Potential of Deep Learning in drought assessment by extracting information from hydrometeorological precursors

This study explores the potential of the Deep Learning (DL) approach to develop a model for basin-scale drought assessment using information from a set of primary hydrometeorological precursors, namely air temperature, surface pressure, wind speed, relative humidity, evaporation, soil moisture and g...

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Autores principales:	Rajib Maity, Mohd Imran Khan, Subharthi Sarkar, Riya Dutta, Subhra Sekhar Maity, Manali Pal, Kironmala Chanda
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	one-dimensional convolutional neural network (conv1d) deep learning drought hydrometeorology machine learning standardized precipitation anomaly index (spai) Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/752bb8734eb9453f885d498ec3e5dc41
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spelling	oai:doaj.org-article:752bb8734eb9453f885d498ec3e5dc412021-11-05T19:08:04ZPotential of Deep Learning in drought assessment by extracting information from hydrometeorological precursors2040-22442408-935410.2166/wcc.2021.062https://doaj.org/article/752bb8734eb9453f885d498ec3e5dc412021-09-01T00:00:00Zhttp://jwcc.iwaponline.com/content/12/6/2774https://doaj.org/toc/2040-2244https://doaj.org/toc/2408-9354This study explores the potential of the Deep Learning (DL) approach to develop a model for basin-scale drought assessment using information from a set of primary hydrometeorological precursors, namely air temperature, surface pressure, wind speed, relative humidity, evaporation, soil moisture and geopotential height. The novelty of the study lies in extracting the information from the hydrometeorological precursors through the efficacy of the DL algorithm, based on a one-dimensional convolutional neural network. Drought-prone regions, from where our study basins are selected, often suffer from the vagaries of rainfall that leads to drought-like situations. It is established that the proposed DL-based model is able to capture the underlying complex relationship between rainfall and the set of aforementioned hydrometeorological variables and, subsequently, shows its promise for the basin-scale meteorological drought assessment as revealed through different performance metrics and skill scores. The accuracy of simulating the correct drought category, among the seven categories, is also high (>70%). Moreover, in general, the skill of any climate model is much higher for the primary meteorological variables as compared with other secondary or tertiary variables/phenomena, like droughts. Thus, the novelty of the proposed DL-based model also lies in the improved assessment of ensuing basin-scale meteorological droughts using the projected meteorological precursors and may lead to new research directions. HIGHLIGHTS This study explores the potential of Deep Learning (DL) approach to capture the hidden complex hydrometeorological association.; A DL-based model is developed for basin-scale drought assessment using the hidden complex relationship from a set of primary hydrometeorological precursors.; DL may be effective to use simulated primary hydrometeorological variables from climate models to assess more complex phenomena.;Rajib MaityMohd Imran KhanSubharthi SarkarRiya DuttaSubhra Sekhar MaityManali PalKironmala ChandaIWA Publishingarticleone-dimensional convolutional neural network (conv1d)deep learningdroughthydrometeorologymachine learningstandardized precipitation anomaly index (spai)Environmental technology. Sanitary engineeringTD1-1066Environmental sciencesGE1-350ENJournal of Water and Climate Change, Vol 12, Iss 6, Pp 2774-2796 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	one-dimensional convolutional neural network (conv1d) deep learning drought hydrometeorology machine learning standardized precipitation anomaly index (spai) Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
spellingShingle	one-dimensional convolutional neural network (conv1d) deep learning drought hydrometeorology machine learning standardized precipitation anomaly index (spai) Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Rajib Maity Mohd Imran Khan Subharthi Sarkar Riya Dutta Subhra Sekhar Maity Manali Pal Kironmala Chanda Potential of Deep Learning in drought assessment by extracting information from hydrometeorological precursors
description	This study explores the potential of the Deep Learning (DL) approach to develop a model for basin-scale drought assessment using information from a set of primary hydrometeorological precursors, namely air temperature, surface pressure, wind speed, relative humidity, evaporation, soil moisture and geopotential height. The novelty of the study lies in extracting the information from the hydrometeorological precursors through the efficacy of the DL algorithm, based on a one-dimensional convolutional neural network. Drought-prone regions, from where our study basins are selected, often suffer from the vagaries of rainfall that leads to drought-like situations. It is established that the proposed DL-based model is able to capture the underlying complex relationship between rainfall and the set of aforementioned hydrometeorological variables and, subsequently, shows its promise for the basin-scale meteorological drought assessment as revealed through different performance metrics and skill scores. The accuracy of simulating the correct drought category, among the seven categories, is also high (>70%). Moreover, in general, the skill of any climate model is much higher for the primary meteorological variables as compared with other secondary or tertiary variables/phenomena, like droughts. Thus, the novelty of the proposed DL-based model also lies in the improved assessment of ensuing basin-scale meteorological droughts using the projected meteorological precursors and may lead to new research directions. HIGHLIGHTS This study explores the potential of Deep Learning (DL) approach to capture the hidden complex hydrometeorological association.; A DL-based model is developed for basin-scale drought assessment using the hidden complex relationship from a set of primary hydrometeorological precursors.; DL may be effective to use simulated primary hydrometeorological variables from climate models to assess more complex phenomena.;
format	article
author	Rajib Maity Mohd Imran Khan Subharthi Sarkar Riya Dutta Subhra Sekhar Maity Manali Pal Kironmala Chanda
author_facet	Rajib Maity Mohd Imran Khan Subharthi Sarkar Riya Dutta Subhra Sekhar Maity Manali Pal Kironmala Chanda
author_sort	Rajib Maity
title	Potential of Deep Learning in drought assessment by extracting information from hydrometeorological precursors
title_short	Potential of Deep Learning in drought assessment by extracting information from hydrometeorological precursors
title_full	Potential of Deep Learning in drought assessment by extracting information from hydrometeorological precursors
title_fullStr	Potential of Deep Learning in drought assessment by extracting information from hydrometeorological precursors
title_full_unstemmed	Potential of Deep Learning in drought assessment by extracting information from hydrometeorological precursors
title_sort	potential of deep learning in drought assessment by extracting information from hydrometeorological precursors
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/752bb8734eb9453f885d498ec3e5dc41
work_keys_str_mv	AT rajibmaity potentialofdeeplearningindroughtassessmentbyextractinginformationfromhydrometeorologicalprecursors AT mohdimrankhan potentialofdeeplearningindroughtassessmentbyextractinginformationfromhydrometeorologicalprecursors AT subharthisarkar potentialofdeeplearningindroughtassessmentbyextractinginformationfromhydrometeorologicalprecursors AT riyadutta potentialofdeeplearningindroughtassessmentbyextractinginformationfromhydrometeorologicalprecursors AT subhrasekharmaity potentialofdeeplearningindroughtassessmentbyextractinginformationfromhydrometeorologicalprecursors AT manalipal potentialofdeeplearningindroughtassessmentbyextractinginformationfromhydrometeorologicalprecursors AT kironmalachanda potentialofdeeplearningindroughtassessmentbyextractinginformationfromhydrometeorologicalprecursors
_version_	1718444045066305536

Potential of Deep Learning in drought assessment by extracting information from hydrometeorological precursors

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