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Just Keep Swimming…in Deadly Conditions

Gems in STEM: How Machine Learning and Remote Sensing Can Help Save Coastal Dead Zones

Lake Erie in October 2011, Credit: ITTNASA

What are dead zones?

Dead zones are exactly what they sound like: areas in bodies of water with near impossible living conditions. Specifically, dead zones are what we call hypoxic, meaning low-oxygen. Since most organisms need oxygen, marine life can’t survive in these dead zones.

The Dangers of Eutrophication

Eutrophication is when a body of water gets an excess of nutrients, usually phosphorus and nitrogen, which then leads to dangerous algal blooms and hypoxic conditions.

A catfish on the shoreline in the algae-filled waters of North Toledo, Ohio. (Andy Morrison/The Blade via AP)
Source: Climate Change And Lakes That Look Like Pea Soup

What do dead zones mean for the future?

Literally, it’s a bigger picture.

Source: Teach Ocean Science

The Solution? Goldilocks Fertilization.

First, I think we need to do a little Law & Ordering and clear some names. Yes, dead zones are, well, deadly, but the real culprit here is what creates dead zones: overfertilization.

Source: Our World in Data

What Machine Learning Can Do

Data is so important because the more data we collect, the smarter we can make machines–which is exactly what machine learning (ML) does. Machines are “trained” with data sets and use this knowledge to respond to situations they’ve never seen before, letting them automatically do things like classification, detection, and pattern recognition.

DigitalGlobe’s WorldView 4 Satellite. Photo: Lockheed Martin

Airborne Hyperspectral Sensors to Measure Crop Traits

Introducing: hyperspectral sensors! Operating on the nanoscale, hyperspectral sensors can detect differences as small as 3–5 nanometers across their entire range and offer hundreds of wavelengths across the full range of visible, near-infrared, and shortwave infrared with high spatial resolution (<1 m). For comparison, other airborne remote sensing technologies can only pick up the visible spectrum and potentially near-infrared, i.e., some small number of spectral bands.

Source: Cubert
Source: Igor Stevanovic via Stock Photos

Spatiotemporal Deep Learning to Monitor Coastal Waters

Source: Mariusz S. Jurgielewicz via Shutterstock
Source: [11]
  1. Can the long-term and large-scale DIN and DIP distribution be accurately estimated by a nonlinear ST-DBN?
  2. How did the spatiotemporal distributions of DIN, DIP, and water quality in ZCS change in the period 2010–2018?
  3. How can the government control nutrients and improve water quality in the future?
Source: [11]
  • It’s a yes to Question #1! We can accurately estimate the long-term and large-scale DIN and DIP distribution with a spatiotemporal model.
  • The water quality was better in spring and summer and poorer in fall and winter.
  • The concentration of DIN and DIP decreased by 24% and 19% in the period 2010–2018, respectively. But, the water quality didn’t significantly improve. Even though the DIN concentration was lower, it still greatly exceeded the worst quality level’s critical value.
  • DIN contributed 93.9% to the worst quality, while DIP only accounted for 37.8%. This goes to show that the eutrophication of DIP in the ZCS has gotten much better compared to that of DIN.

Challenges in the Long-Term and Large-Scale

In order to scale and implement these machine learning models to all our coastal waters, we need data. Unfortunately, we can’t do much if we don’t have accessible and current data sets to train our model. In particular, we would need Analysis Ready Data (ARD) to be readily available, which requires a lot of time and computational power (not to mention smart people) to prepare. We also have technical mountains to climb in preprocessing, extracting, synthesizing, analyzing, storing, transferring, and basically just wrangling these large data sets more efficiently to end up with an accurate and well-developed training data set, which can get hard when we’re talking about complex environments like coastal waters and multi-scale, multi-sensor and multi-platform, and multi-temporal earth observation. Phew, that’s a lot of multi’s!



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Apoorva Panidapu

17 y/o math student, artist, and advocate for youth & gender minorities in STEAM. Winner of Strogatz Prize for Math Communication & Davidson Fellows Laureate.