Salinity forecasting in Chao Phraya River using XGBoost with missing data handling

Jiramate Changklom; Trang Prommana; Phakawat Lamchuan; Adichai Pornprommin; Jiramate Changklom; Trang Prommana; Phakawat Lamchuan; Adichai Pornprommin

doi:10.3934/environsci.2025040

AIMS Environmental Science

2025, Volume 12, Issue 5: 916-935. doi: 10.3934/environsci.2025040

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Salinity forecasting in Chao Phraya River using XGBoost with missing data handling

1.
Department of Water Resources Engineering, Faculty of Engineering, Kasetsart University, Bangkok 10900, Thailand
2.
Office of Engineering and Architectural Design, Royal Irrigation Department, Bangkok 10900, Thailand

Received: 12 July 2025 Revised: 05 October 2025 Accepted: 14 October 2025 Published: 29 October 2025

Machine learning models for water quality prediction often encounter challenges due to incomplete datasets resulting from issues such as sensor failures or data transmission errors. However, XGBoost can learn from incomplete data and make predictions even when some input values are missing, without requiring any manual intervention by the user. This offers a considerable advantage over other models that require complete datasets. To test this capability, we utilized XGBoost to forecast salinity levels in the Chao Phraya River, Thailand, based on investigating forecasts at lead times of 24 and 48 hours, using four predictors: the water levels from three stations and the salinity level. The tested model demonstrated high predictive performance across both complete and incomplete subsets of the data. Where the complete input dataset was available, our XGBoost model outperformed the single-level ANN model and was comparable to the multilevel ANN model. Additionally, we enhanced XGBoost's ability to forecast under incomplete data conditions by utilizing both real and synthetic datasets. The synthetic dataset was generated by removing 1–3 predictor variables from the complete dataset. Based on the results, incorporating synthetic data into the training dataset substantially enhanced the model's robustness against missing data. Notably, training with synthetic data that excluded one variable at a time was sufficient for accurate predictions. These results underscore XGBoost's practicality and reliability for real-world water quality forecasting, particularly under conditions of data scarcity.
- XGBoost,
- missing data,
- salinity forecasting,
- Chao Phraya River
Citation: Jiramate Changklom, Trang Prommana, Phakawat Lamchuan, Adichai Pornprommin. Salinity forecasting in Chao Phraya River using XGBoost with missing data handling[J]. AIMS Environmental Science, 2025, 12(5): 916-935. doi: 10.3934/environsci.2025040

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Abstract

Machine learning models for water quality prediction often encounter challenges due to incomplete datasets resulting from issues such as sensor failures or data transmission errors. However, XGBoost can learn from incomplete data and make predictions even when some input values are missing, without requiring any manual intervention by the user. This offers a considerable advantage over other models that require complete datasets. To test this capability, we utilized XGBoost to forecast salinity levels in the Chao Phraya River, Thailand, based on investigating forecasts at lead times of 24 and 48 hours, using four predictors: the water levels from three stations and the salinity level. The tested model demonstrated high predictive performance across both complete and incomplete subsets of the data. Where the complete input dataset was available, our XGBoost model outperformed the single-level ANN model and was comparable to the multilevel ANN model. Additionally, we enhanced XGBoost's ability to forecast under incomplete data conditions by utilizing both real and synthetic datasets. The synthetic dataset was generated by removing 1–3 predictor variables from the complete dataset. Based on the results, incorporating synthetic data into the training dataset substantially enhanced the model's robustness against missing data. Notably, training with synthetic data that excluded one variable at a time was sufficient for accurate predictions. These results underscore XGBoost's practicality and reliability for real-world water quality forecasting, particularly under conditions of data scarcity.

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