Fractional Importance of Various Moisture Sources Influencing Precipitation in Iran Using a Comparative Analysis of Analytical Hierarchy Processes and Machine Learning Techniques
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Issued Date
2022-12-01
Resource Type
eISSN
20734433
Scopus ID
2-s2.0-85144624559
Journal Title
Atmosphere
Volume
13
Issue
12
Rights Holder(s)
SCOPUS
Bibliographic Citation
Atmosphere Vol.13 No.12 (2022)
Suggested Citation
Heydarizad M., Pumijumnong N., Sorí R., Salari P., Gimeno L. Fractional Importance of Various Moisture Sources Influencing Precipitation in Iran Using a Comparative Analysis of Analytical Hierarchy Processes and Machine Learning Techniques. Atmosphere Vol.13 No.12 (2022). doi:10.3390/atmos13122019 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/84488
Title
Fractional Importance of Various Moisture Sources Influencing Precipitation in Iran Using a Comparative Analysis of Analytical Hierarchy Processes and Machine Learning Techniques
Author(s)
Other Contributor(s)
Abstract
Studying the moisture sources responsible for precipitation in Iran is highly important. In recent years, moisture sources that influence precipitation across Iran have been studied using various methods. In this study, moisture uptake rate from individual sources that influences precipitation across Iran has been determined using the (E − P) values obtained by the FLEXPART model for the 1981–2015 period. Then, moisture uptake rate from individual sources has been used as independent parameters to investigate the fractional importance of moisture sources that influence precipitation in Iran using analytical hierarchy process (AHP) as well as machine learning (ML) methods including artificial neural networks, Decision Tree, Random Forest, Gboost, and XGboost. Furthermore, the average annual precipitation in Iran was simulated using ML methods. The results showed that the Arabian Sea has a dominant fractional influence on precipitation in both wet (November to April) and dry (May to October) periods. Simulation of precipitation amounts using the ML methods presented accurate models during the wet period, whereas the developed models for the dry period were not adequate. Finally, validation of the accuracy of the ML models using RMSE and R2 values showed that the models developed using XGboost had the highest accuracy.