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Estimating the standardized precipitation evapotranspiration index using data-driven techniques: a regional study of Bangladesh

Drought prediction is the most effective way to mitigate drought impacts. The current study examined the ability of three renowned machine learning models, namely additive regression (AR), random subspace (RSS), and M5P tree, and their hybridized versions (AR-RSS, AR-M5P, RSS-M5P, and AR-RSS-M5P) in...

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Main Authors: Elbeltagi, Ahmed, Al Thobiani, Faisal, Mohammad Kamruzzaman, Mohammad Kamruzzaman, Shaid, Shamsuddin, Roy, Dilip Kumar, Limon Deb, Limon Deb, Islam, Md. Mazadul, Kundu, Palash Kumar, Rahman, Md. Mizanur
Format: Article
Language:English
Published: MDPI 2022
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.utm.my/104703/1/ShamsuddinShahid2022_EstimatingtheStandardizedPrecipitationEvapotranspiration.pdf
http://eprints.utm.my/104703/
http://dx.doi.org/10.3390/w14111764
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spelling my.utm.1047032024-03-01T01:33:36Z http://eprints.utm.my/104703/ Estimating the standardized precipitation evapotranspiration index using data-driven techniques: a regional study of Bangladesh Elbeltagi, Ahmed Al Thobiani, Faisal Mohammad Kamruzzaman, Mohammad Kamruzzaman Shaid, Shamsuddin Roy, Dilip Kumar Limon Deb, Limon Deb Islam, Md. Mazadul Kundu, Palash Kumar Rahman, Md. Mizanur TA Engineering (General). Civil engineering (General) Drought prediction is the most effective way to mitigate drought impacts. The current study examined the ability of three renowned machine learning models, namely additive regression (AR), random subspace (RSS), and M5P tree, and their hybridized versions (AR-RSS, AR-M5P, RSS-M5P, and AR-RSS-M5P) in predicting the standardized precipitation evapotranspiration index (SPEI) in multiple time scales. The SPEIs were calculated using monthly rainfall and temperature data over 39 years (1980–2018). The best subset regression model and sensitivity analysis were used to determine the most appropriate input variables from a series of input combinations involving up to eight SPEI lags. The models were built at Rajshahi station and validated at four other sites (Mymensingh, Rangpur, Bogra, and Khulna) in drought-prone northern Bangladesh. The findings indicated that the proposed models can accurately forecast droughts at the Rajshahi station. The M5P model predicted the SPEIs better than the other models, with the lowest mean absolute error (27.89–62.92%), relative absolute error (0.39–0.67), mean absolute error (0.208–0.49), root mean square error (0.39–0.67) and highest correlation coefficient (0.75–0.98). Moreover, the M5P model could accurately forecast droughts with different time scales at validation locations. The prediction accuracy was better for droughts with longer periods. MDPI 2022-06-01 Article PeerReviewed application/pdf en http://eprints.utm.my/104703/1/ShamsuddinShahid2022_EstimatingtheStandardizedPrecipitationEvapotranspiration.pdf Elbeltagi, Ahmed and Al Thobiani, Faisal and Mohammad Kamruzzaman, Mohammad Kamruzzaman and Shaid, Shamsuddin and Roy, Dilip Kumar and Limon Deb, Limon Deb and Islam, Md. Mazadul and Kundu, Palash Kumar and Rahman, Md. Mizanur (2022) Estimating the standardized precipitation evapotranspiration index using data-driven techniques: a regional study of Bangladesh. Water, 14 (11). pp. 1-16. ISSN 2073-4441 http://dx.doi.org/10.3390/w14111764 DOI:10.3390/w14111764
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Elbeltagi, Ahmed
Al Thobiani, Faisal
Mohammad Kamruzzaman, Mohammad Kamruzzaman
Shaid, Shamsuddin
Roy, Dilip Kumar
Limon Deb, Limon Deb
Islam, Md. Mazadul
Kundu, Palash Kumar
Rahman, Md. Mizanur
Estimating the standardized precipitation evapotranspiration index using data-driven techniques: a regional study of Bangladesh
description Drought prediction is the most effective way to mitigate drought impacts. The current study examined the ability of three renowned machine learning models, namely additive regression (AR), random subspace (RSS), and M5P tree, and their hybridized versions (AR-RSS, AR-M5P, RSS-M5P, and AR-RSS-M5P) in predicting the standardized precipitation evapotranspiration index (SPEI) in multiple time scales. The SPEIs were calculated using monthly rainfall and temperature data over 39 years (1980–2018). The best subset regression model and sensitivity analysis were used to determine the most appropriate input variables from a series of input combinations involving up to eight SPEI lags. The models were built at Rajshahi station and validated at four other sites (Mymensingh, Rangpur, Bogra, and Khulna) in drought-prone northern Bangladesh. The findings indicated that the proposed models can accurately forecast droughts at the Rajshahi station. The M5P model predicted the SPEIs better than the other models, with the lowest mean absolute error (27.89–62.92%), relative absolute error (0.39–0.67), mean absolute error (0.208–0.49), root mean square error (0.39–0.67) and highest correlation coefficient (0.75–0.98). Moreover, the M5P model could accurately forecast droughts with different time scales at validation locations. The prediction accuracy was better for droughts with longer periods.
format Article
author Elbeltagi, Ahmed
Al Thobiani, Faisal
Mohammad Kamruzzaman, Mohammad Kamruzzaman
Shaid, Shamsuddin
Roy, Dilip Kumar
Limon Deb, Limon Deb
Islam, Md. Mazadul
Kundu, Palash Kumar
Rahman, Md. Mizanur
author_facet Elbeltagi, Ahmed
Al Thobiani, Faisal
Mohammad Kamruzzaman, Mohammad Kamruzzaman
Shaid, Shamsuddin
Roy, Dilip Kumar
Limon Deb, Limon Deb
Islam, Md. Mazadul
Kundu, Palash Kumar
Rahman, Md. Mizanur
author_sort Elbeltagi, Ahmed
title Estimating the standardized precipitation evapotranspiration index using data-driven techniques: a regional study of Bangladesh
title_short Estimating the standardized precipitation evapotranspiration index using data-driven techniques: a regional study of Bangladesh
title_full Estimating the standardized precipitation evapotranspiration index using data-driven techniques: a regional study of Bangladesh
title_fullStr Estimating the standardized precipitation evapotranspiration index using data-driven techniques: a regional study of Bangladesh
title_full_unstemmed Estimating the standardized precipitation evapotranspiration index using data-driven techniques: a regional study of Bangladesh
title_sort estimating the standardized precipitation evapotranspiration index using data-driven techniques: a regional study of bangladesh
publisher MDPI
publishDate 2022
url http://eprints.utm.my/104703/1/ShamsuddinShahid2022_EstimatingtheStandardizedPrecipitationEvapotranspiration.pdf
http://eprints.utm.my/104703/
http://dx.doi.org/10.3390/w14111764
_version_ 1793149208676532224
score 13.211869

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