Slowing down climate change is an urgent matter. If we fail, our world will face a more extensive crisis than we experienced because of the global COVID-19 pandemic. When artificial intelligence (AI) technology helps solve a problem, problem-solving can be done quicker, and the solution is often one that would have taken longer for humans to discover. There's no time to waste: atmospheric CO2 levels are the highest ever (even with significant drops from the stay-at-home orders for COVID-19), average sea levels are rising (3 inches in the last 25 years alone), and 2019 was the hottest year on record for the world's oceans. Artificial intelligence isn't a silver bullet, but it can certainly help us reduce greenhouse gas (GHG) emissions in various ways.

Hydropower has been stirring up controversies since the early 2000s. Despite being promoted as a solution to mitigate climate change, the hydropower bubble burst when researchers discovered in 2005 that hydropower dams are responsible for huge amounts of greenhouse gas emissions. Hydropower dams' walls restrict the flow of rivers and turn them into pools of stagnant water. Reservoir surfaces and turbines then release methane into the atmosphere. Methane makes up approximately 80 percent of the greenhouse gases emitted from hydropower dams, peaking in the first decade of the dams lifecycle.

Artificial intelligence (AI) isn't just transforming the world -- it's helping protect and preserve the future of the Amazon River. Rapid hydropower expansion has radically altered the Amazon River. When the natural flow of a river is altered, there are often serious, cascading changes. Now, AI and other computer science tools can help reduce these adverse and devastating effects on the environment, according to new research published in Science. FIU researcher Elizabeth Anderson was a part of a collaborative team of scientists from across the United States, Europe and South America who examined how cutting-edge technology can inform more sustainable and strategic planning.

Slowing down climate change is an urgent matter. If we fail, our world will face a more extensive crisis than we experienced because of the global COVID-19 pandemic. When artificial intelligence (AI) technology helps solve a problem, problem-solving can be done quicker, and the solution is often one that would have taken longer for humans to discover. There's no time to waste: atmospheric CO2 levels are the highest ever (even with significant drops from the stay-at-home orders for COVID-19), average sea levels are rising (3 inches in the last 25 years alone), and 2019 was the hottest year on record for the world's oceans. Artificial intelligence isn't a silver bullet, but it can certainly help us reduce greenhouse gas (GHG) emissions in various ways.

H3 Dynamics has partnered with Curitiba-based EPH Engineering in Brazil, a firm that specializes in hydropower design, dam inspections and safety plans, to launch a turnkey dam inspection solution that combines AI-enabled damage assessment and HYCOPTER fuel cell drones capable of flying 3.5 hours at a time. With over 5,000 dams submitted to the Brazilian Dam Safety Plan, and two recent collapse incidents causing more than 300 deaths and major environmental damage, Brazilian authorities have tightened inspection and upkeep requirements in the country. "Many accident reports show that problems were not detected by instrumentation but by visual observation. Drones can help, but due to the large dimensions of these structures we need much longer flight times." Some of the dams are so large that they would require months of battery-powered drone flights to fully scan their surfaces.

Real-world problems are often not fully characterized by a single optimal solution, as they frequently involve multiple competing objectives; it is therefore important to identify the so-called Pareto frontier, which captures solution trade-offs. We propose a fully polynomial-time approximation scheme based on Dynamic Programming (DP) for computing a polynomially succinct curve that approximates the Pareto frontier to within an arbitrarily small epsilon > 0 on tree-structured networks. Given a set of objectives, our approximation scheme runs in time polynomial in the size of the instance and 1/epsilon. We also propose a Mixed Integer Programming (MIP) scheme to approximate the Pareto frontier. The DP and MIP Pareto frontier approaches have complementary strengths and are surprisingly effective. We provide empirical results showing that our methods outperform other approaches in efficiency and accuracy. Our work is motivated by a problem in computational sustainability concerning the proliferation of hydropower dams throughout the Amazon basin. Our goal is to support decision-makers in evaluating impacted ecosystem services on the full scale of the Amazon basin. Our work is general and can be applied to approximate the Pareto frontier of a variety of multiobjective problems on tree-structured networks.