Rainfall Prediction Using Adaptive Neuro-Fuzzy Inference System Method

Authors

Dedy Ardana , Irwandi , Umar Muksin , Mochammad Vicky Idris

DOI:

10.29303/jppipa.v11i2.10148

Published:

2025-02-28

Issue:

Vol. 11 No. 2 (2025): February

Keywords:

ANFIS, Artificial Intelligence, Disaster Mitigation, Extreme Rainfall, Rainfall, Residual, RMSE, STEM, Weather Prediction

Research Articles

Downloads

How to Cite

Ardana, D., Irwandi, I., Muksin, U., & Idris, M. V. (2025). Rainfall Prediction Using Adaptive Neuro-Fuzzy Inference System Method. Jurnal Penelitian Pendidikan IPA, 11(2), 593–601. https://doi.org/10.29303/jppipa.v11i2.10148

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Abstract

This study analyzes rainfall prediction using the Adaptive Neuro-Fuzzy Inference System (ANFIS) method to improve model accuracy, particularly in extreme rainfall events. The objective of this study is to evaluate rainfall prediction using the ANFIS method to enhance model accuracy, especially in predicting extreme rainfall occurrences. The results indicate a moderate positive correlation with R² of 0.55, demonstrating good model performance at low rainfall levels (<20 mm) but a tendency to underestimate high-intensity rainfall (>60 mm). Residual analysis reveals a distribution around zero without systematic bias, though significant outliers (>20 or <-20) suggest the need for accuracy improvement. Monthly RMSE exhibits fluctuations, with the best performance observed in the June-July-August (JJA) season and notable challenges in December-January-February (DJF) due to extreme variability. Annual RMSE is also higher in extreme rainfall years (2018, 2023) compared to stable years (2019, 2020). The implementation of ANFIS enhances prediction sensitivity by incorporating additional variables such as temperature and humidity, leading to more accurate forecasts, particularly in extreme weather conditions. This study is further supported by STEM research at Universitas Syiah Kuala, which emphasizes the importance of artificial intelligence in climate data analysis to improve weather prediction accuracy. The ANFIS-based approach applied in this research aligns with  STEM studies, which highlight the integration of artificial intelligence in meteorology to mitigate hydrometeorological disaster risks

References

Abushariah, A., & Gao, W. (2022). ANFIS and neural networks for short-term wind speed prediction. Wind Energy, 25(10), 844–855. https://doi.org/10.1002/we.2687

Alrahabi, A., & Qader, S. (2023). Application of neuro-fuzzy models in predicting land surface temperature. Energy Reports, 9, 1023–1036. https://doi.org/10.1016/j.egyr.2023.02.014

Castillo, O., & Melin, P. (2022). Fuzzy logic and ANFIS techniques for environmental monitoring. Environmental Monitoring and Assessment, 194(4), 1–13. https://doi.org/10.1007/s10661-022-09969-y

Chen, W., & Chang, M. (2023). Application of ANFIS and neural networks in rainfall prediction. Journal of Hydrology, 601,126725. https://doi.org/10.1016/j.jhydrol.2023.126725

Dabbagh, A., & Rahimpour, M. (2022). ANFIS and machine learning approaches in hydrology: Recent advances. Water, 14(6), 905. https://doi.org/10.3390/w14060905

Huang, H., & Liu, Y. (2022). Using ANFIS in predicting climate changes and environmental variables. Climate Dynamics, 59(1–2), 289–302. https://doi.org/10.1007/s00382-021-05802-3

Izadi, A., & Ranjbar, M. (2022). Implementation of ANFIS for short-term rainfall prediction in urban areas. Urban Climate, 44, 101209. https://doi.org/10.1016/j.uclim.2022.101209

Jha, K., & Singh, K. (2023). Hybrid ANFIS-MLR model for river flow predictions. Water Resources Management, 37(1), 763–780. https://doi.org/10.1007/s11269-022-03349

Kumar, S., & Singh, R. (2023). Forecasting of precipitation using ANFIS and time series analysis. Environmental Science and Pollution Research, 30(15), 39475–39490. https://doi.org/10.1007/s11356-022-23676-8

Lee, H., & Park, J. (2022). Integrating machine learning and fuzzy logic for agricultural yield prediction. Computers and Electronics in Agriculture, 194, 106742. https://doi.org/10.1016/j.compag.2022.106742

Nazeer, A., & Zhang, X. (2022). A comparative review of artificial neural networks and ANFIS in weather prediction. Journal of Climate, 35(4), 1711–1722. https://doi.org/10.1175/JCLI-D-21-0445.1

Ourabah, K., & Boukria, A. (2023). Application of neural networks in hydrological modeling for rainfall predictions. Hydrological Processes, 37(3), e14758. https://doi.org/10.1002/hyp.14758

Rahman, M. H., & Ali, M. (2022). Evaluation of ANFIS and neural network models for climate change impact assessment. Global Change Biology, 28(3), 1673–1686. https://doi.org/10.1111/gcb.16015

Raza, S., & Farooq, U. (2022). Rainfall prediction using ANFIS and machine learning approaches. Hydrology and Earth System Sciences, 26, 4875–4892. https://doi.org/10.5194/hess-26-4875-2022

Sen, S., & Saha, M. (2023). Evaluation of ANFIS for predicting rainfall-runoff in watersheds. Journal of Water Resources Planning and Management, 149(2),04022066. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001638

Sharma, A., & Gupta, N. (2023). Neural network approaches in meteorology: Current trends and future directions. Meteorological Applications, 30*(1), e2208. https://doi.org/10.1002/met.2208

Shi, Y., & Fan, H. (2023). A review of hybrid neural network models in predicting meteorological parameters. Atmosphere, 14(3), 243. https://doi.org/10.3390/atmos14030243

Tang, J., & Zhang, Y. (2023). Hybrid ANFIS-deep learning approaches for precipitation forecasting. Earth Science Informatics, 16(2), 687–700. https://doi.org/10.1007/s12145-023-00852-5

Vasilakos, C., & Papadopoulos, P. (2022). Using hybrid models for short-term weather prediction: A case study. Journal of Atmospheric Sciences, 79(8), 3043–3057. https://doi.org/10.1175/JAS-D-21-0187.1

Zhang, Y., & Dong, L. (2023). Comparative analysis of ANFIS and deep neural networks for weather forecasting. Atmospheric Research, 283, 105297. https://doi.org/10.1016/j.atmosres.2022.105297

Author Biographies

Dedy Ardana, Meteorology Climatology and Geophysics Agency, Banda Aceh

Irwandi, STEM Center, Universitas Syiah Kuala

Umar Muksin, Syiah Kuala University

Mochammad Vicky Idris, Syiah Kuala University

License

Copyright (c) 2025 Dedy Ardana, Irwandi, Umar Muksin, Mochammad Vicky Idris

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with Jurnal Penelitian Pendidikan IPA, agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution 4.0 International License (CC-BY License). This license allows authors to use all articles, data sets, graphics, and appendices in data mining applications, search engines, web sites, blogs, and other platforms by providing an appropriate reference. The journal allows the author(s) to hold the copyright without restrictions and will retain publishing rights without restrictions.
  2. 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 acknowledgment of its initial publication in Jurnal Penelitian Pendidikan IPA.
  3. 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).