GNSS-Based Machine Learning Storm Nowcasting
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data
2.1.1. Integrated Water Vapour Observations
2.1.2. Wet Refractivity Data
2.1.3. Storms
2.2. Application of Random Forest Classifier
2.2.1. Input Features
2.2.2. Random Forest Classifier
2.2.3. Data Preprocessing
2.2.4. Implementation of Storm Classifier
- Max depth—the maximum depth of the tree (from 1 to 11).Each tree in RF makes multiple splits. The depth of the tree relates to how much information is captured. Larger depth in a tree allows to explain more variation in the data. However, too many splits can cause overfitting.
- Max features—the number of features to consider when looking for the best split (from 1 to 216).Before determining the best split RF model randomly resamples features. Trees with a large selection of features from which the best split is chosen might have better performance. However, when many features are considered, trees can be less diverse which will decrease their RF algorithm accuracy.
- N estimators—the number of trees in the forest (from 10 to 2000).Increased number of estimators improves the accuracy of the model, but this is done at the expense of a longer learning process and might be computationally expensive.
- Min sample leaf—the minimum number of samples required to be at a leaf node (from 1 to 21).The optimal value of the minimum number of samples at the leaf depends on the size of the learning set. Too large values may not be able to capture sufficient variation in the data.
2.2.5. Evaluation Metrics
3. Results
3.1. Random Forest Classifier for Storm Nowcasting
3.1.1. Attribute Selection
3.1.2. Classifier Performance
3.1.3. Case Studies
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
GNSS | Global Navigation Satellite Systems |
GPS | Global Positioning System |
PWV | Precipitable Water Vapour |
IWV | Integrated Water Vapour |
ZTD | Zenith Troposphere Delay |
WRF | Weather Research and Forecasting |
E-GVAP | The EUMETNET EIG GNSS water vapour programme |
NPV | Negative Predicted Value |
MDI | Mean Decrease in Impurity |
UPWr | Wroclaw University of Environmental and Life Sciences |
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Actual Positive Class | Actual Negative Class | |
---|---|---|
Predicted Positive Class | True positive (TP) | False positive (FP) |
Predicted Negative Class | False negative (FN) | True negative (TN) |
Month | IWV(NST) | IWV(ST) |
---|---|---|
June | 20.40 | 25.67 |
July | 23.00 | 28.92 |
August | 22.79 | 27.98 |
Class | Reported ‘Storm’ | Reported ‘No Storm’ |
---|---|---|
Predicted ‘Storm’ | 146 | 179 |
Predicted ‘No storm’ | 347 | 3485 |
Date | Reported ‘Storm’ | Reported ‘No Storm’ | Class |
---|---|---|---|
1 August 2017 | 39 | 6 | Predicted ‘Storm’ |
32 | 432 | Predicted ‘No storm’ | |
12 August 2017 | 35 | 62 | Predicted ‘Storm’ |
12 | 1645 | Predicted ‘No storm’ | |
13 June 2019 | 72 | 111 | Predicted ‘Storm’ |
303 | 1408 | Predicted ‘No storm’ |
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Łoś, M.; Smolak, K.; Guerova, G.; Rohm, W. GNSS-Based Machine Learning Storm Nowcasting. Remote Sens. 2020, 12, 2536. https://doi.org/10.3390/rs12162536
Łoś M, Smolak K, Guerova G, Rohm W. GNSS-Based Machine Learning Storm Nowcasting. Remote Sensing. 2020; 12(16):2536. https://doi.org/10.3390/rs12162536
Chicago/Turabian StyleŁoś, Marcelina, Kamil Smolak, Guergana Guerova, and Witold Rohm. 2020. "GNSS-Based Machine Learning Storm Nowcasting" Remote Sensing 12, no. 16: 2536. https://doi.org/10.3390/rs12162536