Assessment Of IDW And ANN On Daily Rainfall Data Imputation in Semarang Central Java
DOI:
10.33395/sinkron.v9i1.14452Keywords:
Rainfall; Imputation; IDW; ANN; Missing DataAbstract
Rainfall plays a critical role in the global water and energy cycle, influencing surface water availability and recharge processes both spatially and temporally. Traditional rainfall data collection using ombrometers provides accurate live data, but often faces the challenge of missing data due to equipment failure or transmission, especially in agencies such as BMKG. This problem of missing data greatly impacts hydrological analysis and requires an effective data recovery process through imputation. This study aims to assess the accuracy of rainfall data imputation techniques using the Inverse Distance Weighting (IDW) and Artificial Neural Network (ANN) methods. In this study, we utilize data from 31 observation stations in Semarang City for more than three decades. The findings show that the spatial distribution of rainfall is variable and exhibits a cyclic pattern despite fluctuations. The ANN model performed very well in overcoming missing data, especially in the dry season with an RMSE of 0.9489 and a coefficient of determination (R2) of 0.9926. By demonstrating the superiority of the ANN model in accurately predicting rainfall, this study offers an effective approach to improve the quality of BMKG climate data. This is expected to support disaster mitigation decisions and sustainable development planning. This approach demonstrates that the selection of an appropriate method is critical for accurate and reliable analysis of rainfall time series data. In addition to making an academic contribution, these results also provide an alternative imputation method for various time series.
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Abdelouahed, S. M., Abla, R., Asmae, E., & Abdellah, A. (2024). Harnessing feature engineering to improve machine learning: A review of different data processing techniques. 2024 International Conference on Intelligent Systems and Computer Vision (ISCV), 1–6. https://doi.org/10.1109/ISCV60512.2024.10620105
Addi, M., Gyasi-Agyei, Y., Obuobie, E., & Amekudzi, L. K. (2022). Evaluation of imputation techniques for infilling missing daily rainfall records on river basins in Ghana. Hydrological Sciences Journal, 67(4). https://doi.org/10.1080/02626667.2022.2030868
Agrawal, J. Das. (2023). ANN in forecasting Missing Rainfall Data. E3S Web of Conferences, 405, 04017. https://doi.org/10.1051/e3sconf/202340504017
Azman, A. H., Tukimat, N. N. A., & Malek, M. A. (2021). Comparison of Missing Rainfall Data Treatment Analysis at Kenyir Lake. IOP Conference Series: Materials Science and Engineering, 1144(1), 012046. https://doi.org/10.1088/1757-899X/1144/1/012046
Castillo-Gómez, J. S. Del, Canchala, T., Torres-López, W. A., Carvajal-Escobar, Y., & Ocampo-Marulanda, C. (2023). Estimation of monthly rainfall missing data in Southwestern Colombia: comparing different methods. RBRH, 28. https://doi.org/10.1590/2318-0331.282320230008
Chiu, P. C., Selamat, A., Krejcar, O., & Kuok, K. K. (2019). Missing rainfall data estimation using artificial neural network and nearest neighbor imputation. Frontiers in Artificial Intelligence and Applications, 318. https://doi.org/10.3233/FAIA190044
Chiu, P. C., Selamat, A., Krejcar, O., Kuok, K. K., Herrera-Viedma, E., & Fenza, G. (2021). Imputation of rainfall data using the sine cosine function fitting neural network. International Journal of Interactive Multimedia and Artificial Intelligence, 6(7). https://doi.org/10.9781/ijimai.2021.08.013
Costa, R. L., Barros Gomes, H., Cavalcante Pinto, D. D., da Rocha Júnior, R. L., dos Santos Silva, F. D., Barros Gomes, H., Lemos da Silva, M. C., & Luís Herdies, D. (2021). Gap Filling and Quality Control Applied to Meteorological Variables Measured in the Northeast Region of Brazil. Atmosphere, 12(10), 1278. https://doi.org/10.3390/atmos12101278
Demetris Koutsoyiannis. (2021). Advances in stochastics of hydroclimatic extremes. https://doi.org/10.13140/RG.2.2.30655.05282/1
Djerbouai, S. (2022). Missing Precipitation Data Estimation Using Long Short-Term Memory Deep Neural Networks. Journal of Ecological Engineering, 23(5), 216–225. https://doi.org/10.12911/22998993/147322
Jahan, F., Sinha, N. C., Rahman, M. M., Rahman, M. M., Mondal, M. S. H., & Islam, M. A. (2019). Comparison of missing value estimation techniques in rainfall data of Bangladesh. Theoretical and Applied Climatology, 136(3–4). https://doi.org/10.1007/s00704-018-2537-y
Li, C., Ren, X., & Zhao, G. (2023). Machine-Learning-Based Imputation Method for Filling Missing Values in Ground Meteorological Observation Data. Algorithms, 16(9), 422. https://doi.org/10.3390/a16090422
Marcelino, C. G., Leite, G. M. C., Celes, P., & Pedreira, C. E. (2022). Missing Data Analysis in Regression. Applied Artificial Intelligence, 36(1), 2032925. https://doi.org/10.1080/08839514.2022.2032925
Miró, J. J., Caselles, V., & Estrela, M. J. (2017). Multiple imputation of rainfall missing data in the Iberian Mediterranean context. Atmospheric Research, 197. https://doi.org/10.1016/j.atmosres.2017.07.016
Mital, U., Dwivedi, D., Brown, J. B., Faybishenko, B., Painter, S. L., & Steefel, C. I. (2020). Sequential Imputation of Missing Spatio-Temporal Precipitation Data Using Random Forests. Frontiers in Water, 2. https://doi.org/10.3389/frwa.2020.00020
Mohamad, N. B., Lai, A.-C., & Lim, B.-H. (2022). A case study in the tropical region to evaluate univariate imputation methods for solar irradiance data with different weather types. Sustainable Energy Technologies and Assessments, 50, 101764. https://doi.org/10.1016/j.seta.2021.101764
Navarro Céspedes, J. M., Hernández, J. H., Alcántara Concepción, P. C., Morales Martínez, J. L., Carreño Aguilera, G., & Padilla Benítez, F. (2022). A comparison of missing values imputation methods applied to precipitation of two semi-arid and humid regions of México. ATMÓSFERA. https://doi.org/10.20937/ATM.53095
Norazizi, N. A. A., & Deni, S. M. (2019). Comparison of Artificial Neural Network (ANN) and Other Imputation Methods in Estimating Missing Rainfall Data at Kuantan Station. In M. W. Berry, B. W. Yap, A. Mohamed, & M. Köppen (Eds.), Soft Computing in Data Science (pp. 298–306). Springer. https://doi.org/10.1007/978-981-15-0399-3_24
Oktaviani, I. D., & Putrada, A. G. (2022). KNN imputation to missing values of regression-based rain duration prediction on BMKG data. JURNAL INFOTEL, 14(4), 249–254. https://doi.org/10.20895/infotel.v14i4.840
Sa’adi, Z., Yusop, Z., Alias, N. E., Chow, M. F., Muhammad, M. K. I., Ramli, M. W. A., Iqbal, Z., Shiru, M. S., Rohmat, F. I. W., Mohamad, N. A., & Ahmad, M. F. (2023). Evaluating Imputation Methods for rainfall data under high variability in Johor River Basin, Malaysia. Applied Computing and Geosciences, 20, 100145. https://doi.org/10.1016/j.acags.2023.100145
Sahoo, A., & Ghose, D. K. (2022). RETRACTED ARTICLE: Imputation of missing precipitation data using KNN, SOM, RF, and FNN. Soft Computing, 26(12), 5919–5936. https://doi.org/10.1007/s00500-022-07029-4
Saputra, M. D., Hadi, A. F., Riski, A., & Anggraeni, D. (2021). Principal Component Regression in Statistical Downscaling with Missing Value for Daily Rainfall Forecasting. International Journal of Quantitative Research and Modeling, 2(3), 139–146. https://doi.org/10.46336/ijqrm.v2i3.151
Varada Rajkumar, K., & Subrahmanyam, D. K. (2021). A Novel Method for Rainfall Prediction and Classification using Neural Networks. International Journal of Advanced Computer Science and Applications, 12(7). https://doi.org/10.14569/IJACSA.2021.0120760
Wangwongchai, A., Waqas, M., Dechpichai, P., Hlaing, P. T., Ahmad, S., & Humphries, U. W. (2023). Imputation of missing daily rainfall data; A comparison between artificial intelligence and statistical techniques. MethodsX, 11, 102459. https://doi.org/10.1016/j.mex.2023.102459
Wuthiwongyothin, S., Kalkan, C., & Panyavaraporn, J. (2021). Evaluating Inverse Distance Weighting and Correlation Coefficient Weighting Infilling Methods on Daily Rainfall Time Series. SNRU Journal of Science and Technology, 13(2).
Zhang, Y., Zhou, B., Cai, X., Guo, W., Ding, X., & Yuan, X. (2021). Missing value imputation in multivariate time series with end-to-end generative adversarial networks. Information Sciences, 551. https://doi.org/10.1016/j.ins.2020.11.035
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