Identifying and forecasting thunderstorms using weather radar data and machine learning
Methods for nowcasting lightning using weather radar data were developed using machine learning models. Reflectivity was selected as the main feature for the prediction. The purpose was to examine if machine learning applications could be used to nowcast thunderstorms with minimal data sets. The emp...
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| Muut tekijät: | , , , , , |
| Aineistotyyppi: | Pro gradu |
| Kieli: | eng |
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2023
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| Linkit: | https://jyx.jyu.fi/handle/123456789/92229 |
| Yhteenveto: | Methods for nowcasting lightning using weather radar data were developed using machine learning models. Reflectivity was selected as the main feature for the prediction. The purpose was to examine if machine learning applications could be used to nowcast thunderstorms with minimal data sets. The emphasis was to find out a model which is based on binary image classification and doesn’t require large sets of training data to work sufficiently. Convolutional neural network was the first choice. Accuracy for the model was 0.83. Another approach was made using random forest model. Precision for class 0 (no lightning) was 0.52, and for class (recorded lightning) 1, 0.90 and with total accuracy of 0.88 To improve the sets more features should be used and possibly larger data sets.
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