of climate change on water quality in large rivers, such as on the long-term change of nutrient concentrations.
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http://dx.doi.org/10.1016/j.scitotenv.2013.09.002DOI Listing
January 2014