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Applying Bipartite Network Approach To Scarce Data: Modeling Habitat Suitability Of A Marine Mammal Species

Current study intends to formulate a habitat suitability model of a newly surveyed marine mammal species where the research scenario is characterized by real-world data that is scarce with no detail demographic value available. It is extremely challenging to solve it using either traditional statist...

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Main Authors: Liew, Chin Ying, Jane, Labadin, Wang, Yin Chai, Andrew, Alek Tuen, Cindy, Peter
Format: E-Article
Language:English
Published: Elsevier B. V 2015
Subjects:
T Technology (General)
Online Access:http://ir.unimas.my/id/eprint/10315/1/NO%2018%20Applying%20bipartite%20network%20approach%20to%20scarce%20dataes%20%28abstract%29.pdf
http://ir.unimas.my/id/eprint/10315/
http://www.sciencedirect.com/science/article/pii/S187705091502253X
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spelling my.unimas.ir.103152016-10-24T01:45:55Z http://ir.unimas.my/id/eprint/10315/ Applying Bipartite Network Approach To Scarce Data: Modeling Habitat Suitability Of A Marine Mammal Species Liew, Chin Ying Jane, Labadin Wang, Yin Chai Andrew, Alek Tuen Cindy, Peter T Technology (General) Current study intends to formulate a habitat suitability model of a newly surveyed marine mammal species where the research scenario is characterized by real-world data that is scarce with no detail demographic value available. It is extremely challenging to solve it using either traditional statistical approaches where huge amount of data are required or deterministic approaches that commonly employ partial differential equations (PDE) model which are based strongly on well-established physical laws and entail detail species-specific demographic values. Conversely, the graph-theoretic based bipartite network modeling (BNM) approach is not bound by the above limitations and is thus employed in this study. The result produced is a bipartite habitat suitability network model consisting thirteen location nodes and thirteen species nodes, each with their respective parameters of which some are quantified through a machine learning algorithm, and thirty-eight weighted edges that are quantified through multiplication rule. Habitat suitability index, generated through implementation of an adapted web-based search algorithm, is produced and utilized for the ranking of these location nodes. The ranking result obtained is in good agreement with the past literature. The results produced also provide pertinent input to the related practitioners for the conservation of the species and preservation of the habitat and environment ecology. Elsevier B. V 2015 E-Article PeerReviewed text en http://ir.unimas.my/id/eprint/10315/1/NO%2018%20Applying%20bipartite%20network%20approach%20to%20scarce%20dataes%20%28abstract%29.pdf Liew, Chin Ying and Jane, Labadin and Wang, Yin Chai and Andrew, Alek Tuen and Cindy, Peter (2015) Applying Bipartite Network Approach To Scarce Data: Modeling Habitat Suitability Of A Marine Mammal Species. Procedia Computer Science, 60 (1). pp. 266-275. ISSN 1877-0509 http://www.sciencedirect.com/science/article/pii/S187705091502253X doi:10.1016/j.procs.2015.08.126
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Liew, Chin Ying
Jane, Labadin
Wang, Yin Chai
Andrew, Alek Tuen
Cindy, Peter
Applying Bipartite Network Approach To Scarce Data: Modeling Habitat Suitability Of A Marine Mammal Species
description Current study intends to formulate a habitat suitability model of a newly surveyed marine mammal species where the research scenario is characterized by real-world data that is scarce with no detail demographic value available. It is extremely challenging to solve it using either traditional statistical approaches where huge amount of data are required or deterministic approaches that commonly employ partial differential equations (PDE) model which are based strongly on well-established physical laws and entail detail species-specific demographic values. Conversely, the graph-theoretic based bipartite network modeling (BNM) approach is not bound by the above limitations and is thus employed in this study. The result produced is a bipartite habitat suitability network model consisting thirteen location nodes and thirteen species nodes, each with their respective parameters of which some are quantified through a machine learning algorithm, and thirty-eight weighted edges that are quantified through multiplication rule. Habitat suitability index, generated through implementation of an adapted web-based search algorithm, is produced and utilized for the ranking of these location nodes. The ranking result obtained is in good agreement with the past literature. The results produced also provide pertinent input to the related practitioners for the conservation of the species and preservation of the habitat and environment ecology.
format E-Article
author Liew, Chin Ying
Jane, Labadin
Wang, Yin Chai
Andrew, Alek Tuen
Cindy, Peter
author_facet Liew, Chin Ying
Jane, Labadin
Wang, Yin Chai
Andrew, Alek Tuen
Cindy, Peter
author_sort Liew, Chin Ying
title Applying Bipartite Network Approach To Scarce Data: Modeling Habitat Suitability Of A Marine Mammal Species
title_short Applying Bipartite Network Approach To Scarce Data: Modeling Habitat Suitability Of A Marine Mammal Species
title_full Applying Bipartite Network Approach To Scarce Data: Modeling Habitat Suitability Of A Marine Mammal Species
title_fullStr Applying Bipartite Network Approach To Scarce Data: Modeling Habitat Suitability Of A Marine Mammal Species
title_full_unstemmed Applying Bipartite Network Approach To Scarce Data: Modeling Habitat Suitability Of A Marine Mammal Species
title_sort applying bipartite network approach to scarce data: modeling habitat suitability of a marine mammal species
publisher Elsevier B. V
publishDate 2015
url http://ir.unimas.my/id/eprint/10315/1/NO%2018%20Applying%20bipartite%20network%20approach%20to%20scarce%20dataes%20%28abstract%29.pdf
http://ir.unimas.my/id/eprint/10315/
http://www.sciencedirect.com/science/article/pii/S187705091502253X
_version_ 1644510930164252672
score 13.211869

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