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Evaluation of a machine learning framework to forecast storm surge Daryl Metters Coastal Impacts Unit Department of Environment and Science The Coastal Impacts: what we do The primary Queensland Government agency involved in the

  1. Evaluation of a machine learning framework to forecast storm surge Daryl Metters Coastal Impacts Unit Department of Environment and Science

  2. The Coastal Impacts: what we do • The primary Queensland Government agency involved in the management of extreme storm-tide events in Queensland • Operate 36 storm-tide gauges and 14 tide gauges along the Queensland coast. These measure the magnitude of storm- tide during cyclonic events for the use by disaster management groups for evacuation purposes • During severe events we Liaise with the Bureau of Meteorology to confirm information in storm-tide advice (warnings) and provide technical advice on storm-tide to local, district and state groups

  3. What is Storm Tide ?

  4. What is Storm Tide ?

  5. What is Storm Surge? Storm Surge = Actual - Tide Prediction

  6. Storm-tide data • We present and distribute the actual storm-tide data along with the tide prediction and residual in near real time • This process predicts or forecasts the tide component only and not the surge level • The surge level is calculated in near real time as the levels are reported by each STG • Storm Surge = actual (measured) level – tide prediction.

  7. Storm Surge forecasting • Many methods developed over recent years to help forecast the storm surge level • Most make use of the linear relationship between the driving parameter(s) and the actual water level recorded • Successful methods used in modern times are based on numerical modelling of the physical driving forces responsible for the surge levels • These modelling efforts are expensive to maintain due to the large computing power needed to operate the models

  8. Storm Surge forecast Important for planning: 1. Evacuation during severe events 2. Recreational activities 3. Commercial transport 4. Scientific marine activities

  9. The Project Storm-tide forecasting using Machine Learning A proof of concept • Transfer of knowledge and understanding of machine learning principles to DES staff • To establish and test various machine learning models, and use those machine learning models to forecast storm-tide levels • Formulate an understanding of the effectiveness of machine learning in forecasting storm-tide

  10. Machine Learning • A type of artificial intelligence • Enables the ability to "learn" with data, without being explicitly programmed • Explores the study and construction of algorithms that can learn from and make predictions on data • This overcomes following strictly static program instructions by making data-driven predictions or decisions, through building a model from sample inputs • Employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or unfeasible.

  11. Computing and code • Python library: Scikit-learn • Anaconda 3 • Used CPU and memory • Amazon AWS High Performance Computing facilities 72 cores of virtual CPUs and 144 GB memory

  12. Data preparation • Storm tide, wind and pressure data checked for errors and gaps • Filtered for out of range values: if out of range then removed • Single missing points given the average of the two points before and after, larger gaps were considered missing data and removed.

  13. Model Training, Testing and Forecasting • The prepared dataset was divided into training and testing datasets • 2/3 training : to improve model accuracy • 1/3 testing : to check model accuracy • The ML model output is then used to forecast 72 hours beyond the training and testing datasets

  14. Input Data • Clump Point Storm Tide Gauge • Storm Tide: one minute • Atmospheric pressure: one minute • Wind speed : 10 minute • Wind direction: 10 minute • Tide predictions : 10 minute

  15. Machine Learning Two general types of machine learning models utilised (1) Feature based models: • Decision Tree • Neural Networks • Linear • k-Nearest Neighbour (kNN) • Support Vector Machine (SVM) and • Random Forest (2) Time series models: ARIMA and Prophet.

  18. Model approach's • V1 Modelled weather approach Storm Tide Training/testing Input : storm tide, atmospheric pressure, wind speed and direction, tide predictions Forecast input : BoM Access-G modelled weather forecast Storm Surge Training/testing Input : residual, atmospheric pressure, wind speed and direction Forecast input : BoM Access-G modelled weather forecast • V2 Dataset shift approach • V3 Modelled weather forecast approach using the time series models • V2 and V3 approach’s not taken to forecast phase

  19. Model performance • Metrics used: 1. Run time: Linux command 'time' used to generate real time: wall clock time - time from start to finish of the call. 2. Mean Square error 3. Correlation coefficients

  20. Model performance Model Run Time Forecast type Model Real time Storm Tide Decision Tree 0m 9.5s Neural Network 128m 7.9s Linear Model 0m 8.7s KNN 0m 25.8s Random Forest 14m 35.8s SVR 31m 23.9s Residual Decision Tree 0m 12.0s Neural Network 132m 45.7s Linear Model 0m 8.6s KNN 0m 56.8s Random Forest 14m 50.4s SVR 31m 29.1s

  21. Model performance Model Run Time Forecast type Model Real time Storm Tide Decision Tree 0m 9.5s Neural Network 128m 7.9s Linear Model 0m 8.7s KNN 0m 25.8s Random Forest 14m 35.8s SVR 31m 23.9s Residual Decision Tree 0m 12.0s Neural Network 132m 45.7s Linear Model 0m 8.6s KNN 0m 56.8s Random Forest 14m 50.4s SVR 31m 29.1s

  22. Model performance Model Run Time Forecast type Model Real time Storm Tide Decision Tree 0m 9.5s Neural Network 128m 7.9s Linear Model 0m 8.7s KNN 0m 25.8s Random Forest 14m 35.8s SVR 31m 23.9s Residual Decision Tree 0m 12.0s Neural Network 132m 45.7s Linear Model 0m 8.6s KNN 0m 56.8s Random Forest 14m 50.4s SVR 31m 29.1s

  23. Model performance testing phase Storm Tide with tide predictions Correlation Model Mean Squared coefficient error 0.988 KNN 0.010 0.990 SVR 0.007 0.990 Decision Tree 0.008 0.991 Random Forest 0.007 0.990 Linear Model 0.007 0.990 Neural Network 0.007 High correlations All models performed equally well

  24. Model performance testing phase Storm Tide without tide predictions Correlation Model Mean Squared coefficient error KNN 0.400 0.057 SVR 0.374 0.069 Decision Tree 0.374 0.111 Random Forest 0.374 0.106 Linear Model 0.377 0.019 Neural Network 0.378 -0.014 Very low correlation All models performed poorly

  25. Model performance testing phase Storm Surge (Residual) 1 month 3 months 6 months 12 months average Model 0.264 0.288 0.421 0.398 0.343 KNN 0.377 0.398 0.568 0.403 0.437 SVR 0.307 0.326 0.400 0.425 0.365 Decision Tree 0.375 0.383 0.610 0.543 0.478 Random Forest 0.400 0.397 0.383 0.343 0.381 Linear Model 0.400 0.393 0.514 0.512 0.455 Neural Network 0.354 0.364 0.483 0.437 average Moderate correlation

  26. Model performance testing phase Storm Surge (Residual) 1 month 3 months 6 months 12 months average Model 0.264 0.288 0.421 0.398 0.343 KNN 0.377 0.398 0.568 0.403 0.437 SVR 0.307 0.326 0.400 0.425 0.365 Decision Tree 0.375 0.383 0.610 0.543 0.478 Random Forest 0.400 0.397 0.383 0.343 0.381 Linear Model 0.400 0.393 0.514 0.512 0.455 Neural Network 0.354 0.364 0.483 0.437 average Moderate correlation Increasing correlation with increase in data length

  27. Model performance testing phase Storm Surge (Residual) 1 month 3 months 6 months 12 months average Model 0.264 0.288 0.421 0.398 0.343 KNN 0.377 0.398 0.568 0.403 0.437 SVR 0.307 0.326 0.400 0.425 0.365 Decision Tree 0.375 0.383 0.610 0.543 0.478 Random Forest 0.400 0.397 0.383 0.343 0.381 Linear Model 0.400 0.393 0.514 0.512 0.455 Neural Network 0.354 0.364 0.483 0.437 average Moderate correlation Increasing correlation with increase in data length Random Forest best performing model

  28. Model performance testing phase Storm Surge (Residual) 1 month 3 months 6 months 12 months average Model 0.264 0.288 0.421 0.398 0.343 KNN 0.377 0.398 0.568 0.403 0.437 SVR 0.307 0.326 0.400 0.425 0.365 Decision Tree 0.375 0.383 0.610 0.543 0.478 Random Forest 0.400 0.397 0.383 0.343 0.381 Linear Model 0.400 0.393 0.514 0.512 0.455 Neural Network 0.354 0.364 0.483 0.437 average Moderate correlation Increasing correlation with increase in data length Random Forest and Neural Network best performing

  29. Model performance testing phase Time series models Correlation ARIMA model Mean Squared Coefficient error -0.02 Storm Tide 0.438 0.09 Wind Speed 36.739 0.13 Wind Direction 6438.297 0.51 Air Pressure 67.119 Correlation Prophet model Mean Squared Coefficient error 0.34 Storm Tide 0.014 0.13 Wind Speed 86.645 0.41 Wind Direction 8958.065 0.59 Air Pressure 54.353 Very low correlation for Storm Tide, and wind speed and direction

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