Forecasting standardized precipitation index using data intelligence models: regional investigation of Bangladesh

Abstract A noticeable increase in drought frequency and severity has been observed across the globe due to climate change, which attracted scientists in development of drought prediction models for mitigation of impacts. Droughts are usually monitored using drought indices (DIs), most of which are p...

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Autores principales:	Zaher Mundher Yaseen, Mumtaz Ali, Ahmad Sharafati, Nadhir Al-Ansari, Shamsuddin Shahid
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/86d2998ed6864c0f8756534d8bb35ba9
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spelling	oai:doaj.org-article:86d2998ed6864c0f8756534d8bb35ba92021-12-02T12:09:18ZForecasting standardized precipitation index using data intelligence models: regional investigation of Bangladesh10.1038/s41598-021-82977-92045-2322https://doaj.org/article/86d2998ed6864c0f8756534d8bb35ba92021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-82977-9https://doaj.org/toc/2045-2322Abstract A noticeable increase in drought frequency and severity has been observed across the globe due to climate change, which attracted scientists in development of drought prediction models for mitigation of impacts. Droughts are usually monitored using drought indices (DIs), most of which are probabilistic and therefore, highly stochastic and non-linear. The current research investigated the capability of different versions of relatively well-explored machine learning (ML) models including random forest (RF), minimum probability machine regression (MPMR), M5 Tree (M5tree), extreme learning machine (ELM) and online sequential-ELM (OSELM) in predicting the most widely used DI known as standardized precipitation index (SPI) at multiple month horizons (i.e., 1, 3, 6 and 12). Models were developed using monthly rainfall data for the period of 1949–2013 at four meteorological stations namely, Barisal, Bogra, Faridpur and Mymensingh, each representing a geographical region of Bangladesh which frequently experiences droughts. The model inputs were decided based on correlation statistics and the prediction capability was evaluated using several statistical metrics including mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (R), Willmott’s Index of agreement (WI), Nash Sutcliffe efficiency (NSE), and Legates and McCabe Index (LM). The results revealed that the proposed models are reliable and robust in predicting droughts in the region. Comparison of the models revealed ELM as the best model in forecasting droughts with minimal RMSE in the range of 0.07–0.85, 0.08–0.76, 0.062–0.80 and 0.042–0.605 for Barisal, Bogra, Faridpur and Mymensingh, respectively for all the SPI scales except one-month SPI for which the RF showed the best performance with minimal RMSE of 0.57, 0.45, 0.59 and 0.42, respectively.Zaher Mundher YaseenMumtaz AliAhmad SharafatiNadhir Al-AnsariShamsuddin ShahidNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-25 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Zaher Mundher Yaseen Mumtaz Ali Ahmad Sharafati Nadhir Al-Ansari Shamsuddin Shahid Forecasting standardized precipitation index using data intelligence models: regional investigation of Bangladesh
description	Abstract A noticeable increase in drought frequency and severity has been observed across the globe due to climate change, which attracted scientists in development of drought prediction models for mitigation of impacts. Droughts are usually monitored using drought indices (DIs), most of which are probabilistic and therefore, highly stochastic and non-linear. The current research investigated the capability of different versions of relatively well-explored machine learning (ML) models including random forest (RF), minimum probability machine regression (MPMR), M5 Tree (M5tree), extreme learning machine (ELM) and online sequential-ELM (OSELM) in predicting the most widely used DI known as standardized precipitation index (SPI) at multiple month horizons (i.e., 1, 3, 6 and 12). Models were developed using monthly rainfall data for the period of 1949–2013 at four meteorological stations namely, Barisal, Bogra, Faridpur and Mymensingh, each representing a geographical region of Bangladesh which frequently experiences droughts. The model inputs were decided based on correlation statistics and the prediction capability was evaluated using several statistical metrics including mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (R), Willmott’s Index of agreement (WI), Nash Sutcliffe efficiency (NSE), and Legates and McCabe Index (LM). The results revealed that the proposed models are reliable and robust in predicting droughts in the region. Comparison of the models revealed ELM as the best model in forecasting droughts with minimal RMSE in the range of 0.07–0.85, 0.08–0.76, 0.062–0.80 and 0.042–0.605 for Barisal, Bogra, Faridpur and Mymensingh, respectively for all the SPI scales except one-month SPI for which the RF showed the best performance with minimal RMSE of 0.57, 0.45, 0.59 and 0.42, respectively.
format	article
author	Zaher Mundher Yaseen Mumtaz Ali Ahmad Sharafati Nadhir Al-Ansari Shamsuddin Shahid
author_facet	Zaher Mundher Yaseen Mumtaz Ali Ahmad Sharafati Nadhir Al-Ansari Shamsuddin Shahid
author_sort	Zaher Mundher Yaseen
title	Forecasting standardized precipitation index using data intelligence models: regional investigation of Bangladesh
title_short	Forecasting standardized precipitation index using data intelligence models: regional investigation of Bangladesh
title_full	Forecasting standardized precipitation index using data intelligence models: regional investigation of Bangladesh
title_fullStr	Forecasting standardized precipitation index using data intelligence models: regional investigation of Bangladesh
title_full_unstemmed	Forecasting standardized precipitation index using data intelligence models: regional investigation of Bangladesh
title_sort	forecasting standardized precipitation index using data intelligence models: regional investigation of bangladesh
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/86d2998ed6864c0f8756534d8bb35ba9
work_keys_str_mv	AT zahermundheryaseen forecastingstandardizedprecipitationindexusingdataintelligencemodelsregionalinvestigationofbangladesh AT mumtazali forecastingstandardizedprecipitationindexusingdataintelligencemodelsregionalinvestigationofbangladesh AT ahmadsharafati forecastingstandardizedprecipitationindexusingdataintelligencemodelsregionalinvestigationofbangladesh AT nadhiralansari forecastingstandardizedprecipitationindexusingdataintelligencemodelsregionalinvestigationofbangladesh AT shamsuddinshahid forecastingstandardizedprecipitationindexusingdataintelligencemodelsregionalinvestigationofbangladesh
_version_	1718394701211500544

Forecasting standardized precipitation index using data intelligence models: regional investigation of Bangladesh

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