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.

Suresh Sethi, associate professor of natural resources and the environment in CALS; Carla Gomes, professor of computer science at Cornell Bowers CIS; and Alex Flecker, professor of ecology and evolutionary biology in CALS, are pictured on a field trip to the Marañón River, in Peru.

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.

Geoglyphs in the southern Amazon are evidence of a once-thriving population. Before Spanish invaders conquered South America, sparse groups of nomadic people clustered around the Amazon River, leaving the surrounding rain forest pristine and untouched. New research suggests a very different story--an Amazonian region peppered with rain forest villages, ceremonial earthworks, and a much larger population than previously thought. The research, funded in part by the National Geographic Society and published today in the journal Nature Communications, challenges a common perception of the pre-Columbian Amazon rain forest as sparsely populated. That perception has endured despite 16th-century accounts of large, interconnected villages that go against modern assumptions.

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.