Typhoon Intensity Forecasting Based on LSTM Using the Rolling Forecast Method
<p>The number of typhoons recorded from 2000 to 2019. TY, typhoon; STY, strong typhoon; super TY, super typhoon.</p> "> Figure 2
<p>The processes of the rolling forecasting and ensemble forecasting methods.</p> "> Figure 3
<p>The typhoon intensity forecasting models based on LSTM and the feed-forward neural network (FNN).</p> "> Figure 4
<p>The structure of typhoon intensity forecasting experiments.</p> "> Figure 5
<p>The evaluation indicators of the model based on an FNN and LSTM on the test set.</p> "> Figure 6
<p>The prediction curves of the models based on LSTM and the FNN on test sets of 6, 12, 24 and 48 h ahead.</p> "> Figure 7
<p>The prediction curves of the models based on LSTM and the FNN on test sets of 72, 96 and 120 h ahead.</p> "> Figure 8
<p>The evaluation indicators of the model based on an FNN and LSTM using the typhoon Chan-hom (2015).</p> "> Figure 9
<p>The pressure prediction curves of the models based on an FNN and LSTM on typhoon Chan-hom (2015).</p> "> Figure 10
<p>The wind speed prediction curves of the models based on an FNN and LSTM on typhoon Chan-hom (2015).</p> ">
Abstract
:1. Introduction
2. Study Area and Data
3. Methodologies
3.1. Model Settings
3.2. Forecasting Method
3.3. Data Processing and Evaluation Indicators
4. LSTM Models
5. Experimental Results
5.1. Determining the Optimal Prediction Factors
5.2. Validation on the Test Set
5.3. Validation on Typhoon Cases
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Date (YYYYMMDDHH) | Intensity Level | Latitude | Longitude | Minimum Pressure (h Pa) | Maximum Wind Speed (m/s) |
---|---|---|---|---|---|
2015063000 | 1 | 95 | 1607 | 1000 | 15 |
2015063006 | 1 | 98 | 1601 | 1000 | 15 |
2015063012 | 2 | 100 | 1595 | 998 | 18 |
2015063018 | 2 | 101 | 1587 | 995 | 20 |
2015070100 | 2 | 105 | 1575 | 995 | 20 |
2015070106 | 2 | 110 | 1566 | 995 | 20 |
2015070112 | 2 | 113 | 1556 | 992 | 23 |
2015070118 | 3 | 113 | 1543 | 990 | 25 |
2015070200 | 3 | 111 | 1528 | 990 | 25 |
Statistics | P (h Pa) | WS (m/s) | Lat | Lon | MS (km/h) | |||||
---|---|---|---|---|---|---|---|---|---|---|
Train | Test | Train | Test | Train | Test | Train | Test | Train | Test | |
No. | 5394 | 2308 | 5394 | 2308 | 5394 | 2308 | 5394 | 2308 | 5258 | 2251 |
Max | 1010 | 1012 | 65 | 78 | 519 | 537 | 2029 | 2260 | 81.54 | 80.64 |
Min | 910 | 888 | 10 | 8 | 48 | 28 | 1008 | 1023 | 0 | 0 |
Mean | 977.77 | 975.91 | 29.31 | 30.26 | 212.1 | 206.24 | 1356.11 | 1345.11 | 12.28 | 12.39 |
Variables | Time | ||||
---|---|---|---|---|---|
t | t-1 | t-2 | t-3 | t-4 | |
P | P (t) | P (t-1) | P (t-2) | P (t-3) | P (t-4) |
WS | WS (t) | WS (t-1) | WS (t-2) | WS (t-3) | WS (t-4) |
Lat | Lat (t) | Lat (t-1) | Lat (t-2) | Lat (t-3) | Lat (t-4) |
Lon | Lon (t) | Lon (t-1) | Lon (t-2) | Lon (t-3) | Lon (t-4) |
MS | MS (t) | MS (t-1) | MS (t-2) | MS (t-3) | MS (t-4) |
Hyperparameters | LSTM Models | FNN Model |
---|---|---|
Number of hidden layers | 1 | 1 |
Number of prediction factors | n | n |
Number of hidden layer neurons | 4*n | 4*n |
Activation function of the hidden layer | Tanh | Sigmoid |
Loss function | MSE | MSE |
Maximum iteration step | 300 | 5000 |
Optimizer | Adam | Default |
Learning rate | 0.01 | 0.001 |
Goal learning rate | Default | 0.0001 |
Model | Function Expression |
---|---|
LSTM-1 | P(i+1), WS(t+i) = f[P(t+i-1), WS(t+i-1)] |
LSTM-2 | P(t+i), WS(t+i) = f[P(t+i-1), WS(t+i-1), P(t+i-2), WS(t+i-2)] |
LSTM-3 | P(t+i), WS(t+i), Lat(t+i) = f[P(t+i-1), WS(t+i-1), Lat(t+i-1)] |
LSTM-4 | P(t+i), WS(t+i), Lat(t+i) = f[P(t+i-1), WS(t+i-1), Lat(t+i-1), P(t+i-2), WS(t+i-2), LAT(t+i-2)] |
LSTM-5 | P(t+i), WS(t+i), Lon(t+i) = f[P(t+i-1), WS(t+i-1), Lon(t+i-1)] |
LSTM-6 | P(t+i), WS(t+i), Lon(t+i) = f[P(t+i-1), WS(t+i-1), Lon(t+i-1), P(t+i-2), WS(t+i-2), Lon(t+i-2)] |
LSTM-7 | P(t+i), WS(t+i), Lat(t+i), Lon(t+i) = f[P(t+i-1), WS(t+i-1), Lat(t+i-1), Lon(t+i-1)] |
LSTM-8 | P(t+i), WS(t+i), Lat(t+i), Lon(t+i) = f[P(t+i-1), WS(t+i-1), Lat(t+i-1), Lon(t+i-1), |
P(t+i-2), WS(t+i-2), Lat(t+i-2), Lon(t+i-2)] | |
LSTM-9 | P(t+i), WS(t+i), Lat(t+i), Lon(t+i), MS(t+i) = f[P(t+i-1), WS(t+i-1), Lat(t+i-1), |
Lon(t+i-1), MS(t+i-1)] | |
LSTM-10 | P(t+i), WS(t+i), Lat(t+i), Lon(t+i), MS(t+i) = f[P(t+i-1), WS(t+i-1), Lat(t+i-1), |
Lon(t+i-1), MS(t+i-1), P(t+i-2), WS(t+i-2), Lat(t+i-2), Lon(t+i-2), MS(t+i-2)] |
Models | Pressure (Lead Time, h) | Wind Speed (Lead Time, h) | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6 | 12 | 24 | 48 | 72 | 96 | 120 | 6 | 12 | 24 | 48 | 72 | 96 | 120 | |
LSTM-1 | 4.02 | 7.14 | 12.24 | 19.65 | 24.18 | 26.23 | 26.04 | 2.26 | 4.11 | 7.17 | 11.71 | 14.43 | 15.53 | 15.3 |
LSTM-2 | 3.44 | 6.07 | 11 | 18.15 | 22.44 | 24.64 | 25.09 | 1.95 | 3.43 | 6.31 | 10.62 | 13.1 | 14.3 | 14.52 |
LSTM-3 | 3.85 | 6.46 | 10.59 | 16.45 | 20.4 | 23.02 | 24.17 | 2.16 | 3.61 | 6.01 | 9.44 | 11.65 | 13.03 | 13.79 |
LSTM-4 | 3.42 | 5.74 | 9.87 | 15.49 | 18.9 | 21.42 | 23.6 | 1.89 | 3.15 | 5.45 | 8.81 | 10.82 | 12.34 | 13.7 |
LSTM-5 | 4.02 | 7.04 | 11.75 | 18.02 | 21.49 | 22.78 | 22.41 | 2.28 | 4.06 | 6.92 | 10.78 | 12.76 | 13.39 | 13.12 |
LSTM-6 | 3.35 | 5.73 | 10.09 | 16.1 | 19.6 | 21.23 | 21.38 | 1.9 | 3.3 | 5.9 | 9.56 | 11.5 | 12.31 | 12.38 |
LSTM-7 | 3.72 | 6.11 | 9.84 | 14.76 | 17.68 | 19.57 | 22.54 | 2.06 | 3.37 | 5.52 | 8.36 | 9.95 | 11.13 | 12.42 |
LSTM-8 | 3.33 | 5.38 | 8.67 | 12.46 | 14.78 | 17.35 | 20 | 1.84 | 2.98 | 4.87 | 7.12 | 8.5 | 9.99 | 11.57 |
LSTM-9 | 3.79 | 6.27 | 10.1 | 15.19 | 17.94 | 19.28 | 23.74 | 2.09 | 3.46 | 5.67 | 8.71 | 10.34 | 11.27 | 12.49 |
LSTM-10 | 3.42 | 5.55 | 8.93 | 12.71 | 15.19 | 18.08 | 20.85 | 1.9 | 3.09 | 5.03 | 7.32 | 8.78 | 10.41 | 11.91 |
Model | Function Expression |
---|---|
LSTM-11 | P(t+i), WS(t+i), Lat(t+i), Lon(t+i)=f[P(t+i-1), WS(t+i-1), Lat(t+i-1), Lon(t+i-1), P(t+i-2), WS(t+i-2), Lat(t+i-2), Lon(t+i-2), |
P(t+i-3), WS(t+i-3), Lat(t+i-3), Lon(t+i-3)] | |
LSTM-12 | P(t+i), WS(t+i), Lat(t+i), Lon(t+i)=f[P(t+i-1), WS(t+i-1), Lat(t+i-1), Lon(t+i-1), P(t+i-2), WS(t+i-2), Lat(t+i-2), Lon(t+i-2), |
P(t+i-3), WS(t+i-3), Lat(t+i-3), Lon(t+i-3), P(t+i-4), WS(t+i-4), Lat(t+i-4), Lon(t+i-4)] |
Models | Pressure (Lead Time, h) | Wind Speed (Lead Time, h) | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6 | 12 | 24 | 48 | 72 | 96 | 120 | 6 | 12 | 24 | 48 | 72 | 96 | 120 | |
LSTM-8 | 3.33 | 5.38 | 8.67 | 12.46 | 14.78 | 17.35 | 20 | 1.84 | 2.98 | 4.87 | 7.12 | 8.5 | 9.99 | 11.57 |
LSTM-11 | 3.4 | 5.52 | 9.14 | 14 | 16.63 | 18.48 | 19.64 | 1.96 | 3.16 | 5.32 | 8.14 | 9.53 | 10.47 | 11.14 |
LSTM-12 | 3.41 | 5.46 | 8.93 | 14.07 | 16.88 | 18.16 | 18.83 | 1.95 | 3.13 | 5.29 | 8.41 | 9.94 | 10.58 | 10.89 |
Evaluation Indicators | Models | Pressure (Lead Time, h) | Wind Speed (Lead Time, h) | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6 | 12 | 24 | 48 | 72 | 96 | 120 | 6 | 12 | 24 | 48 | 72 | 96 | 120 | ||
MAE | FNN | 3.45 | 5.67 | 9.17 | 13.35 | 15.41 | 16.61 | 18.29 | 1.96 | 3.19 | 5.17 | 7.69 | 8.94 | 9.79 | 10.85 |
LSTM-8 | 3.33 | 5.38 | 8.67 | 12.46 | 14.78 | 17.35 | 20 | 1.84 | 2.98 | 4.87 | 7.12 | 8.5 | 9.99 | 11.57 | |
RMSE | FNN | 5.21 | 6.99 | 13.17 | 24.71 | 32.21 | 34.81 | 35.87 | 2.79 | 4.02 | 7.74 | 14.65 | 19.36 | 21.48 | 22.12 |
LSTM-8 | 4.84 | 7.86 | 12.32 | 17.62 | 21.57 | 25.65 | 29.47 | 2.5 | 4.15 | 6.65 | 9.76 | 12.1 | 14.45 | 16.62 |
Typhoon Cases | Models | Pressure (Lead Time, h) | Wind Speed (Lead Time, h) | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6 | 12 | 24 | 48 | 72 | 96 | 120 | 6 | 12 | 24 | 48 | 72 | 96 | 120 | ||
Chan-hom | FNN | 7.17 | 9.99 | 14.29 | 20.83 | 18.98 | 17.18 | 19.31 | 4.25 | 6.24 | 8.05 | 11.37 | 8.89 | 8.11 | 8.44 |
LSTM-8 | 4.04 | 8.21 | 13.02 | 13.73 | 9.99 | 7.54 | 15.47 | 2.59 | 4.97 | 7.86 | 8.46 | 5.86 | 3.79 | 8.94 | |
Soudelor | FNN | 8.37 | 12.17 | 17.69 | 34.14 | 46.35 | 44.45 | 39.55 | 5.09 | 7.87 | 13.92 | 19.15 | 33 | 45.47 | 43.77 |
LSTM-8 | 3.85 | 6.79 | 11.07 | 19.67 | 27.58 | 30.79 | 30.69 | 2.08 | 3.48 | 5.13 | 9.48 | 14.19 | 16.69 | 16.89 |
Typhoon Cases | Models | Pressure (Lead Time, h) | Wind Speed (Lead Time, h) | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6 | 12 | 24 | 48 | 72 | 96 | 120 | 6 | 12 | 24 | 48 | 72 | 96 | 120 | ||
Chan-hom | FNN | 7.24 | 11.54 | 17.03 | 21.11 | 19.6 | 16.28 | 15.31 | 5.11 | 7.92 | 11.79 | 14 | 13.42 | 12.63 | 9.02 |
LSTM-8 | 5.61 | 10.72 | 16.19 | 16.58 | 13.47 | 10.07 | 9.69 | 3.5 | 6.82 | 10.42 | 10.33 | 8.05 | 5.73 | 5.63 | |
Soudelor | FNN | 11.25 | 16.75 | 24.33 | 43.24 | 52.81 | 51.67 | 47.53 | 7.63 | 11.84 | 22.67 | 27.72 | 40.48 | 50.21 | 49.06 |
LSTM-8 | 5.33 | 9.33 | 15.29 | 25.59 | 33.68 | 36.28 | 33.55 | 2.84 | 4.62 | 7.13 | 12.6 | 17.58 | 19.71 | 18.89 |
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Yuan, S.; Wang, C.; Mu, B.; Zhou, F.; Duan, W. Typhoon Intensity Forecasting Based on LSTM Using the Rolling Forecast Method. Algorithms 2021, 14, 83. https://doi.org/10.3390/a14030083
Yuan S, Wang C, Mu B, Zhou F, Duan W. Typhoon Intensity Forecasting Based on LSTM Using the Rolling Forecast Method. Algorithms. 2021; 14(3):83. https://doi.org/10.3390/a14030083
Chicago/Turabian StyleYuan, Shijin, Cheng Wang, Bin Mu, Feifan Zhou, and Wansuo Duan. 2021. "Typhoon Intensity Forecasting Based on LSTM Using the Rolling Forecast Method" Algorithms 14, no. 3: 83. https://doi.org/10.3390/a14030083