Survey on the Application of Deep Learning in Extreme Weather Prediction

Fang, Wei; Xue, Qiongying; Shen, Liang; Sheng, Victor S.

doi:10.3390/atmos12060661

Cited by 32 publications

(10 citation statements)

References 33 publications

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“…Tidak hanya model berbasis peubah, tetapi model klasik dan model kontemporer juga telah dilakukan oleh ahli dalam bidang peramalan (forecasting). Beberapa penelitian terkait diantaranya Fang, et al [5] menunjukkan bahwa prediksi cuaca ekstrim di China dengan pendekatan algoritma deep learning yakni Recurrent Neural Network (RNN), Backpropogation Neural Network (BPNN), Convolutional Neural Network (CNN) memberikan tingkat akurasi yang cukup tinggi dibandingkan dengan metode pemetaan. Han, et al [6] memprediksi data cuaca dari stasiun Harvard Graduate School of Design (GSD) menggunakan algoritma RNN dengan tingkat akurasi yang cukup tinggi dibandingkan dengan model seperti BNN dan NN.…”

Section: Pendahuluanunclassified

Peramalan Data Cuaca Ekstrim Indonesia Menggunakan Model ARIMA dan Recurrent Neural Network

Hikmah

Asrirawan²,

Apriyanto³

et al. 2023

Jambura J. Math

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Extreme weather modeling is a challenge for modeling experts in Indonesia and the world. Extreme weather prediction is a complex problem because the chances of it happening are very small, so the developed models often have a low level of accuracy. The purpose of this research is to combine the classic model, Autoregressive Integrated Moving Average (ARIMA), recurrent neural network (RNN) model using Adam and SGD estimation (RNN-Adam and RNN-SGD) with the reLU, tanh, sigmoid and gaussian activation functions. In addition, the ARIMA-RNN mix model was also demonstrated in this study. These models are applied to monthly period extreme weather data obtained from the Meteorology, Climatology and Geophysics Agency (BMKG) of West Sulawesi Province which are converted into training data and test data. The RMSE value is used to see the level of prediction accuracy in both training data and test data. Based on the research results, the best model obtained for modeling Indonesia’s extreme weather is the ARIMA-RNN-Adam mix model with the reLU activation function based on the RMSE value on the training and test data. At n = 50, the smallest RMSE and MSE values of the third model are the ARIMA-RNN-Adam model which is 0.23212 using the reLU activation function, then the ARIMA-RNN-SGD model which is 0.25432 with the same activation function, while the ARIMA value is 0.3270. At n=100 it can be seen that the smallest RMSE and MSE values of the three models are the ARIMA-RNN-Adam model which is equal to 0.25149 using the reLU activation function, then the ARIMA-RNN-SGD model which is equal to 0.25256 with the same activation function, while the ARIMA value is 0.2644.

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Section: Pendahuluanunclassified

Peramalan Data Cuaca Ekstrim Indonesia Menggunakan Model ARIMA dan Recurrent Neural Network

Hikmah

Asrirawan²,

Apriyanto³

et al. 2023

Jambura J. Math

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“…While they presented the challenges regarding available data, they also reviewed papers that forecast weather parameters. Fang et al (2021a) narrowed down the focus to only cover studies that tackled the prediction of extreme weather events, meaning the prediction of rare climate occurrences.…”

Section: Review Papersmentioning

confidence: 99%

A Systematic Review of Deep Learning Applications in Interpolation and Extrapolation of Precipitation Data

Sit¹,

Demiray²,

Demir³

2022

Preprint

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With technological enhancements, the volume, velocity, and variety (3Vs) of the raw digital Earth data have increased in recent years. Due to the increased availability of computer resources and the growing popularity of deep learning applications, this data has been a crucial source for data-driven studies that have transformed the fields of climate and earth science. One of the critical data sources is precipitation supporting climate and earth science studies on modeling, forecasting, and preparedness for extreme events (i.e., floods, droughts, pollution transport). In this study, we worked on an extensive review of manuscripts focusing on use of deep learning methods to tackle challenges either to improve the quality or extrapolate (forecast) rainfall datasets. The purpose of this study is to summarize the most recent developments in deep learning approaches for forecasting rainfall or improving rainfall datasets, as well as highlighting issues, shortcomings, and open questions with insightful recommendations for future directions.

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“…In the actual training, we select ϵ = 0.1 σ as our choice, because of the observation that the relative error of the model's predictions for extreme values is often below 10% (Bellprat & Doblas-Reyes, 2016) (Fang et al, 2021). Therefore it is sufficient to consider an error margin of 10%.…”

Section: A1 Analysis Of the Optimization Objectivementioning

confidence: 99%

Concrete-to-concrete interface shear strength prediction based on explainable extreme gradient boosting approach

Chen

et al. 2021

Construction and Building Materials

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Survey on the Application of Deep Learning in Extreme Weather Prediction

Cited by 32 publications

References 33 publications

Peramalan Data Cuaca Ekstrim Indonesia Menggunakan Model ARIMA dan Recurrent Neural Network

Peramalan Data Cuaca Ekstrim Indonesia Menggunakan Model ARIMA dan Recurrent Neural Network

A Systematic Review of Deep Learning Applications in Interpolation and Extrapolation of Precipitation Data

Concrete-to-concrete interface shear strength prediction based on explainable extreme gradient boosting approach

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