Machine learning-enabled weather forecasting for real-time radioactive transport and contamination prediction

Ayoub, Ali; Wainwright, Haruko M.; Sansavini, Giovanni

doi:10.1016/j.pnucene.2024.105255

Cited by 4 publications

References 21 publications

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An enhanced fourier neural operator surrogate for radioactive plume transport forecasting

Ayoub,

Wainwright,

Wang

et al. 2024

Stoch Environ Res Risk Assess

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Accurate real-time forecasts of atmospheric plume behavior are crucial for effective management of environmental release incidents. However, the computational demands of weather simulations and particle transport codes limit their applicability during emergencies. In this study, we employ a U-Net enhanced Fourier Neural Operator (U-FNO) to statistically emulate the calculations of the WSPEEDI dose forecasting numerical simulator, using pre-calculated ensemble simulations. The developed emulator is capable of effectively simulating any radioactive-release scenario and generating the time series of dose distribution in the environment 4000 times faster than the numerical simulator, while still maintaining high accuracy. It predicts the plume direction, extent, and dose-rate magnitudes using initial- and boundary-condition meteorological data as input. The speed and efficiency of this framework offers a powerful tool for swift decision-making during emergencies, facilitating risk-informed protective actions, evacuation execution, and zone delineation. Its application extends to various contaminant release and transport problems, and can be instrumental in engineering tasks requiring uncertainty quantification (UQ) for environmental risk assessment.

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An enhanced fourier neural operator surrogate for radioactive plume transport forecasting

Ayoub,

Wainwright,

Wang

et al. 2024

Stoch Environ Res Risk Assess

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Artificial intelligence and numerical weather prediction models: A technical survey

Waqas,

Humphries,

Chueasa

et al. 2024

Natural Hazards Research

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Comparison of RNN-LSTM, TFDF and stacking model approach for weather forecasting in Bangladesh using historical data from 1963 to 2022

Hasan,

Rahman

2024

PLoS ONE

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Forecasting the weather in an area characterized by erratic weather patterns and unpredictable climate change is a challenging endeavour. The weather is classified as a non-linear system since it is influenced by various factors that contribute to climate change, such as humidity, average temperature, sea level pressure, and rainfall. A reliable forecasting system is crucial in several industries, including transportation, agriculture, tourism, & development. This study showcases the effectiveness of data mining, meteorological analysis, and machine learning techniques such as RNN-LSTM, TensorFlow Decision Forest (TFDF), and model stacking (including ElasticNet, GradientBoost, KRR, and Lasso) in improving the precision and dependability of weather forecasting. The stacking model strategy entails aggregating multiple base models into a meta-model to address issues of overfitting and underfitting, hence improving the accuracy of the prediction model. To carry out the study, a comprehensive 60-year meteorological record from Bangladesh was gathered, encompassing data on rainfall, humidity, average temperature, and sea level pressure. The results of this study suggest that the stacking average model outperforms the TFDF and RNN-LSTM models in predicting average temperature. The stacking average model achieves an RMSLE of 1.3002, which is a 10.906% improvement compared to the TFDF model. It is worth noting that the TFDF model had previously outperformed the RNN-LSTM model. The performance of the individual stacking model is not as impressive as that of the average model, with the validation results being better in TFDF.

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Machine learning-enabled weather forecasting for real-time radioactive transport and contamination prediction

Cited by 4 publications

References 21 publications

An enhanced fourier neural operator surrogate for radioactive plume transport forecasting

An enhanced fourier neural operator surrogate for radioactive plume transport forecasting

Artificial intelligence and numerical weather prediction models: A technical survey

Comparison of RNN-LSTM, TFDF and stacking model approach for weather forecasting in Bangladesh using historical data from 1963 to 2022

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