Forecasting of Temperature by using LSTM and Bidirectional LSTM approach: Case Study in Semarang, Indonesia

Nizar, Ilham Muhamad; Adytia, Didit; Ramadhan, Annas Wahyu

doi:10.1109/icodsa53588.2021.9617495

Cited by 6 publications

(1 citation statement)

References 6 publications

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“…In AI development, Deep Learning (DL) algorithms can be used to make weather forecasts by utilising BiLSTM which is a development of LSTM which can improve model efficiency and accuracy in classification scenarios (Bengio, 2009;Ravi et al, 2017;Vaidya et al, 2021). This increase in accuracy is mentioned because BiLSTM uses information from historical data in the past and future as input data for the model (Nizar et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Improving Accuracy of Daily Weather Forecast Model at Soekarno-Hatta Airport Using BILSTM with SMOTE and ADASYN

Danitasari,

Ryan,

Handoko

et al. 2024

jppipa, pendidikan ipa, fisika, biologi, kimia

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Bidirectional LSTM (BiLSTM) is an extension of LSTM which can improve model efficiency and accuracy in classification scenarios based on time series data or longer time series data repeatedly. This research uses the BiLSTM algorithm to build a daily weather forecast model at Soekarno-Hatta Airport. The model built will assist forecasters in making weather forecasts on a local scale. This research is expected to be implemented and able to increase the verification value of Soekarno-Hatta Airport weather forecasts to support flight safety in Indonesia. The dataset used is hourly surface air weather parameter data (synoptic data) of Soekarno-Hatta Meteorological Station for the period January 2018 - December 2022. There is an imbalance in the data set, so the SMOTE and ADASYN techniques are used to handle the problem. The output of this research is weather conditions categorised into sunny, sunny cloudy, cloudy, light rain, moderate rain, heavy rain, and thunder rain. The results obtained will go through model verification and evaluation by finding the accuracy value by comparing the weather forecast model output with actual weather data using a multi-category contingency table. The BiLSTM - ADASYN model obtained the highest average accuracy value compared to other models, which was 83.2%.

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Section: Introductionmentioning

confidence: 99%

Improving Accuracy of Daily Weather Forecast Model at Soekarno-Hatta Airport Using BILSTM with SMOTE and ADASYN

Danitasari,

Ryan,

Handoko

et al. 2024

jppipa, pendidikan ipa, fisika, biologi, kimia

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A Hybrid SARIMA‐LSTM Model for Air Temperature Forecasting

Yang

2022

Advcd Theory and Sims

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In order to improve the prediction accuracy of air temperature forecasting, a temperature prediction model based on the hybrid SARIMA (seasonal autoregressive integrated moving average)-LSTM (long short-term memory) model is constructed. First, this method decomposes the temperature series into three series of trend, seasonal, and residual through seasonal-trend decomposition procedure based on Loess decomposition method. It establishes SARIMA to predict the trend and seasonal series and extracts the linear information contained in the time series to the maximum extent. Then, the LSTM model is used to fit the residual series and the hidden nonlinear information is further extracted. Finally, the prediction results of two parts are added in series to obtain the prediction result of the final hybrid model. Three indexes, namely, root mean square error, mean absolute error, and mean absolute percentage error are evaluated to calculate the prediction accuracy about single models including ARIMA, SARIMA, and LSTM and the hybrid models ARIMA-LSTM and SARIMA-LSTM. Also the Kupiec index is used to show tail performance. The empirical results show that the SARIMA-LSTM combination model is more accurate than the single prediction methods and other combination model. Its accuracy increases by 10.0-27.7%.

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Volume of Imbalance Container Prediction using Kalman Filter and Long Short-Term Memory

Kim,

Block,

Mercorelli

2024

Lecture Notes in Networks and Systems

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Forecasting of Temperature by using LSTM and Bidirectional LSTM approach: Case Study in Semarang, Indonesia

Cited by 6 publications

References 6 publications

Improving Accuracy of Daily Weather Forecast Model at Soekarno-Hatta Airport Using BILSTM with SMOTE and ADASYN

Improving Accuracy of Daily Weather Forecast Model at Soekarno-Hatta Airport Using BILSTM with SMOTE and ADASYN

A Hybrid SARIMA‐LSTM Model for Air Temperature Forecasting

Volume of Imbalance Container Prediction using Kalman Filter and Long Short-Term Memory

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