Automated Model Selection Using Bayesian Optimization and the Asynchronous Successive Halving Algorithm for Predicting Daily Minimum and Maximum Temperatures

Dilip Kumar Roy (), Mohamed Anower Hossain, Mohamed Panjarul Haque, Abed Alataway, Ahmed Z. Dewidar and Mohamed A. Mattar ()
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Dilip Kumar Roy: Irrigation and Water Management Division, Bangladesh Agricultural Research Institute, Gazipur 1701, Bangladesh
Mohamed Anower Hossain: Irrigation and Water Management Division, Bangladesh Agricultural Research Institute, Gazipur 1701, Bangladesh
Mohamed Panjarul Haque: Irrigation and Water Management Division, Bangladesh Agricultural Research Institute, Gazipur 1701, Bangladesh
Abed Alataway: Prince Sultan Bin Abdulaziz International Prize for Water Chair, Prince Sultan Institute for Environmental, Water and Desert Research, King Saud University, Riyadh 11451, Saudi Arabia
Ahmed Z. Dewidar: Prince Sultan Bin Abdulaziz International Prize for Water Chair, Prince Sultan Institute for Environmental, Water and Desert Research, King Saud University, Riyadh 11451, Saudi Arabia
Mohamed A. Mattar: Prince Sultan Bin Abdulaziz International Prize for Water Chair, Prince Sultan Institute for Environmental, Water and Desert Research, King Saud University, Riyadh 11451, Saudi Arabia

Agriculture, 2024, vol. 14, issue 2, 1-30

Abstract: This study addresses the crucial role of temperature forecasting, particularly in agricultural contexts, where daily maximum ( T m a x ) and minimum ( T m i n ) temperatures significantly impact crop growth and irrigation planning. While machine learning (ML) models offer a promising avenue for temperature forecasts, the challenge lies in efficiently training multiple models and optimizing their parameters. This research addresses a research gap by proposing advanced ML algorithms for multi-step-ahead T m a x and T m i n forecasting across various weather stations in Bangladesh. The study employs Bayesian optimization and the asynchronous successive halving algorithm (ASHA) to automatically select top-performing ML models by tuning hyperparameters. While both the Bayesian and ASHA optimizations yield satisfactory results, ASHA requires less computational time for convergence. Notably, different top-performing models emerge for T m a x and T m i n across various forecast horizons. The evaluation metrics on the test dataset confirm higher accuracy, efficiency coefficients, and agreement indices, along with lower error values for both T m a x and T m i n forecasts at different weather stations. Notably, the forecasting accuracy decreases with longer horizons, emphasizing the superiority of one-step-ahead predictions. The automated model selection approach using Bayesian and ASHA optimization algorithms proves promising for enhancing the precision of multi-step-ahead temperature forecasting, with potential applications in diverse geographical locations.

Keywords: temperature forecasts; automated machine learning; Bayesian optimization; asynchronous successive halving algorithm; Bangladesh (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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