Weather forecasting using merged Long Short-Term Memory Model (LSTM) and Autoregressive Integrated Moving Average (ARIMA) Model

Afan Galih Salman, Yaya Heryadi, Edi Abdurahman, Wayan Suparta

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Weather forecasting is an interesting research problem in flight navigation area. One of the important weather data in aviation is visibility. Visibility is an important factor in all phases of flight, especially when the aircraft is maneuvering on or close to the ground, i.e., during taxi-out, takeoff and initial climb, approach and landing and taxi-in. The aim of these study is to analyze intermediate variables and do the comparison of visibility forecasting by using Autoregressive Integrated Moving Average (ARIMA) and Long Short Term Memory (LSTM) Model. This paper proposes ARIMA model and LSTM model for forecasting visibility at Hang Nadim Airport, Batam Indonesia using one variable weather data as predictor such as visibility and combine with another variable weather data as moderating variables such as temperature, dew point and humidity. The models were tested using weather time series data at Hang Nadim Airport, Batam Indonesia. This research compares the Root Mean Square Error (RMSE) resulted by LTSM model with the RMSE resulted by ARIMA model. The results of this experiment show that LSTM model with/or without intermediate variable has better performance than ARIMA Model.

Original languageEnglish
Pages (from-to)930-938
Number of pages9
JournalJournal of Computer Science
Volume14
Issue number7
DOIs
Publication statusPublished - 1 Jan 2018
Externally publishedYes

Fingerprint

Weather forecasting
Visibility
Airports
Mean square error
Long short-term memory
Takeoff
Landing
Aviation
Time series
Atmospheric humidity
Navigation
Aircraft

Keywords

  • Autoregressive integrated moving average
  • Long Short-Term Memory
  • Root mean square error
  • Visibility
  • Weather forecasting

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Artificial Intelligence

Cite this

Weather forecasting using merged Long Short-Term Memory Model (LSTM) and Autoregressive Integrated Moving Average (ARIMA) Model. / Salman, Afan Galih; Heryadi, Yaya; Abdurahman, Edi; Suparta, Wayan.

In: Journal of Computer Science, Vol. 14, No. 7, 01.01.2018, p. 930-938.

Research output: Contribution to journalArticle

Salman, Afan Galih ; Heryadi, Yaya ; Abdurahman, Edi ; Suparta, Wayan. / Weather forecasting using merged Long Short-Term Memory Model (LSTM) and Autoregressive Integrated Moving Average (ARIMA) Model. In: Journal of Computer Science. 2018 ; Vol. 14, No. 7. pp. 930-938.
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