Long Short-Term Memory Algorithm for Rainfall Prediction Based on El-Nino and IOD Data

被引：20

作者：

Haq, Dina Zatusiva ^{[1
]}

Novitasari, Dian Candra Rini ^{[1
]}

Hamid, Abdulloh ^{[1
]}

Ulinnuha, Nurissaidah ^{[1
]}

Arnita ^{[2
]}

Farida, Yuniar ^{[1
]}

Nugraheni, R. R. Diah ^{[3
]}

Nariswari, Rinda ^{[4
]}

Ilham ^{[5
]}

Rohayani, Hetty ^{[6
]}

Pramulya, Rahmat ^{[7
]}

Widjayanto, Ari ^{[8
]}

机构：

[1] UIN Sunan Ampel, Dept Math, Surabaya, Indonesia

[2] Univ Negeri Medan, Dept Math, Medan 20221, Indonesia

[3] UIN Sunan Ampel, Dept Environm Engn, Surabaya 60237, Indonesia

[4] Bina Nusantara Univ, Sch Comp Sci, Stat Dept, Jakarta 11480, Indonesia

[5] UIN Sunan Ampel, Dept Informat Syst, Surabaya 60237, Indonesia

[6] Adiwangsa Jambi Univ, Dept Informat Technol, Jambi 36125, Indonesia

[7] Univ Teuku Umar, Fac Agr, Aceh, Indonesia

[8] Meteorol Climatol & Geophys Agcy, Surabaya 60165, Indonesia

来源：

5TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND COMPUTATIONAL INTELLIGENCE 2020 | 2021年 / 179卷

关键词：

Deep Learning; Long Short-Term Memory; LSTM; Rainfall; Forecasting;

D O I：

10.1016/j.procs.2021.01.071

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Rainfall has the highest correlation with adverse natural disasters. One of them, rainfall can cause damage to the hot mud embankments in Sidoarjo, East Java, Indonesia. Therefore, in this study, rainfall prediction is carried out to anticipate the damage to the embankments. The rainfall prediction was carried out using Long Short-Term Memory (LSTM) based on rainfall parameters: El-Nino and Indian Ocean Dipole (IOD). Experiments were carried out with two schemes: the first scheme used the El-Nino and IOD parameters, while the second scheme used rainfall time series pattern. Each scheme used varied number of hidden layers, batch size, and learn drop period. The prediction results using El-Nino and IOD parameters obtained MAAPE values of 0.9644 with hidden layer, batch size and learn rate drop period values of 100, 64, and 50. The prediction results using rainfall parameters resulted in a more accurate prediction with a MAAPE value of 0.5810. The best prediction results were obtained with the number of hidden layers, batch size and learn rate drop period of 100, 32, and 150 respectively. (C) 2021 The Authors. Published by Elsevier B.V.

引用

页码：829 / 837

页数：9

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