Multiple imputations by chained equations for recovering missing daily streamflow observations: a case study of Langat River basin in Malaysia

ABSTRACT

Missing values in hydrological studies are a common issue for hydrologists, especially in statistical analyses as a complete dataset is required. This work evaluates the performance of the multiple imputations by chained equations (MICE) approach to predicting recurrence in streamflow datasets. To evaluate and verify the effectiveness of the MICE approach in treating missing streamflow data, complete historical daily streamflow data from 2012 to 2014 were used. Later, MICE methods coupled with multiple linear regression (MLR) were applied to restore streamflow rates in Malaysia’s Langat River basin from 1978 to 2016. The best estimation methods are validated with tests such as adjusted R-squared (Adj R²), residual standard error (RSE), and mean absolute percentage error (MAPE). The findings revealed that the classification and regression tree (CART) method combined with MLR outperformed the other approaches tested, with the highest Adj R² value and the lowest RSE and MAPE values observed regardless of missing conditions.

EDITOR:

• A. Castellarin

ASSOCIATE EDITOR:

• A. Castellarin & A. Requena

KEYWORDS:

• missing data
• multiple imputations
• chained equations
• streamflow
• CART
• MICE

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Geran Universiti Penyelidikan GUP-2020-013.