Oceanic Wave Forecasting
Time Series Analysis • Independent Project
Challenge
Tofino, a coastal town known for its vibrant tourism and marine activities, requires accurate oceanic wave forecasts to enhance safety and efficiently manage coastal resources. The challenge was to develop a reliable model that could predict wave periods and heights, providing critical insights for activities like surfing, boating, and emergency planning.
Approach
I developed a machine learning model using historical wave and environmental data, such as wind speed and atmospheric pressure, to predict wave periods and heights. This involved extensive data collection and cleaning, followed by feature engineering to identify the key variables influencing wave behavior. I then trained and optimized the model to ensure accuracy in forecasting future wave conditions. Visualizations were created to make the data and predictions accessible for local authorities and stakeholders.
Results
Although the tool is not live yet, it has demonstrated strong predictive capability in test scenarios, accurately forecasting wave periods and heights based on historical data. The model provides valuable insights for coastal management and tourism planning, offering a data-driven approach to understanding and preparing for oceanic conditions.
Future Plans
The next steps include integrating real-time data sources and tidal information to create a live forecasting system, enhancing the model’s accuracy and utility. Additionally, collaboration with local authorities could make the tool publicly available, while further improvements will focus on incorporating long-term climate trends to better forecast future conditions.
Expertise
As the sole developer on this project, I applied my expertise in Time Series, feature engineering, and environmental data analysis to build a robust forecasting tool. By working independently, I tailored the solution to address the specific needs of Tofino’s coastal management and safety, ensuring a practical and scalable approach to wave forecasting.