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
收藏
页码：829 / 837
页数：9
相关论文
共 50 条
  • [41] Short-Term Traffic Prediction Using Long Short-Term Memory Neural Networks
    Abbas, Zainab
    Al-Shishtawy, Ahmad
    Girdzijauskas, Sarunas
    Vlassov, Vladimir
    2018 IEEE INTERNATIONAL CONGRESS ON BIG DATA (IEEE BIGDATA CONGRESS), 2018, : 57 - 65
  • [42] A data-driven approach based on long short-term memory and hidden Markov model for crack propagation prediction
    Nguyen-Le, Duyen H.
    Tao, Q. B.
    Vu-Hieu Nguyen
    Abdel-Wahab, Magd
    Nguyen-Xuan, H.
    ENGINEERING FRACTURE MECHANICS, 2020, 235
  • [43] Streamflow and rainfall forecasting by two long short-term memory-based models
    Ni, Lingling
    Wang, Dong
    Singh, Vijay P.
    Wu, Jianfeng
    Wang, Yuankun
    Tao, Yuwei
    Zhang, Jianyun
    JOURNAL OF HYDROLOGY, 2020, 583
  • [44] Deep Bi-directional Long Short-Term Memory Model for Short-Term Traffic Flow Prediction
    Wang, Jingyuan
    Hu, Fei
    Li, Li
    NEURAL INFORMATION PROCESSING, ICONIP 2017, PT V, 2017, 10638 : 306 - 316
  • [45] Niederschlags-Abfluss-Modellierung mit Long Short-Term Memory (LSTM)Rainfall-Runoff modeling with Long Short-Term Memory Networks (LSTM)—an overview
    Frederik Kratzert
    Martin Gauch
    Grey Nearing
    Sepp Hochreiter
    Daniel Klotz
    Österreichische Wasser- und Abfallwirtschaft, 2021, 73 (7-8) : 270 - 280
  • [46] Prediction of Dissolved Oxygen Factor at Oncheon Stream Watershed Using Long Short-Term Memory Algorithm
    Lim, Heesung
    Shin, Hyungjin
    Lee, Jaenam
    Do, Jongwon
    Song, Inhyeok
    Jin, Youngkyu
    WATER, 2024, 16 (17)
  • [47] A new training algorithm for long short-term memory artificial neural network based on particle swarm optimization
    Cansu, Turan
    Kolemen, Emine
    Karahasan, Ozlem
    Bas, Eren
    Egrioglu, Erol
    GRANULAR COMPUTING, 2023, 8 (06) : 1645 - 1658
  • [48] Animal Behavior Prediction with Long Short-Term Memory
    Roberts, Henry
    Segev, Aviv
    2020 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2020, : 4157 - 4164
  • [49] Bridge acceleration data denoising based on long short-term memory
    Zhou, Xiaohang
    Zeng, Lingfeng
    Cui, Ranting
    Zhu, Xuan
    MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2025, 224
  • [50] Effective long short-term memory with differential evolution algorithm for electricity price prediction
    Peng, Lu
    Liu, Shan
    Liu, Rui
    Wang, Lin
    ENERGY, 2018, 162 : 1301 - 1314
12345