Preprints
https://doi.org/10.5194/hess-2023-299
https://doi.org/10.5194/hess-2023-299
23 Jan 2024
 | 23 Jan 2024
Status: this preprint is currently under review for the journal HESS.

Exploring Long-term Monthly Prediction of Precipitation Isotopes over Southeast Asia: A Comparative Analysis of Machine-Learning Models

Mojtaba Heydarizad, Liu Zhongfang, Nathsuda Pumijumnong, Masoud Minaei, Pouya Salari, Rogert Sori, and Hamid Ghalibaf Mohammadabadi

Abstract. Using stable isotope methods is essential for studying tropical hydrology and climatology. The purpose of this research was to investigate the influence of large-scale climate modes (teleconnection indices) and local meteorological parameters on the stable isotope contents in six different stations, including Bangkok, Kuala Lumpur, Jakarta, Kota Bharu, Jayapura, and Singapore in Southeast Asia. To achieve this goal, several machine learning (ML) techniques were employed, such as shallow neural network (SNN), deep neural network (DNN), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost). XGBoost demonstrated the highest accuracy across the majority of studied stations, with a R2 = 0.91, VNS=0.90, AIC= 405, BIC=410, and RMSE = 0.76. Additionally, DNN exhibited superior accuracy in specific cases, achieving a R2 = 0.87, VNS=0.87, AIC = 445, BIC = 460, and RMSE = 1.10. Furthermore, a bootstrap analysis was conducted to assess the uncertainty of the simulated data in each station. The results of this analysis demonstrated acceptable accuracy, as the majority of simulated data points fell within the 95 % confidence intervals. Finally, stable isotope contents in precipitation were forecasted for one year using Vector Autoregression (VAR) and ML techniques. This study underscores the efficacy of ML techniques in both simulating and forecasting stable isotope contents with high precision. The inclusion of specific accuracy metrics strengthens the validity of claims in this study and provides a clearer picture of the quantitative outcomes of this research.

Mojtaba Heydarizad, Liu Zhongfang, Nathsuda Pumijumnong, Masoud Minaei, Pouya Salari, Rogert Sori, and Hamid Ghalibaf Mohammadabadi

Status: open (until 30 Mar 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on hess-2023-299', Ali Mobaraki, 28 Jan 2024 reply
    • AC1: 'Reply on CC1', Mojtaba Heydarizad, 02 Feb 2024 reply
  • CC2: 'Comment on hess-2023-299', Ville Järvinen, 15 Feb 2024 reply
    • AC2: 'Reply on CC2', Mojtaba Heydarizad, 18 Feb 2024 reply
Mojtaba Heydarizad, Liu Zhongfang, Nathsuda Pumijumnong, Masoud Minaei, Pouya Salari, Rogert Sori, and Hamid Ghalibaf Mohammadabadi
Mojtaba Heydarizad, Liu Zhongfang, Nathsuda Pumijumnong, Masoud Minaei, Pouya Salari, Rogert Sori, and Hamid Ghalibaf Mohammadabadi

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Short summary
This research showed how various factors affect 18O and 2H isotopes in precipitation in Southeast Asia. Various machine learning (ML) models were used to analyze the data. The reliability of predictions were also tested which confirmed the accurate predictions of this study. In addition, another model called VAR, beside ML model have been used to forecast the stable isotopes.