Geology topics

Qinru Shi

Publications and source records attributed to Qinru Shi.

5 recordsLinked to original sources

USGS2021

Climate change may impair electricity generation and economic viability of future Amazon hydropower

Numerous hydropower facilities are under construction or planned in tropical and subtropical rivers worldwide. While dams are typically designed considering historic river discharge regimes, climate change may induce large-scale alterations in river hydrology. Here we analyze how future climate change will affect river hydrology, electricity generation, and economic viability of > 350 potential hydropower dams across the Amazon, Earth’s largest river basin and a global hotspot for future hydropower development. Midcentury projections for the RCP 4.5 and 8.5 climate change scenarios show basin-wide reductions of river discharge (means, 13 and 16%, respectively) and hydropower generation (19 and 27%). Declines are sharper for dams in Brazil, which harbors 60% of the proposed projects. Climate change will cause more frequent low-discharge interruption of hydropower generation and less frequent full-capacity operation. Consequently, the minimum electricity sale price for projects to break even more than doubles at many proposed dams, rendering much of future Amazon hydropower less competitive than increasingly lower cost renewable sources such as wind and solar. Climate-smart power systems will be fundamental to support environmentally and financially sustainable energy development in hydropower-dependent regions.

Global Environmental Change↗

USGS2021

Learning augmented methods for matching: Improving invasive species management and urban mobility

With the success of machine learning, integrating learned models into real-world systems has become a critical chal- lenge. Naively applying predictions to combinatorial opti- mization problems can incur high costs, which has motivated researchers to consider learning augmented algorithms that can make use of faulty or incomplete predictions. Inspired by two matching problems in computational sustainability where data are abundant, we consider the learning augmented min weight matching problem where some nodes are revealed online while others are known a priori, e.g., by being pre- dicted by machine learning. We develop an algorithm that is able to make use of this extra information and provably im- proves upon pessimistic online algorithms. We evaluate our algorithm on two settings from computational sustainability – the coordination of opportunistic citizen scientists for inva- sive species management and the matching between taxis and riders under uncertain trip duration predictions. In both cases, we perform extensive experiments on real-world datasets and find that our method outperforms baselines, showing how learning augmented algorithms can reliably improve solu- tions for problems in computational sustainability

New York↗

USGS2021

Accelerating ecological sciences from above: Spatial contrastive learning for remote sensing

The rise of neural networks has opened the door for automatic analysis of remote sensing data. A challenge to using this machinery for computational sustainability is the necessity of massive labeled data sets, which can be cost-prohibitive for many non-profit organizations. The primary motivation for this work is one such problem; the efficient management of invasive species -- invading flora and fauna that are estimated to cause damages in the billions of dollars annually. As an ongoing collaboration with the New York Natural Heritage Program, we consider the use of unsupervised deep learning techniques for dimensionality reduction of remote sensing images, which can reduce sample complexity for downstream tasks and decreases the need for large labeled data sets. We consider spatially augmenting contrastive learning by training neural networks to correctly classify two nearby patches of a landscape as such. We demonstrate that this approach improves upon previous methods and naive classification for a large-scale data set of remote sensing images derived from invasive species observations obtained over 30 years. Additionally, we simulate deployment in the field via active learning and evaluate this method on another important challenge in computational sustainability -- landcover classification -- and again find that it outperforms previous baselines.

Proceedings of the AAAI Conference on Artificial I↗

USGS2019

Reducing greenhouse gas emissions of Amazon hydropower with strategic dam planning

Hundreds of dams have been proposed throughout the Amazon basin, one of the world’s largest untapped hydropower frontiers. While hydropower is a potentially clean source of renewable energy, some projects produce high greenhouse gas (GHG) emissions per unit electricity generated (carbon intensity). Here we show how carbon intensities of proposed Amazon upland dams (median = 39 kg CO 2 eq MWh −1 , 100-year horizon) are often comparable with solar and wind energy, whereas some lowland dams (median = 133 kg CO 2 eq MWh −1 ) may exceed carbon intensities of fossil-fuel power plants. Based on 158 existing and 351 proposed dams, we present a multi-objective optimization framework showing that low-carbon expansion of Amazon hydropower relies on strategic planning, which is generally linked to placing dams in higher elevations and smaller streams. Ultimately, basin-scale dam planning that considers GHG emissions along with social and ecological externalities will be decisive for sustainable energy development where new hydropower is contemplated.

Nature Communications↗

USGS2018

Efficiently approximating the Pareto frontier: Hydropower dam placement in the Amazon basin

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 > 0 on treestructured networks. Given a set of objectives, our approximation scheme runs in time polynomial in the size of the instance and 1/. 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.

Conference Paper↗