Toward a world of plastic-free beaches
The United Nations Environment Programme and the IBM® Data Science and AI Elite team showcase how AI can reduce marine litter and accelerate beach cleanup efforts
Plastic waste is destroying marine ecosystems at a rapid pace – from ruining beaches to killing baby turtles to destroying coral on the sea floor. With half the world’s plastic produced in the last 13 years and 8.8 million tons washing into the oceans annually, few places on earth have escaped its reach. Plastic litter fouls the remote, icy coves of Antarctica, the pristine shores of Réunion and Mauritius, and even the unfathomable reaches of the 10,000 meter-deep Mariana Trench.
A problem so pervasive and pernicious requires immediate, global attention.
Sustainable Development Goals aim to conserve life below water
The United Nations Environment Programme (UNEP) rallies marine experts, environmentalists, nonprofits, academics and citizen scientists from countries around the world to confront environmental sustainability. In 2015, it established 17 sustainable Development Goals (SDG) for the planet, with goal 14 calling for conservation and sustainable use of the oceans. Its United Nations Development Programme (UNDP) set a goal of significantly reducing marine pollution by 2025. But to reduce pollution demands measuring it; the organization needed to establish a standard and baseline – so a subset goal called for the creation of an index to measure coastal eutrophication and the density of floating plastic.
While no one would argue against the importance of ridding beaches of single-use plastic and other forms of debris, there’s a big problem: you can’t improve what you can’t measure. There’s no process in place to deliver data on the amount of plastic polluting beaches today – and no one really knows if siloed beach cleanup efforts are even making a dent.
Establishing a baseline for marine plastics means we need to answer some critical questions:
- How much is plastic pollution advancing year over year?
- How do plastic waste levels fluctuate between different countries or states?
- Which policies – like locally banning plastics – effectively reduce marine litter?
- Where is the problem getting better and where is it getting worse?
And most important, in places where plastic pollution is most severe, which volunteer efforts will make a substantial impact?
Learn what happened when the IBM Data Science and AI Elite team (DSE) joined forces with the UNEP and the Wilson Center to sort through oceans of data to understand the world’s most insidious litter culprit. And meet the world’s first virtual environmental advocate – Sam – designed to unify researchers, communities and policymakers –to get all stakeholders on board. Dive into this conversation to learn how IBM sank three massive data challenges in the world’s fight against marine litter.
Challenge 1: Uniting the world’s ocean litter data
Estimating the volume of marine litter scattered across all five oceans is harder than it seems. No standard marine litter data collection method exists to guide countries and organizations. So we needed to harmonize tons of schemas and metadata so data reported from all corners of the world could be used.
To foster more effective collaboration between all stakeholders, UNEP set a key objective to establish a global platform for marine litter. With IBM Watson® Knowledge Catalog (WKC) on IBM Cloud Pak for Data we were able to automatically clean, crosswalk, classify, conform and make the right data available quickly for data scientists. WKC also allows citizen scientists to trace origins of the data, collaborate with other scientists, request datasets and share their insights on the dataset using rating and tagging mechanisms.
Challenge 2: Conquering conditional datasets to preserve the health of beaches
The second challenge was to calculate the volume of marine beach litter. Statistically randomized surveys help create accurate scientific estimates, but data collection about litter is, by its very nature, random. Heavily reliant on volunteer cleanup crews, data about cleanup efforts can be shaped by temporal and spatial biases. For example, one volunteer collects beach litter daily. What they collect each day will differ from what someone who collects weekly or monthly may find, leading to samples highly dependent on myriad variables, thus difficult to compare and analyze.
And clean-up efforts are inconsistent across locations, with some places cleaned too frequently, others rarely or never touched, indicating that samples are neither independent nor identically distributed (IID). Such conditional datasets prevent problem resolution using typical machine learning method.
To address these challenges, the DSE team utilized the Bayesian Inference method with Markov Chain Monte Carlo (MCMC) sampling techniques. The Bayesian approach allowed us to account for uncertainties in the problem; MCMC allows us to create a chain of dependent events to estimate the parameters of marine litter. This proof of concept revealed this unique hybrid methodology could be adjusted and modified to enhance the model’s strength.
The DSE created a machine learning pipeline in IBM Cloud Pak for Data to establish a streamlined end-to-end AI lifecycle. Once we established a baseline for measuring marine litter, the team could predict the number of volunteers needed for a cleanup effort at a particular beach. Given current trends and policies the model will help project the amount of expected litter five years into the future.
Challenge 3: Looking ahead to shore up prevention and support
The best way to solve the marine litter problem is to prevent it. Looking forward, how can we forestall permanent damage to pristine coastlines? The DSE team created a time-series forecast to help communities track marine plastic and develop more accurate and effective policy to eradicate it. To make the dataset easily consumable, our team created an executive dashboard allowing various stakeholders to:
- Monitor the progression of marine litter density year over year
- Slice and dice the data by national location to evaluate litter trends over time
- Narrow focus to specific beaches for more granular data collection
- Refine methodology to recommend the best mobile apps to volunteer groups.
To help drive a more effective marine debris collection effort, this dashboard provides users with a comprehensive view of cleanup efforts and displays their impact relative to other areas.
From proof of concept to production: the future of data-driven cleanup efforts
The DSE team presented this pilot solution to the steering committee of the Global Platform on Marine Litter, an expert panel of multi-disciplinary marine scientists and statisticians, and on World Ocean Day in Oslo to the United Nations Environmental Assembly – the world’s highest governing body on the environment. The event was livestreamed to over 4,000 participants from across the globe.
By harnessing the power of technology to battle plastic pollution, IBM demonstrated to The United Nations its commitment to the preservation of the environment – emphasizing that AI can be a vital tool for measuring future progress and influence direct policy on marine plastic interventions toward building a sustainable marine ecosystem.
For more information, read the company’s 30th annual report on the environment, 2019 IBM and the Environment Report.
Related reading: IBM expands data and AI excellence with data cataloging technology in Cloud Pak for Data“