Evaluating the Effectiveness of TBaWI for Imputation of Missing Rainfall Data


Lukman Syafie(1*); Narendra Awangga(2); Yulita Salim(3);

(1) Universitas Muslim Indonesia
(2) Universitas Muslim Indonesia
(3) Universitas Muslim Indonesia
(*) Corresponding Author

  

Abstract


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.

Keywords


Rainfall Imputation, TBaWI, Adaptive Window, CHIRPS, Tropical Rainfall, Missing Data

  
  

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doi  https://doi.org/10.33096/ilkom.v18i1.3273.97-108 		  

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M. Addi, Y. Gyasi-Agyei, E. Obuobie, And L. K. Amekudzi, “Evaluation Of Imputation Techniques For Infilling Missing Daily Rainfall Records On River Basins In Ghana,” Hydrological Sciences Journal, Vol. 67, No. 4, Pp. 613–627, Mar. 2022, Doi:10.1080/02626667.2022.2030868.

R. L. Prasetio, A. Zakaria, A. Ashruri, And S. Sumiharni, “Analisis Data Curah Hujan Yang Hilang Dengan Menggunakan Metode Normal Ratio, Inversed Square Distance, Rata - Rata Aljabar Dan Metode Modifikasi,” Jurnal Rekayasa Sipil Dan Desain, Vol. 9, No. 3, Pp. 559–570, Nov. 2021, Doi: 10.23960/Jrsdd.V9i3.1992.

A. Wangwongchai, M. Waqas, P. Dechpichai, P. T. Hlaing, S. Ahmad, And U. W. Humphries, “Imputation Of Missing Daily Rainfall Data; A Comparison Between Artificial Intelligence And Statistical Techniques,” Methodsx, Vol. 11, P. 102459, Dec. 2023, Doi: 10.1016/J.Mex.2023.102459.

V. D. Banda, R. B. Dzwairo, S. K. Singh, And T. Kanyerere, “Hydrological Modelling And Climate Adaptation Under Changing Climate: A Review With A Focus In Sub-Saharan Africa,” Water (Basel), Vol. 14, No. 24, P. 4031, Dec. 2022, Doi: 10.3390/W14244031.

F. R. Muhammad, S. W. Lubis, And S. Setiawan, “Impacts Of The Madden–Julian Oscillation On Precipitation Extremes In Indonesia,” International Journal Of Climatology, Vol. 41, No. 3, Pp. 1970–1984, Mar. 2021, Doi: 10.1002/Joc.6941.

A. Mulsandi, Y. Koesmaryono, R. Hidayat, A. Faqih, And A. Sopaheluwakan, “On The Interannual Variability Of Indonesian Monsoon Rainfall (Imr): A Literature Review Of The Role Of Its External Forcing,” Jurnal Meteorologi Dan Geofisika, Vol. 24, No. 2, Pp. 115–127, Mar. 2024, Doi: 10.31172/Jmg.V24i2.1049.

H. Darwis And F. Umar, “Precipitation Missing Data Prediction Using Recommendation System,” International Journal Of Scientific & Technology Research, Vol. 8, No. 11, 2019, [Online]. Available: Www.Ijstr.Org

Giarno Et Al., “Changing Of Return Periods Of Extreme Rainfall Using Satellite Observation In Java Island,” E3s Web Of Conferences, Vol. 464, P. 11005, Dec. 2023, Doi: 10.1051/E3sconf/202346411005.

L. Syafie, F. Umar, A. Mude, H. Darwis, Herman, And Harlinda, “Missing Data Handling Using The Naive Bayes Logarithm (Nbl) Formula,” In 2018 2nd East Indonesia Conference On Computer And Information Technology (Eiconcit), Ieee, Nov. 2018, Pp. 318–321. Doi: 10.1109/Eiconcit.2018.8878538.

R. Rodríguez Et Al., “Water-Quality Data Imputation With A High Percentage Of Missing Values: A Machine Learning Approach,” Sustainability, Vol. 13, No. 11, P. 6318, Jun. 2021, Doi: 10.3390/Su13116318.

S. M. Shaharudin, “Imputation Methods For Addressing Missing Data Of Monthly Rainfall In Yogyakarta, Indonesia,” International Journal Of Advanced Trends In Computer Science And Engineering, Vol. 9, No. 1.4, Pp. 646–651, Sep. 2020, Doi: 10.30534/Ijatcse/2020/9091.42020.

L. V. Duarte, K. T. M. Formiga, And V. A. F. Costa, “Comparison Of Methods For Filling Daily And Monthly Rainfall Missing Data: Statistical Models Or Imputation Of Satellite Retrievals?,” Water (Basel), Vol. 14, No. 19, P. 3144, Oct. 2022, Doi: 10.3390/W14193144.

Z. Sa’adi Et Al., “Evaluating Imputation Methods For Rainfall Data Under High Variability In Johor River Basin, Malaysia,” Applied Computing And Geosciences, Vol. 20, P. 100145, Dec. 2023, Doi: 10.1016/J.Acags.2023.100145.

S. Mariana Che Mat Nor, S. M. Shaharudin, S. Ismail, N. H. Zainuddin, And M. L. Tan, “A Comparative Study Of Different Imputation Methods For Daily Rainfall Data In East-Coast Peninsular Malaysia,” Bulletin Of Electrical Engineering And Informatics, Vol. 9, No. 2, Apr. 2020, Doi: 10.11591/Eei.V9i2.2090.

L. Syafie, D. Indra, dan Y. Yuhandri, “Modifikasi Rumus Bayesian Network untuk Klasifikasi Dokumen,” in Proceedings of the Seminar Ilmiah Nasional Teknologi Komputer (SENATKOM 2015), vol. 1, pp. 176–181, Padang, Okt. 2015.

G. V. Nguyen, X.-H. Le, L. Nguyen Van, D. T. T. May, S. Jung, And G. Lee, “Machine Learning Approaches For Reconstructing Gridded Precipitation Based On Multiple Source Products,” J Hydrol Reg Stud, Vol. 48, P. 101475, Aug. 2023, Doi: 10.1016/J.Ejrh.2023.101475.

B. Dey Et Al., “Monitoring Groundwater Potential Dynamics Of North-Eastern Bengal Basin In Bangladesh Using Ahp-Machine Learning Approaches,” Ecol Indic, Vol. 154, P. 110886, Oct. 2023, Doi: 10.1016/J.Ecolind.2023.110886.

O. Mudele, F. M. Bayer, L. F. R. Zanandrez, A. E. Eiras, And P. Gamba, “Modeling The Temporal Population Distribution Of Ae. Mosquito Using Big Earth Observation Data,” Ieee Access, Vol. 8, Pp. 14182–14194, 2020, Doi: 10.1109/Access.2020.2966080.

L. E. Alejo-Sanchez Et Al., “Missing Data Imputation Of Climate Time Series: A Review,” Methodsx, Vol. 15, P. 103455, Dec. 2025, Doi: 10.1016/J.Mex.2025.103455.

C. Li, X. Ren, And G. Zhao, “Machine-Learning-Based Imputation Method For Filling Missing Values In Ground Meteorological Observation Data,” Algorithms, Vol. 16, No. 9, P. 422, Sep. 2023, Doi: 10.3390/A16090422.

M. Alabadla Et Al., “Systematic Review Of Using Machine Learning In Imputing Missing Values,” Ieee Access, Vol. 10, Pp. 44483–44502, 2022, Doi: 10.1109/Access.2022.3160841.

B. O’sullivan And G. Kelly, “Efficient Likelihood And Machine‐Learning Models For Spatiotemporal Rainfall Estimation And Imputation,” International Journal Of Climatology, Aug. 2025, Doi: 10.1002/Joc.70072.

M. Toure Et Al., “Machine Learning Approaches For Imputing Missing Meteorological Data In Senegal,” Applied Computing And Geosciences, Vol. 27, P. 100281, Sep. 2025, Doi: 10.1016/J.Acags.2025.100281.

Herman Et Al., “Comparison Of Artificial Neural Network And Gaussian Naïve Bayes In Recognition Of Hand-Writing Number,” In 2018 2nd East Indonesia Conference On Computer And Information Technology (Eiconcit), Ieee, Nov. 2018, Pp. 276–279. Doi: 10.1109/Eiconcit.2018.8878651.

A. Wangwongchai, M. Waqas, P. Dechpichai, P. T. Hlaing, S. Ahmad, And U. W. Humphries, “Imputation Of Missing Daily Rainfall Data; A Comparison Between Artificial Intelligence And Statistical Techniques,” Methodsx, Vol. 11, P. 102459, Dec. 2023, Doi: 10.1016/J.Mex.2023.102459.

S. Pinthong Et Al., “Imputation Of Missing Monthly Rainfall Data Using Machine Learning And Spatial Interpolation Approaches In Thale Sap Songkhla River Basin, Thailand,” Environmental Science And Pollution Research, Vol. 31, No. 41, Pp. 54044–54060, Sep. 2022, Doi: 10.1007/S11356-022-23022-8.

G. R. Arathy Nair, S. Adarsh, A. El-Shafie, And A. N. Ahmed, “Enhancing Hydrological Data Completeness: A Performance Evaluation Of Various Machine Learning Techniques Using Probabilistic Fusion Imputer With Neural Networks For Streamflow Data Reconstruction,” J Hydrol (Amst), Vol. 639, P. 131583, Aug. 2024, Doi: 10.1016/J.Jhydrol.2024.131583.

J. Suryanto, Amprin, And Anisum, “Validasi Curah Hujan Harian Chirps Precipitation Satellite Product Di Provinsi Kalimantan Barat,” Jurnal Ilmiah Rekayasa Pertanian Dan Biosistem, Vol. 11, No. 1, Pp. 73–88, Mar. 2023, Doi: 10.29303/Jrpb.V11i1.442.

M. Shahid Et Al., “Assessing The Potential And Hydrological Usefulness Of The Chirps Precipitation Dataset Over A Complex Topography In Pakistan,” Hydrological Sciences Journal, Vol. 66, No. 11, Pp. 1664–1684, Aug. 2021, Doi: 10.1080/02626667.2021.1957476.

K. Seu, M.-S. Kang, And H. Lee, “An Intelligent Missing Data Imputation Techniques: A Review,” Joiv : International Journal On Informatics Visualization, Vol. 6, No. 1–2, P. 278, May 2022, Doi: 10.30630/Joiv.6.1-2.935.

M. Mastika, T. Siswantining, And A. Bustamam, “Comparison Of Missing Value Imputation Using Mean, Bayesian Knn, And Non-Bayesian Knn On Tep Gene Expression Data,” Media Statistika, Vol. 18, No. 1, Pp. 61–72, Oct. 2025, Doi: 10.14710/Medstat.18.1.61-72.


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