Evaluating the Effectiveness of TBaWI for Imputation of Missing Rainfall Data - Syafie, Lukman; Awangga, Narendra; Salim, Yulita (Prodi Teknik Informatika FIK Universitas Muslim Indonesia, 2026)
Daily rainfall data plays an important role in hydrological and climatological analysis, especially in tropical regions characterised by high rainfall variability and sharp seasonal changes. However, observational data often has gaps, which can reduce model accuracy and obscure relevant climatological signals. This study addresses these issues by applying the Trend-Based Adaptive Window Imputation (TBaWI) method, an adaptive imputation approach that considers local temporal trends and seasonal dynamics in estimating missing rainfall values. This method was tested using CHIRPS data for the Makassar region for the period 2014–2023 with synthetic data loss scenarios of 10%, 15%, 20%, and 25%. The results show that TBaWI consistently provides a lower Mean Absolute Error (MAE) value, namely 6.14–7.65 mm, compared to linear interpolation, which produces 6.46–7.75 mm. The SMAPE value of TBaWI is also lower, for example 33.16% in the 15% data loss scenario, compared to interpolation at 35.06%. In addition, this method showed an improvement in the ability to identify dry days through the Zero Hit Rate (ZHR), which reached 60.08% in the 20% data loss scenario, higher than the interpolation of 58.32%, while the Rainy Hit Rate (RHR) remained in a stable range of 79–88%. These findings indicate that TBaWI is more effective in maintaining climatological consistency and numerical accuracy of tropical rainfall data. Further research is expected to integrate spatial aspects and optimise machine learning-based parameters to improve the generalisation of the method under various climatic conditions.