Multivariate Time Series Forecasting Using Recurrent Neural Networks for Meteorological Data

Victor Hariadi, Ahmad Saikhu, Nurotuz Zakiya, Arya Yudhi Wijaya, Fajar Baskoro

Abstract


Rainfall is related to a number of factors that are interdependent and influenced by dynamic global time, region and climate factors. Determination of relevant predictors is important for the efficiency of the rainfall estimator model. Although some climate modeling studies in one region/country have high accuracy, this model is not necessarily suitable for other regions. Determination of predictor variables by considering spatio-temporal factors and local / global features results in a very large number of inputs. Feature selection produces minimal input so that it gets relevant predictor variables and minimizes variable redundancy. Recurrent Neural Networks is one of the artificial neural networks that can be used to predict time series data. This study aims to predict rainfall by combining the SVM classification method and the RNN method. Tests on the Perak 1 daily and monthly weather data (WMO ID: 96933) and Perak 2 Station daily and monthly data(WMO ID: 96937), showed high accuracy results with an R2 are 92.1%; 94.1%; 90.9% and 89.6%.


Keywords


Feature Selection, minimum Redundancy Maximal Relevance, Support Vector Machine, Recurrent Neural Network.

References


Aldrian, E., & Budiman, M. K. (2011). Adaptasi dan mitigasi perubahan iklim di Indonesia. Pusat Perubahan Iklim dan Kualitas Udara, Kedeputian Bidang Klimatologi, Badan Meteorologi, Klimatologi, dan Geofisika.

Ettema, J., & Aldrian, E. (2012). SPATIOTEMPORAL CHARACTERISTICS OF EXTREME RAINFALL EVENTS OVER JAVA ISLAND, INDONESIA. Indonesian Journal of Geography, 44(1), 62-86.

Humphrey, G. B., Galelli, S., Castelleti, A., Maier, H. R., Dandy, G. C., & Gibbs, M. S. (2014). A new evaluation framework for input variable selection algorithms used in environmental modelling.

Fernandes, M. V., Schmidt, A. M., & Migon, H. S. (2009). Modelling zero-inflated spatio-temporal processes. Statistical Modelling, 9(1), 3-25.

De Jay, N., Papillon-Cavanagh, S., Olsen, C., El-Hachem, N., Bontempi, G., & Haibe-Kains, B. (2013). mRMRe: an R package for parallelized mRMR ensemble feature selection. Bioinformatics, 29(18), 2365-2368.

Ortiz-García, E. G., Salcedo-Sanz, S., & Casanova-Mateo, C. (2014). Accurate precipitation prediction with support vector classifiers: A study including novel predictive variables and observational data. Atmospheric Research, 139, 128-136.

Madan, R., & SarathiMangipudi, P. (2018, August). Predicting Computer Network Traffic: A Time Series Forecasting Approach Using DWT, ARIMA and RNN. In 2018 Eleventh International Conference on Contemporary Computing (IC3) (pp. 1-5). IEEE.

Pratama, S. W., & Nhita, F. (2016, May). Implementation of local regression smoothing and fuzzy-grammatical evolution on rainfall forecasting for rice planting calendar. In 2016 4th International Conference on Information and Communication Technology (ICoICT) (pp. 1-5). IEEE.

Nhita, F., Saepudin, D., & Wisesty, U. N. (2015, December). Comparative Study of Moving Average on Rainfall Time Series Data for Rainfall Forecasting Based on Evolving Neural Network Classifier. In 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI) (pp. 112-116). IEEE.

Nisak, S. C. (2016). Seemingly Unrelated Regression Approach for GSTARIMA Model to Forecast Rain Fall Data in Malang Southern Region Districts. CAUCHY, 4(2), 57-64.

Kanigoro, B., & Salman, A. G. (2016, August). Recurrent gradient descent adaptive learning rate and momentum neural network for rainfall forecasting. In 2016 International Seminar on Application for Technology of Information and Communication (ISemantic) (pp. 23-26). IEEE.

Vasimalla, K. (2014). A survey on time series data mining. Intern. J. of Innovative Research in Computer and Communication Engineering, 2, 170-179.

Putri, L. A. A. R. (2017). SELEKSI FITUR DALAM KLASIFIKASI GENRE MUSIK. Jurnal Ilmu Komputer, 10(1), 19-26.

Saputro, D. R. S. (2009). Memprediksi Curah Hujan (Data Spatio-Temporal) dengan Metode Bayesian Network. In Proceeding of National Seminar on Research, Teaching, and Application of Mathematics and Science (pp. 37-42).

Ding, C., & Peng, H. (2005). Minimum redundancy feature selection from microarray gene expression data. Journal of bioinformatics and computational biology, 3(02), 185-205.

Dong, D., Sheng, Z., & Yang, T. (2018, November). Wind Power Prediction Based on Recurrent Neural Network with Long Short-Term Memory Units. In 2018 International Conference on Renewable Energy and Power Engineering (REPE) (pp. 34-38). IEEE.




DOI: http://dx.doi.org/10.28989/senatik.v5i0.365

Article Metrics

Abstract view : 1756 times
PDF (Bahasa Indonesia) - 783 times

Refbacks

  • There are currently no refbacks.




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

Conference SENATIK P-ISSN :2337-3881 and  E-ISSN : 2528-1666

Jumlah penggunjung = Web Analytics orang

Statistik Senatik

Flag Counter