Proposing novel ensemble approach of particle swarm optimized and machine learning algorithms for drought vulnerability mapping in Jharkhand, India

Saha, S; Gayen, A; Gogoi, P; Kundu, B; Paul, GC; Pradhan, B

Proposing novel ensemble approach of particle swarm optimized and machine learning algorithms for drought vulnerability mapping in Jharkhand, India

Saha, S Gayen, A Gogoi, P Kundu, B Paul, GC Pradhan, B

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Publisher:: TAYLOR & FRANCIS LTD
Publication Type:: Journal Article
Citation:: Geocarto International, 2022, 37, (25), pp. 8004-8035
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Saha, S
dc.contributor.author	Gayen, A
dc.contributor.author	Gogoi, P
dc.contributor.author	Kundu, B
dc.contributor.author	Paul, GC
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.date.accessioned	2023-04-06T00:46:52Z
dc.date.available	2023-04-06T00:46:52Z
dc.date.issued	2022-01-01
dc.identifier.citation	Geocarto International, 2022, 37, (25), pp. 8004-8035
dc.identifier.issn	1010-6049
dc.identifier.issn	1752-0762
dc.identifier.uri	http://hdl.handle.net/10453/169233
dc.description.abstract	Drought, a natural and very complex climatic hazard, causes impacts on natural and socio-economic environments. This study aims to produce the drought vulnerability map (DVM) considering novel ensemble machine learning algorithms (MLAs) in Jharkhand, India. Forty, drought vulnerability determining factors under the categories of exposure, sensitivity, and adaptive capacity were used. Then, four machine learning and four novel ensemble approaches of particle swarm optimized (PSO) algorithms, named random forest (RF), PSO-RF, multi-layer perceptron (MLP), PSO-MLP, support vector regression (SVM), PSO-MLP, Bagging, and PSO-Bagging, were established for DVMs. The receiver operating characteristic curve (ROC), mean-absolute-error (MAE), root-mean-square-error (RMSE), precision, and K-index were utilized for judging the performance of novel ensemble MLAs. The obtained results show that the PSO-RF had the highest performance with an AUC of 0.874, followed by RF, PSO-MLP, PSO-Bagging, Bagging, MLP, PSO-SVM and SVM, respectively. Produced DVMs would be helpful for policy intervention to minimize drought vulnerability.
dc.language	English
dc.publisher	TAYLOR & FRANCIS LTD
dc.relation.ispartof	Geocarto International
dc.relation.isbasedon	10.1080/10106049.2021.1989500
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	This is an Accepted Manuscript of an article published by Taylor & Francis in Geocarto International on 2021-11-18, available online: https://doi.org/10.1080/10106049.2021.1989500
dc.subject	0909 Geomatic Engineering
dc.subject.classification	Geological & Geomatics Engineering
dc.title	Proposing novel ensemble approach of particle swarm optimized and machine learning algorithms for drought vulnerability mapping in Jharkhand, India
dc.type	Journal Article
utslib.citation.volume	37
utslib.for	0909 Geomatic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2023-04-06T00:46:51Z
pubs.issue	25
pubs.publication-status	Published
pubs.volume	37
utslib.citation.issue	25

Abstract:

Drought, a natural and very complex climatic hazard, causes impacts on natural and socio-economic environments. This study aims to produce the drought vulnerability map (DVM) considering novel ensemble machine learning algorithms (MLAs) in Jharkhand, India. Forty, drought vulnerability determining factors under the categories of exposure, sensitivity, and adaptive capacity were used. Then, four machine learning and four novel ensemble approaches of particle swarm optimized (PSO) algorithms, named random forest (RF), PSO-RF, multi-layer perceptron (MLP), PSO-MLP, support vector regression (SVM), PSO-MLP, Bagging, and PSO-Bagging, were established for DVMs. The receiver operating characteristic curve (ROC), mean-absolute-error (MAE), root-mean-square-error (RMSE), precision, and K-index were utilized for judging the performance of novel ensemble MLAs. The obtained results show that the PSO-RF had the highest performance with an AUC of 0.874, followed by RF, PSO-MLP, PSO-Bagging, Bagging, MLP, PSO-SVM and SVM, respectively. Produced DVMs would be helpful for policy intervention to minimize drought vulnerability.

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http://hdl.handle.net/10453/169233