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Article

Estimating the Standardized Precipitation Evapotranspiration Index Using Data-Driven Techniques: A Regional Study of Bangladesh

1
Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura 35516, Egypt
2
Faculty of Maritime Studies, King Abdulaziz University, Jeddah 21589, Saudi Arabia
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Farm Machinery and Postharvest Technology Division, Bangladesh Rice Research Institute, Gazipur 1701, Bangladesh
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School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Malaysia
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Irrigation and Water Management Division, Bangladesh Agricultural Research Institute, Gazipur 1701, Bangladesh
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Agricultural Economics Division, Bangladesh Rice Research Institute, Gazipur 1701, Bangladesh
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Tuber Crops Research Centre, Bangladesh Agricultural Research Institute, Gazipur 1701, Bangladesh
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Irrigation and Water Management Division, Bangladesh Rice Research Institute, Gazipur 1701, Bangladesh
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Mustafa M. Aral
Water 2022, 14(11), 1764; https://doi.org/10.3390/w14111764
Received: 23 April 2022 / Revised: 14 May 2022 / Accepted: 23 May 2022 / Published: 30 May 2022
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. View Full-Text
Keywords: drought prediction; standardized precipitation evapotranspiration index; hybrid machine learning; additive regression; northern Bangladesh drought prediction; standardized precipitation evapotranspiration index; hybrid machine learning; additive regression; northern Bangladesh
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MDPI and ACS Style

Elbeltagi, A.; AlThobiani, F.; Kamruzzaman, M.; Shaid, S.; Roy, D.K.; Deb, L.; Islam, M.M.; Kundu, P.K.; Rahman, M.M. Estimating the Standardized Precipitation Evapotranspiration Index Using Data-Driven Techniques: A Regional Study of Bangladesh. Water 2022, 14, 1764. https://doi.org/10.3390/w14111764

AMA Style

Elbeltagi A, AlThobiani F, Kamruzzaman M, Shaid S, Roy DK, Deb L, Islam MM, Kundu PK, Rahman MM. Estimating the Standardized Precipitation Evapotranspiration Index Using Data-Driven Techniques: A Regional Study of Bangladesh. Water. 2022; 14(11):1764. https://doi.org/10.3390/w14111764

Chicago/Turabian Style

Elbeltagi, Ahmed, Faisal AlThobiani, Mohammad Kamruzzaman, Shamsuddin Shaid, Dilip Kumar Roy, Limon Deb, Md Mazadul Islam, Palash Kumar Kundu, and Md. Mizanur Rahman. 2022. "Estimating the Standardized Precipitation Evapotranspiration Index Using Data-Driven Techniques: A Regional Study of Bangladesh" Water 14, no. 11: 1764. https://doi.org/10.3390/w14111764

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