Publication:
Development of cyanobacteria prediction models and mitigation countermeasures

dc.contributor.advisor Sharma, Ashish en_US
dc.contributor.advisor Mehrotra, Rajeshwar en_US
dc.contributor.advisor Kim, Seokyeon en_US
dc.contributor.author Kim, Seungbeom en_US
dc.date.accessioned 2022-03-15T08:35:37Z
dc.date.available 2022-03-15T08:35:37Z
dc.date.issued 2020 en_US
dc.description.abstract Cyanobacteria, also referred to as blue-green algae, are aquatic and photosynthetic, that is, that live primarily in fresh water and salt water. In addition to unattractive colour and smelly odour, abundance of cyanobacteria worsens water-quality and generates toxins that can harm humans and animals alike. Lack of enough data, its independence across multiple sampling time steps, as well as the presence of more than one causative factors, each with different levels of influence on the response, has resulted in limited progress in the development of generalized Cyanobacteria modelling and prediction frameworks. In this thesis, using a few key dominant factors, relatively practical and universally applicable two models for predicting the cyanobacterial bloom have been developed. The first model is a binary model and forecasts the occurrence/non-occurrence of cyanobacterial bloom at a given time step conditional on the dominant environmental variables and the cyanobacteria concentration at the preceding time step. The bacterial growth dynamic to the model is included by defining the weight functions which quantify the importance assigned to the key environmental variables namely, temperature, velocity and nutrients. A probabilistic model can yield a distribution of possible outcomes and therefore helps not only to understand the degree of outcome but also to make a relevant solution with uncertainty. Following this, a two-stage probabilistic model has been developed. In the first stage, cyanobacteria occurrences are generated using a first-order conditional Markov model. The conditioning vector includes the cyanobacterial count on the preceding time step and a few environmental variables. On occasions where the first stage model predicts the occurrence of cyanobacteria, the second stage model generates cyanobacterial cell counts using a nonparametric kernel density approach assuming first-order Markovian conditional dependence. As a final stage of this thesis, a few scenarios for controlling cyanobacterial bloom growth are assessed in terms of changes in the environmental variables and financial implications. Both developed models provide promising results and offer the capability of applying them to other areas. The suggested countermeasure provides an interesting and economically feasible solution to deal with cyanobacterial bloom issue en_US
dc.identifier.uri http://hdl.handle.net/1959.4/70164
dc.language English
dc.language.iso EN en_US
dc.publisher UNSW, Sydney en_US
dc.rights CC BY-NC-ND 3.0 en_US
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/3.0/au/ en_US
dc.subject.other Markov Model en_US
dc.subject.other Cyanobacteria en_US
dc.subject.other Probabilistic Modelling en_US
dc.title Development of cyanobacteria prediction models and mitigation countermeasures en_US
dc.type Thesis en_US
dcterms.accessRights open access
dcterms.rightsHolder Kim, Seungbeom
dspace.entity.type Publication en_US
unsw.accessRights.uri https://purl.org/coar/access_right/c_abf2
unsw.date.embargo 2022-03-18 en_US
unsw.description.embargoNote Embargoed until 2022-03-18
unsw.identifier.doi https://doi.org/10.26190/unsworks/2174
unsw.relation.faculty Engineering
unsw.relation.originalPublicationAffiliation Kim, Seungbeom, Civil & Environmental Engineering, Faculty of Engineering, UNSW en_US
unsw.relation.originalPublicationAffiliation Sharma, Ashish, Civil & Environmental Engineering, Faculty of Engineering, UNSW en_US
unsw.relation.originalPublicationAffiliation Mehrotra, Rajeshwar, Civil & Environmental Engineering, Faculty of Engineering, UNSW en_US
unsw.relation.originalPublicationAffiliation Kim, Seokyeon, Civil & Environmental Engineering, Faculty of Engineering, UNSW en_US
unsw.relation.school School of Civil and Environmental Engineering *
unsw.thesis.degreetype Masters Thesis en_US
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