Manifesting deep learning algorithms for developing drought vulnerability index in monsoon climate dominant region of West Bengal, India

Saha, S; Kundu, B; Saha, A; Mukherjee, K; Pradhan, B

Manifesting deep learning algorithms for developing drought vulnerability index in monsoon climate dominant region of West Bengal, India

Saha, S Kundu, B Saha, A Mukherjee, K Pradhan, B

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Publisher:: SPRINGER WIEN
Publication Type:: Journal Article
Citation:: Theoretical and Applied Climatology, 2023, 151, (1-2), pp. 891-913
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Saha, S
dc.contributor.author	Kundu, B
dc.contributor.author	Saha, A
dc.contributor.author	Mukherjee, K
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.date.accessioned	2024-02-01T03:44:50Z
dc.date.available	2024-02-01T03:44:50Z
dc.date.issued	2023-01-01
dc.identifier.citation	Theoretical and Applied Climatology, 2023, 151, (1-2), pp. 891-913
dc.identifier.issn	0177-798X
dc.identifier.issn	1434-4483
dc.identifier.uri	http://hdl.handle.net/10453/175214
dc.description.abstract	Drought is a natural and complex climatic hazard that has consequences for both natural and socio-economic contexts. The drought vulnerability assessment is an important contribution towards reducing its impact on the society and people. Although there has been significant research progress in the forecasting and prediction of droughts, but its vulnerability evaluation using high-precision deep learning algorithms has not yet been made. Keeping these things in mind, the present research is focused on preparing drought vulnerability map (DVM) for the monsoon climate dominant region of West Bengal, India. Fifty drought vulnerability determining variables were employed and divided into four categories based on the drought types: hydrological drought, agricultural drought, meteorological drought, and socio-economic drought. Then one deep learning model, i.e., Deep Learning Neural Network (DLNN) and two machine learning methods, i.e., Artificial Neural Network (ANN) and Multitask Gaussian Process (MGP) were used for preparing the drought vulnerability maps (DVMs). Results show nearly 24% of the study area is very highly vulnerable to drought. Several statistical performance indices such as the receiver operating characteristic (ROC) curve, mean-absolute-error (MAE), root-mean-square-error (RMSE), accuracy, Jaccard, F1 measure, MCC, and K-index were applied for judging the efficacy of the DVMs. The results show that the DLNN performed best, with an AUC of 94.8%, followed by ANN (89.66%) and MGP (87.08%). Hence, the deep learning algorithms performed better than the traditional machine learning models to prepare the DVMs. The combination of various parameters of different types of droughts can accurately predict the drought vulnerable zones with very high accuracy. This can be useful for policy-makers in managing drought severity in the south-western part of West Bengal.
dc.language	English
dc.publisher	SPRINGER WIEN
dc.relation.ispartof	Theoretical and Applied Climatology
dc.relation.isbasedon	10.1007/s00704-022-04300-4
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0401 Atmospheric Sciences
dc.subject.classification	Meteorology & Atmospheric Sciences
dc.subject.classification	3701 Atmospheric sciences
dc.subject.classification	3702 Climate change science
dc.subject.classification	4101 Climate change impacts and adaptation
dc.title	Manifesting deep learning algorithms for developing drought vulnerability index in monsoon climate dominant region of West Bengal, India
dc.type	Journal Article
utslib.citation.volume	151
utslib.for	0401 Atmospheric Sciences
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	closed_access	*
dc.date.updated	2024-02-01T03:44:49Z
pubs.issue	1-2
pubs.publication-status	Published
pubs.volume	151
utslib.citation.issue	1-2

Abstract:

Drought is a natural and complex climatic hazard that has consequences for both natural and socio-economic contexts. The drought vulnerability assessment is an important contribution towards reducing its impact on the society and people. Although there has been significant research progress in the forecasting and prediction of droughts, but its vulnerability evaluation using high-precision deep learning algorithms has not yet been made. Keeping these things in mind, the present research is focused on preparing drought vulnerability map (DVM) for the monsoon climate dominant region of West Bengal, India. Fifty drought vulnerability determining variables were employed and divided into four categories based on the drought types: hydrological drought, agricultural drought, meteorological drought, and socio-economic drought. Then one deep learning model, i.e., Deep Learning Neural Network (DLNN) and two machine learning methods, i.e., Artificial Neural Network (ANN) and Multitask Gaussian Process (MGP) were used for preparing the drought vulnerability maps (DVMs). Results show nearly 24% of the study area is very highly vulnerable to drought. Several statistical performance indices such as the receiver operating characteristic (ROC) curve, mean-absolute-error (MAE), root-mean-square-error (RMSE), accuracy, Jaccard, F1 measure, MCC, and K-index were applied for judging the efficacy of the DVMs. The results show that the DLNN performed best, with an AUC of 94.8%, followed by ANN (89.66%) and MGP (87.08%). Hence, the deep learning algorithms performed better than the traditional machine learning models to prepare the DVMs. The combination of various parameters of different types of droughts can accurately predict the drought vulnerable zones with very high accuracy. This can be useful for policy-makers in managing drought severity in the south-western part of West Bengal.

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