Real-Time Detection of Weather-based Disasters

Vaidya, Sunit; Virani, Kumail; Nambiar, Gokul; Devadkar, Kailas

doi:10.1109/icesc51422.2021.9532615

Cited by 1 publication

(1 citation statement)

References 14 publications

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“…Artificial Intelligence (AI) (Fente & Singh, 2018) is the study of giving computers the ability to work like humans using various algorithms, which include thinking and other functions (Akhila et al, 2022). 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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