APPLICATION OF MULTIVARIATE ANFIS FOR DAILY RAINFALL PREDICTION: INFLUENCES OF TRAINING DATA SIZE

Authors

Edvin Edvin, Agency for the Assessment and Application of Technology (BPP Teknologi), Jakarta 10340, IndonesiaFollow
Yudha Djamil Djamil, The INSIDE Project Research Associates, Agency for the Assessment and Application of Technology (BPP Teknologi), Jakarta 10340, Indonesia

Abstract

This study investigates the use of multi variable Adaptive Neuro Fuzzy Inference System (ANFIS) in predicting daily rainfall using several surface weather parameters as predictors. The data used in this study comes from automatic weather station data collected in Timika airport from January until July 2005 with 15-minute time interval. We found out that relative humidity is the best predictor with a stable performance regardless of training data size and low RMSE amount especially in comparison to those from other predictors. Other predictors shows no consistent performances with different training data size. Performances of ANFIS reach a slightly above 0.6 in correlation values for daily rainfall data without any filtering for up to 100 data in a time series. The performance of ANFIS is sensitive to the magnitude and scale differences among predictors, thus suggesting introducing a transforming and scaling factor or functions. Application of multivariate ANFIS is relatively new in Indonesia. However, results presented here indicate some promises and possible roadmaps for improvements.

References

[1] BMG, Analisis Iklim di Area Kontrak Karya PT. Freeport Indonesia, Pusat Analisa dan Pengolahan Data, Badan Meteorologi dan Geofisika, Jakarta, 2001. [2] Indragustari, Lokakarya Nasional Forum Prakiraan, Evaluasi dan Validasi, Jakarta, 2005. [3] T.H. Liong, Pelatihan Aplikasi Matlab Untuk Meteorologi, Bandung, 2005. [4] Z.L. Dupe, Noersomadi, Lokakarya Nasional Forum Prakiraan, Evaluasi dan Validasi, Jakarta, 2005. [5] F.H. Widodo, J. Arifian, M. Kudsy, S. Tikno, S. Nuryanto, J. Sains dan Teknologi Modifikasi Cuaca (2004) 5, 10 – 21 [6] J.S.R. Jang, ANFIS: Adaptive-Network-Based Fuzzy Inference System, IEEE Transactions on Systems, Man and Cybernetics (1993) 23, 665-685.

Recommended Citation

Edvin, Edvin and Djamil, Yudha Djamil (2018) "APPLICATION OF MULTIVARIATE ANFIS FOR DAILY RAINFALL PREDICTION: INFLUENCES OF TRAINING DATA SIZE," Makara Journal of Science: Vol. 12: Iss. 1, Article 5.
Available at: https://scholarhub.ui.ac.id/science/vol12/iss1/5