Perfomance Evaluation of Multi Layer Perceptron Algorithm in Rainfall Prediction Based on AWS Station Topography Characteristics
DOI:
https://doi.org/10.30871/jaic.v10i3.12605Keywords:
AWS, Machine Learning, MLP, Performance, RainfallAbstract
Accurate rainfall prediction is crucial for mitigating hydrometeorological disasters in Indonesia, where weather patterns are highly volatile and heavily influenced by topography. This study evaluates the performance of MLP algorithm in predicting rainfall intensity using high-resolution AWS data. The evaluation compares model accuracy across three contrasting topographical characteristics: coastal area (Serang Maritime Station), open lowland area (Cengkareng Meteorological Station), and mountainous area (Citeko Meteorological Station). The MLP model utilized historical rainfall, temperature differences, and humidity differences as input variables, trained over a three-year dataset (2022-2024). The results indicate that the MLP model possesses high nowcasting capabilities with an average MAE of 0.29 mm. The coastal area yielded the best prediction performance with RMSE 1.10 mm and MAE 0.22 mm, due to stable wind circulation patterns. Conversely, the open lowland area showed the highest RMSE of 2.14 mm due to sudden extreme rainfall spikes, while the mountainous region proved the most challenging to model with MAE 0.34 mm, due to highly dynamic orographic effects. Furthermore, the study identifies a structural limitation where the MLP underestimates extreme peak amplitudes, caused by data sparsity and the conservative nature of the MSE loss function. Ultimately, while the MLP is highly reliable as an early warning system for light to moderate rainfall, it requires further architectural modifications to accurately estimate extreme heavy rainfall.
Downloads
References
[1] K. Calvin et al., “IPCC, 2023: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, H. Lee and J. Romero (eds.)]. IPCC, Geneva, Switzerland.,” Jul. 2023. doi: 10.59327/IPCC/AR6-9789291691647.
[2] E. Savitri, N. Wahyuningrum, H. Y. S. H. Nugroho, N. Sudiana, Y. Indrajaya, and N. Christanto, “Identification of the rain gauge stations for the participatory flood and landslide mitigation in the Serayu river basin, Central Java,” in IOP Conference Series: Earth and Environmental Science, Institute of Physics, 2022. doi: 10.1088/1755-1315/1109/1/012007.
[3] R. O. Imhoff, C. C. Brauer, A. Overeem, A. H. Weerts, and R. Uijlenhoet, “Spatial and Temporal Evaluation of Radar Rainfall Nowcasting Techniques on 1,533 Events,” Water Resour. Res., vol. 56, no. 8, Aug. 2020, doi: 10.1029/2019WR026723.
[4] R. Droździoł and D. Absalon, “Evaluation of Selected Amateur Rain Gauges with Hellmann Rain Gauge Measurements,” Climate, vol. 11, no. 5, p. 107, May 2023, doi: 10.3390/cli11050107.
[5] A. Kurniawan, “Evaluasi Pengukuran Curah Hujan Antara Hasil Pengukuran Permukaan (AWS, HELLMAN, OBS) dan Hasil Estimasi (Citra Satelit =GSMaP) Di Stasiun Klimatologi Mlati Tahun 2018,” Jurnal Geografi, Edukasi dan Lingkungan (JGEL), vol. 4, no. 1, pp. 1–7, Jan. 2020, doi: 10.29405/jgel.v4i1.3797.
[6] O. V. Petrochenko, “The problem of flooding and analysis of the ways of its solution,” Environmental safety and natural resources, vol. 46, no. 2, pp. 5–22, Jun. 2023, doi: 10.32347/2411-4049.2023.2.5-22.
[7] A. F. Radjab, H. Akib, Rifdan, and Jasruddin, “Partnership in Weather Observation using the Crowdsourcing Method,” IOP Conf. Ser. Earth Environ. Sci., vol. 499, no. 1, p. 012019, Jun. 2020, doi: 10.1088/1755-1315/499/1/012019.
[8] N. Ananda, H. Hartanto, and D. Kurniadi, “Preliminary Evaluation of Weather Radar Rainfall Estimation in Bandung City,” in 2023 8th International Conference on Instrumentation, Control, and Automation (ICA), IEEE, Aug. 2023, pp. 76–80. doi: 10.1109/ICA58538.2023.10273091.
[9] A. I. Pathan et al., “Comparative assessment of rainfall-based water level prediction using machine learning (ML) techniques,” Ain Shams Engineering Journal, vol. 15, no. 7, p. 102854, Jul. 2024, doi: 10.1016/j.asej.2024.102854.
[10] S. Soekirno et al., “Design of drought early warning system based on standardized precipitation index prediction using hybrid ARIMA-MLP in Banten province,” IAES International Journal of Artificial Intelligence (IJ-AI), vol. 13, no. 2, p. 1878, Jun. 2024, doi: 10.11591/ijai.v13.i2.pp1878-1887.
[11] Y. Dai et al., “Variations in Present and Future Hourly Extreme Rainfall: Insights from High-Resolution Data and Novel Temporal Disaggregation Model,” Water (Basel)., vol. 16, no. 23, p. 3463, Dec. 2024, doi: 10.3390/w16233463.
[12] S. Haribabu, G. S. Gupta, P. N. Kumar, and P. S. Rajendran, “Prediction of Flood by Rainf All Using MLP Classifier of Neural Network Model,” in 2021 6th International Conference on Communication and Electronics Systems (ICCES), IEEE, Jul. 2021, pp. 1360–1365. doi: 10.1109/ICCES51350.2021.9489161.
[13] Y. Wang, J. Zhang, and Y. Chang, “A probability prediction model for flood disasters based on Multi-layer Perceptron,” J. Phys. Conf. Ser., vol. 2905, no. 1, p. 012003, Nov. 2024, doi: 10.1088/1742-6596/2905/1/012003.
[14] S. W. Powell, , Robert A. Houze, and S. R. Brodzik, “Rainfall-Type Categorization of Radar Echoes Using Polar Coordinate Reflectivity Data,” J. Atmos. Ocean. Technol., vol. 33, no. 3, pp. 523–538, Mar. 2016, doi: 10.1175/JTECH-D-15-0135.1.
[15] Hartanto, S. Humaidi, E. Frida, M. Sinambela, and N. Ananda, “Evaluation of Meteorological Radar Precipitation Forecast in Banten,” in 2023 International Conference on Information Technology and Computing (ICITCOM), IEEE, Dec. 2023, pp. 297–300. doi: 10.1109/ICITCOM60176.2023.10442051.
[16] M. Vijaya Chitra and G. Jacob, “Using Sliding Window Algorithm for Rainfall Forecasting,” in Proceedings of International Conference on Artificial Intelligence, Smart Grid and Smart City Applications, Cham: Springer International Publishing, 2020, pp. 333–342. doi: 10.1007/978-3-030-24051-6_32.
[17] Hartanto, S. Humaidi, E. Frida, N. Ananda, and M. Sinambela, “Spatial evaluation rainfall estimation on weather radar using Marshall–Palmer reflectivity–rainfall rate in Banten,” Journal of Water and Land Development, pp. 193–200, Oct. 2024, doi: 10.24425/jwld.2024.151567.
[18] S. van Buuren and K. Groothuis-Oudshoorn, “MICE: Multivariate Imputation by Chained Equations in R,” J. Stat. Softw., vol. 45, no. 3, 2011, doi: 10.18637/jss.v045.i03.
[19] S. Shu, H. Xu, and W. Zhang, “Convective-Stratiform Rainfall of Typhoon Fitow (2013): Sensitivity to Rainfall Partitioning Methods,” Journal of Geophysical Research: Atmospheres, vol. 125, no. 3, Feb. 2020, doi: 10.1029/2019JD031510.
[20] K. G, Y. Sushmitha, K. L. Saranya, P. Naga Ramya Sri, and P. Amulya, “Rainfall Prediction Using Deep Learning and Machine Learning Techniques,” in 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI), IEEE, May 2023, pp. 1–7. doi: 10.1109/ACCAI58221.2023.10199905.
[21] X. Huang, L. Gao, R. S. Crosbie, N. Zhang, G. Fu, and R. Doble, “Groundwater recharge prediction using linear regression, multi-layer perception network, and deep learning,” Water (Switzerland), vol. 11, no. 9, 2019, doi: 10.3390/w11091879.
[22] R. Ramezanian, A. Peymanfar, and S. B. Ebrahimi, “An integrated framework of genetic network programming and multi-layer perceptron neural network for prediction of daily stock return: An application in Tehran stock exchange market,” Applied Soft Computing Journal, vol. 82, Sep. 2019, doi: 10.1016/j.asoc.2019.105551.
[23] D. Chicco, M. J. Warrens, and G. Jurman, “The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation,” PeerJ Comput. Sci., vol. 7, pp. 1–24, 2021, doi: 10.7717/PEERJ-CS.623.
[24] WMO, “Guide to Instruments and Methods of Observation WMO-No. 8,” Geneva, 2021.
[25] B. Sevruk, M. Ondrás, and B. Chvíla, “The WMO precipitation measurement intercomparisons,” Atmos. Res., vol. 92, no. 3, pp. 376–380, May 2009, doi: 10.1016/j.atmosres.2009.01.016.
[26] A. Abdelhalim, M. Rico-Ramirez, W. Liu, and D. Han, “Advancing Spatiotemporal Rainfall Nowcasting through Deep Learning Techniques,” Jan. 20, 2025. doi: 10.5194/egusphere-egu24-19377.
[27] A. Shukla, M. Sharma, K. Tiwari, V. D. Vani, N. Kumar, and Pooja, “Predicting Rainfall Using an Artificial Neural Network-Based Model,” in 2023 6th International Conference on Contemporary Computing and Informatics (IC3I), IEEE, Sep. 2023, pp. 2700–2704. doi: 10.1109/IC3I59117.2023.10397714.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Taswanda Taryo, Sajarwo Anggai , Hartanto Hartanto, Naufal Naufal
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) ) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
