Staff View: Combination of data-driven models and best subset regression for predicting the standardized precipitation index (SPI) at the Upper Godavari Basin in India

Combination of data-driven models and best subset regression for predicting the standardized precipitation index (SPI) at the Upper Godavari Basin in India

Standardized precipitation index prediction and monitoring are essential to mitigating the effect of drought actions on precision farming, environments, climate-smart agriculture, and the water cycle. In this study, four data-driven models, additive regression, random subspace, M5Pruned (M5P), and b...

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Main Authors:	Pande C.B., Costache R., Sammen S.S., Noor R., Elbeltagi A.
Other Authors:	57193547008
Format:	Article
Published:	Springer 2024
Subjects:	Godavari Basin India climate modeling climate prediction drought machine learning regression analysis
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spelling	my.uniten.dspace-343052024-10-14T11:18:57Z Combination of data-driven models and best subset regression for predicting the standardized precipitation index (SPI) at the Upper Godavari Basin in India Pande C.B. Costache R. Sammen S.S. Noor R. Elbeltagi A. 57193547008 55888132500 57192093108 57221282650 57204724397 Godavari Basin India climate modeling climate prediction drought machine learning regression analysis Standardized precipitation index prediction and monitoring are essential to mitigating the effect of drought actions on precision farming, environments, climate-smart agriculture, and the water cycle. In this study, four data-driven models, additive regression, random subspace, M5Pruned (M5P), and bagging tree models, were adopted to predict the standardized precipitation index (SPI) at the Upper Godavari Basin for various periods (3�months, 6�months, and 12�months). The data-driven models� input data was pre-processed with machine learning models to increase quality and the model�s performance a priori. These four models predicted the SPI-3, SPI-6, and SPI-12�months based on three metrological station data. Based on the statistical performance metrics such as correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE), relative absolute error (RAE), and root relative squared error (RRSE), our findings showed that the bagging was the best model for predicting SPI-3 and SPI-6 while the M5P the best for SPI-12 estimation in station 1, while in stations 2 and 3, M5P was superlative in predicting the SPI-3 and SPI-12�months, and the bagging was best in SPI-6. All the best models had acceptable mid-term drought forecasting based on the SPI-3, SPI-6, and SPI-12�months for three stations in the Upper Godavari Basin in India. The machine learning models created in this study produced satisfactory results in short-term and mid-term drought forecasting, and it will be a new strategy for water developers and planners to use for future management and scheduling. � 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature. Final 2024-10-14T03:18:56Z 2024-10-14T03:18:56Z 2023 Article 10.1007/s00704-023-04426-z 2-s2.0-85150652830 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150652830&doi=10.1007%2fs00704-023-04426-z&partnerID=40&md5=75764d6178a9da91c7be314ff1e6b5ac https://irepository.uniten.edu.my/handle/123456789/34305 152 1-Feb 535 558 Springer Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
topic	Godavari Basin India climate modeling climate prediction drought machine learning regression analysis
spellingShingle	Godavari Basin India climate modeling climate prediction drought machine learning regression analysis Pande C.B. Costache R. Sammen S.S. Noor R. Elbeltagi A. Combination of data-driven models and best subset regression for predicting the standardized precipitation index (SPI) at the Upper Godavari Basin in India
description	Standardized precipitation index prediction and monitoring are essential to mitigating the effect of drought actions on precision farming, environments, climate-smart agriculture, and the water cycle. In this study, four data-driven models, additive regression, random subspace, M5Pruned (M5P), and bagging tree models, were adopted to predict the standardized precipitation index (SPI) at the Upper Godavari Basin for various periods (3�months, 6�months, and 12�months). The data-driven models� input data was pre-processed with machine learning models to increase quality and the model�s performance a priori. These four models predicted the SPI-3, SPI-6, and SPI-12�months based on three metrological station data. Based on the statistical performance metrics such as correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE), relative absolute error (RAE), and root relative squared error (RRSE), our findings showed that the bagging was the best model for predicting SPI-3 and SPI-6 while the M5P the best for SPI-12 estimation in station 1, while in stations 2 and 3, M5P was superlative in predicting the SPI-3 and SPI-12�months, and the bagging was best in SPI-6. All the best models had acceptable mid-term drought forecasting based on the SPI-3, SPI-6, and SPI-12�months for three stations in the Upper Godavari Basin in India. The machine learning models created in this study produced satisfactory results in short-term and mid-term drought forecasting, and it will be a new strategy for water developers and planners to use for future management and scheduling. � 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.
author2	57193547008
author_facet	57193547008 Pande C.B. Costache R. Sammen S.S. Noor R. Elbeltagi A.
format	Article
author	Pande C.B. Costache R. Sammen S.S. Noor R. Elbeltagi A.
author_sort	Pande C.B.
title	Combination of data-driven models and best subset regression for predicting the standardized precipitation index (SPI) at the Upper Godavari Basin in India
title_short	Combination of data-driven models and best subset regression for predicting the standardized precipitation index (SPI) at the Upper Godavari Basin in India
title_full	Combination of data-driven models and best subset regression for predicting the standardized precipitation index (SPI) at the Upper Godavari Basin in India
title_fullStr	Combination of data-driven models and best subset regression for predicting the standardized precipitation index (SPI) at the Upper Godavari Basin in India
title_full_unstemmed	Combination of data-driven models and best subset regression for predicting the standardized precipitation index (SPI) at the Upper Godavari Basin in India
title_sort	combination of data-driven models and best subset regression for predicting the standardized precipitation index (spi) at the upper godavari basin in india
publisher	Springer
publishDate	2024
_version_	1814061115677605888
score	13.222552

Combination of data-driven models and best subset regression for predicting the standardized precipitation index (SPI) at the Upper Godavari Basin in India

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