Application of Advanced Optimized Soft Computing Models for Atmospheric Variable Forecasting

Rana Muhammad Adnan, Sarita Gajbhiye Meshram, Reham R. Mostafa, Abu Reza Md. Towfiqul Islam (), S. I. Abba, Francis Andorful and Zhihuan Chen ()
Additional contact information
Rana Muhammad Adnan: School of Economics and Statistics, Guangzhou University, Guangzhou 510006, China
Sarita Gajbhiye Meshram: Water Resources and Applied Mathematics Research Lab, Nagpur 440027, India
Reham R. Mostafa: Information Systems Department, Faculty of Computers and Information Sciences, Mansoura University, Mansoura 35516, Egypt
Abu Reza Md. Towfiqul Islam: Department of Disaster Management, Begum Rokeya University, Rangpur 5400, Bangladesh
S. I. Abba: Interdisciplinary Research Center for Membranes and Water Security, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
Francis Andorful: Department of Geography and Resource Development, University of Ghana, Accra 23321, Ghana
Zhihuan Chen: Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 431400, China

Mathematics, 2023, vol. 11, issue 5, 1-29

Abstract: Precise Air temperature modeling is crucial for a sustainable environment. In this study, a novel binary optimized machine learning model, the random vector functional link (RVFL) with the integration of Moth Flame Optimization Algorithm (MFO) and Water Cycle Optimization Algorithm (WCA) is examined to estimate the monthly and daily temperature time series of Rajshahi Climatic station in Bangladesh. Various combinations of temperature and precipitation were used to predict the temperature time series. The prediction ability of the novel binary optimized machine learning model (RVFL-WCAMFO) is compared with the single optimized machine learning models (RVFL-WCA and RVFL-MFO) and the standalone machine learning model (RVFL). Root mean square errors (RMSE), the mean absolute error (MAE), the Nash–Sutcliffe efficiency (NSE), and the determination coefficient (R 2 ) statistical indexes were utilized to access the prediction ability of the selected models. The proposed binary optimized machine learning model (RVFL-WCAMFO) outperformed the other single optimized and standalone machine learning models in prediction of air temperature time series on both scales, i.e., daily and monthly scale. Cross-validation technique was applied to determine the best testing dataset and it was found that the M3 dataset provided more accurate results for the monthly scale, whereas the M1 dataset outperformed the other two datasets on the daily scale. On the monthly scale, periodicity input was also added to see the effect on prediction accuracy. It was found that periodicity input improved the prediction accuracy of the models. It was also found that precipitation-based inputs did not provided very accurate results in comparison to temperature-based inputs. The outcomes of the study recommend the use of RVFL-WCAMFO in air temperature modeling.

Keywords: machine learning; hybrid modeling; moth flame optimization (MFO); water cycle algorithm (WCA); random vector functional link (RVFL) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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