Weather Forecasting Using Merged Long Short-Term Memory Model (LSTM) and Autoregressive Integrated Moving Average (ARIMA) Model

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

doi:10.3844/jcssp.2018.930.938

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, take-off 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.

Highlights

Weather forecasting attracts great attention of researchers from various research communities due to its effect to the global human life
This study indicates that visibility forecasting by all model Long Short Term Memory (LSTM) with intermediate variable or without intermediate variable yields better accuracy than visibility forecasting using Autoregressive Integrated Moving Average (ARIMA)
LSTM and ARIMA model have been used in time series forecasting

Summary

Introduction

Weather forecasting attracts great attention of researchers from various research communities due to its effect to the global human life. The current wide availability of massive weather observation data motivated researchers to explore pattern hidden in the large dataset and make weather prediction. The advent of information and computer technology in the last decade support the research of weather forecasting to get more accurate results. Weather forecasting is an interesting research problem in flight navigation area (Maunder et al, 2000). 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, take-off and initial climb, approach and landing and taxi-in

Methods

Results

Conclusion