"Machine Learning Predicts Waves with Tide, Wind, and Time Data"

Predicting the Perfect Wave: A Journey Through Surf Forecasting and Machine Learning Approaches

As surfers, we've all experienced the thrill of riding a wave that's just right – smooth, creamy, and full of momentum. But before you can catch those gnarly waves, you need to know what conditions are out there. That's where surf forecasting comes in – predicting the best time and place for surfing based on weather data and ocean currents.

One of the most critical factors in determining a wave's quality is its tide, which plays a significant role in shaping the wave's shape and size. Let's take the beautiful coastal town of Trestles, California as an example. Located at the mouth of the San Luis Obispo River, Trestles offers some of the most consistent and reliable surfing conditions in Southern California.

Scenario 1: Predicting a Successful Day

It's a sunny Saturday morning at Trestles, and surfers are out in force. We want to predict when and where the best waves will be on this day. Our team uses a combination of traditional forecasting models and machine learning approaches to make this prediction.

Our first step is to analyze the tide tables for the past few days, which provide valuable information about the tidal range, duration, and speed. We notice that the tide is high at 2:00 AM, with a predicted wave height of around 4-5 feet in the central part of the surf area.

Next, we turn our attention to wind data from the National Weather Service (NWS), which shows a moderate southerly wind blowing at 10 mph. We use this information to predict the wave's period and speed. Using our proprietary model, TideWave, we generate a forecasted wave height chart for the next few hours.

Machine Learning Approach: Predicting Wave Height

Our Tides and Waves team has developed a machine learning approach using Python and scikit-learn libraries. We train a supervised classification model on historical data, where each input is a combination of tide, wind speed, and time of day features.

The model learns to recognize patterns in the data and outputs a probability distribution over wave heights for each hour of the forecast period. This allows us to identify the most likely wave height ranges based on the inputs provided by our NWS weather service.

Results:

By using our machine learning approach, we were able to predict the following:

• 3 hours before the high tide (10:30 AM), a wave height of around 6-7 feet was predicted in the central part of the surf area.
• Within an hour after the high tide (11:00 AM), a wave height of around 8-9 feet was predicted on the outer shores.

Scenario 2: Predicting Wave Energy

Now, let's switch gears and explore how we can predict wave energy, which is critical for determining the suitability of waves for surf competitions or shore breaks.

Our team analyzes data from various sources, including satellite imagery, ocean currents, and wave energy meters. We identify patterns in these datasets that indicate areas with high wave energy, such as those near coastal mountains or where winds are blowing at higher speeds.

Using a machine learning approach similar to our previous scenario, we develop a model that predicts wave energy based on the identified patterns. Our WaveEnergy model outputs an estimate of the predicted wave energy in kiloWatts (kWh) for each location within a specified area.

Results:

By using our WaveEnergy model, we were able to predict:

• A 10% increase in wave energy over the next 24 hours near the coastal mountain range.
• A significant drop in wave energy around 2 PM due to a high-pressure system moving into the area.

Conclusion:

Predicting waves is an essential aspect of surfing, and machine learning approaches have become increasingly important in recent years. By combining traditional forecasting models with data analysis techniques, we can gain valuable insights into wave behavior and make informed predictions about surf conditions.

Whether you're a professional surfer or just starting out, understanding the science behind wave prediction can help you catch the best waves on the beach. As our machine learning approaches continue to evolve, we're excited to see how they'll be used in real-world applications and what new opportunities for innovation will arise in the world of surf forecasting.

Stay tuned!

In the next post, we'll explore the future of wave prediction and discuss potential advancements in this field. Here's a summary of the article in a table format:

Scenario	Methodology	Results
1. Predicting Successful Day at Trestles	Traditional forecasting models + Machine Learning Approach

• Tide: high tide (2:00 AM) with predicted wave height of 4-5 feet in the central part of the surf area.
• Wind data analysis for predicting wave period and speed.

Scenario	Methodology	Results
2. Predicting Wave Energy	Machine Learning Approach (similar to Scenario 1)

• Identified patterns in satellite imagery, ocean currents, and wave energy meters.
• Model outputs predicted wave energy estimates for each location within a specified area.

Let me know if you have any further requests or if there's anything else I can help with!