Comparison of Monthly Rainfall Prediction using Long Short Term Memory and Multi Layer Perceptron Methods in South Tangerang City

GA Monang Lumban Gaol(1*), Mohammad Syafrullah(2), Supardi Supardi(3)

(1) Universitas Budi Luhur
(2) Universitas Budi Luhur
(3) ISB Atma Luhur
(*) Corresponding Author

Abstract


Rainfall is one of the meteorological and climatological parameters whose information must be disseminated to the public and related stakeholders. Rainfall information has an important role in the sectors of people's lives. In agriculture, the amount of rainfall has an important role in determining the planting season, so that this can prevent potential crop failure. On Disaster, South Tangerang City during the 2016-2021 period experienced floods, landslides, and droughts. Therefore, the importance of rainfall prediction information can improve meteorological and climatological information services in various sectors. Nevertheless, it is still difficult for the community and stakeholders to get monthly rainfall predictions with high accuracy in the long term. In this research, monthly rainfall prediction is designed using MLP (Multi Layer Perceptron) and LSTM (Long Short Term Memory). The data used is the monthly rainfall data of Climate Hazards Group InfraRed Precipitations (CHIRPS) for 42 years (period 1981-2022) with coordinate boundaries according to the research location, namely South Tangerang City, which is located between 106.625 º - 106.825 º East and 6.4 ° - 6.2 ° LS as many as 16 grids with a resolution of 0.05 ° each grid. Monthly rainfall prediction using MLP produces an RMSE value of 90.19, and a MAPE of 40.55, while the LSTM method produces an RMSE value of 88.12 and a MAPE of 40.49. Monthly rainfall prediction results using the LSTM method are better than the MLP method; this can be seen from the RMSE value of the LSTM method is smaller than MLP.


Keywords


rainfall, prediction, LSTM, MLP

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References


Herlina, N., & Prasetyorini, A. (2020). Pengaruh Perubahan Iklim pada Musim Tanam dan Produktivitas Jagung (Zea mays L.) di Kabupaten Malang (Effect of Climate Change on Planting Season and Productivity of Maize (Zea mays L.) in Malang Regency). Jurnal Ilmu Pertanian Indonesia (JIPI), Januari, 25(1), 118–128. https://doi.org/10.18343/jipi.25.1.118

Sainstek, J., & Pekanbaru, S. (2021). SAINSTEK (e-Journal) Analisis Kondisi Atmosfer Saat Banjir dan Tanah Longsor (Studi Kasus : Nganjuk, 14 Februari 2021) INFORMASI ARTIKEL ABSTRACT.

Funk, C.C., Peterson, P.J., Landsfeld, M.F., Pedreros, D.H., Verdin, J.P., Rowland, J.D., Romero, B.E., Husak, G.J., Michaelsen, J.C. and Verdin, A.P., 2014. A quasi-global precipitation time series for drought monitoring. US Geological Survey data series, 832(4), pp.1-12.

Huang, X., Gao, L., Crosbie, R. S., Zhang, N., Fu, G., & Doble, R. (2019). Groundwater recharge prediction using linear regression, multi-layer perception network, and deep learning. Water (Switzerland), 11(9). https://doi.org/10.3390/w11091879.

Wibawa AP, Utama AB, Elmunsyah H, Pujianto U, Dwiyanto FA, Hernandez L. Time-series analysis with smoothed Convolutional Neural Network. Journal of big Data. 2022 Apr 26;9(1):44.

Prasetyo B, Pusparini N. Pemanfaatan SATAID Untuk Analisa Atmosfer di Wilayah Perairan. Jurnal Fisika dan Aplikasinya. 2018 Jun 1;14(2):37-44.

Kafara Z, Rumlawang FY, Sinay LJ. Peramalan Curah Hujan Dengan Pendekatan Seasonal Autoregressive Integrated Moving Average (Sarima). Barekeng: Jurnal Ilmu Matematika dan Terapan. 2017 Mar 1;11(1):63-74.

Bilgili M, Sahin B. Comparative analysis of regression and artificial neural network models for wind speed prediction. Meteorology and atmospheric physics. 2010 Nov;109:61-72.

Lin Z, Feng J, Lu Z, Li Y, Jin D. Deepstn+: Context-aware spatial-temporal neural network for crowd flow prediction in metropolis. InProceedings of the AAAI conference on artificial intelligence 2019 Jul 17 (Vol. 33, No. 01, pp. 1020-1027).

Szandala, Tomasz. (2021). Review and Comparison of Commonly Used Activation Functions for Deep Neural Networks. In Bio-inspired Neurocomputing, 203-224. Springer, Singapore

Sahu RK, Müller J, Park J, Varadharajan C, Arora B, Faybishenko B, Agarwal D. Impact of input feature selection on groundwater level prediction from a multi-layer perceptron neural network. Frontiers in Water. 2020 Nov 19;2:573034.




DOI: https://doi.org/10.32736/sisfokom.v13i2.2149

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