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

Yaseen, Zaher M, Ali, Mumtaz, Sharafati, Ahmad, Al-Ansari, Nadhir and Shahid, Shamsuddin 2021, Forecasting standardized precipitation index using data intelligence models: regional investigation of Bangladesh, Scientific reports, vol. 11, no. 1, pp. 1-25, doi: 10.1038/s41598-021-82977-9.

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Title Forecasting standardized precipitation index using data intelligence models: regional investigation of Bangladesh
Author(s) Yaseen, Zaher M
Ali, MumtazORCID iD for Ali, Mumtaz orcid.org/0000-0002-6975-5159
Sharafati, Ahmad
Al-Ansari, Nadhir
Shahid, Shamsuddin
Journal name Scientific reports
Volume number 11
Issue number 1
Article ID 3435
Start page 1
End page 25
Total pages 25
Publisher Nature Publishing Group
Place of publication London, Eng.
Publication date 2021
ISSN 2045-2322
2045-2322
Keyword(s) Climate change
Climate sciences
Hydrology
Projection and prediction
Summary 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.
Language eng
DOI 10.1038/s41598-021-82977-9
Indigenous content off
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30147983

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.