Computational sustainability

Gomes, Carla P.; Dietterich, Thomas G.; Barrett, Christopher B.; Conrad, Jon M.; Dilkina, Bistra; Ermon, Stefano; Fang, Fei; Farnsworth, Andrew; Fern, Alan; Fern, Xiaoli Z.; Fink, Daniel; Fisher, Douglas H.; Flecker, Alexander S.; Freund, Daniel; Fuller, Angela K.; Gregoire, John M.; Hopcroft, John E.; Kelling, Steve; Kolter, Zico; Powell, Warren B.; Sintov, Nicole D.; Selker, John S.; Selman, Bart; Sheldon, Daniel; Shmoys, David B.; Tambe, Milind; Wong, Weng‐Keen; Wood, Christopher; Wu, Xiaojian; Xue, Yexiang; Yadav, Amulya; Yakubu, Abdul‐Aziz; Zeeman, Mary Lou

doi:10.1145/3339399

Cited by 57 publications

(13 citation statements)

References 27 publications

(30 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sustainability issues have long been politically ignored, but became much more relevant in recent years. As a result, this societal challenge has recently started to get traction in computer science (Gomes et al, 2019) and natural language processing (Conforti et al, 2020), where only few previous works study methods to support SDGs: Conforti et al (2020) classify user-perceived values on unstructured interview text in order to gather structured data about the people's subjective values. This is performed in developing countries to increase the success of sustainability projects, each targeted at one or more SDGs, by aligning them to the encountered values, so that the projects will be more likely to be continued by the community after their initial implementation.…”

Section: Foundations and Related Workmentioning

confidence: 99%

Supporting Land Reuse of Former Open Pit Mining Sites using Text Classification and Active Learning

Schröder¹,

Annanias²,

Niekler³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

View full text Add to dashboard Cite

Open pit mines left many regions worldwide inhospitable or uninhabitable. Many sites are left behind in a hazardous or contaminated state, show remnants of waste, or have other restrictions imposed upon them, e.g., for the protection of human or nature. Such information has to be permanently managed in order to reuse those areas in the future. In this work we present and evaluate an automated workflow for supporting the post-mining management of former lignite open pit mines in the eastern part of Germany, where prior to any planned land reuse, aforementioned information has to be acquired to ensure the safety and validity of such an endeavor. Usually, this information is found in expert reports, either in the form of paper documents, or in the best case as digitized unstructured text-all of them in German language. However, due to the size and complexity of these documents, any inquiry is tedious and time-consuming, thereby slowing down or even obstructing the reuse of related areas. Since no training data is available, we employ active learning in order to perform multi-label sentence classification for two categories of restrictions and seven categories of topics. The final system integrates optical character recognition (OCR), active-learningbased text classification, and geographic information system visualization in order to effectively extract, query, and visualize this information for any area of interest. Active learning and text classification results are twofold: Whereas the restriction categories were reasonably accurate (>0.85 F1), the seven topicoriented categories seemed to be complex even for human annotators and achieved mediocre evaluation scores (<0.70 F1).

show abstract

Section: Foundations and Related Workmentioning

confidence: 99%

Supporting Land Reuse of Former Open Pit Mining Sites using Text Classification and Active Learning

Schröder¹,

Annanias²,

Niekler³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

View full text Add to dashboard Cite

show abstract

“…At the same time, there is very exciting work that is more recent that I wanted to share with you on AI in science. The computational sustainability community is developing new approaches in multi-agent systems, constraint reasoning, machine learning, and optimization to address a range of environmental problems (e.g., Gomes et al 2019). There is also very important work on materials discovery, where from automated text extraction from published articles they are able to identify particular molecules, recreate the periodic table, and actually predict discoveries that have occurred in the past just from looking at the trends in the literature (e.g., Tshitoyan et al 2019).…”

Section: The Imperative For Ai In Sciencementioning

confidence: 99%

Will AI write scientific papers in the future?

Gil

2021

AI Magazine

View full text Add to dashboard Cite

In this presidential address, I would like to start with a personal reflection on the field and then share with you the research directions I am pursuing and my excitement about the future of AI. In my personal research to advance AI while advancing scientific discoveries, one question that I have been pondering for some years now is whether AI will write scientific papers in the future. I want to reflect on this question, and look back at the many accomplishments in our field that can make us very hopeful that the answer will be yes, and that it may happen sooner than we might expect.

show abstract

“…Computational Sustainability (Gomes et al 2019) is a field within Computer Science that aims to use Artificial Intelligence to work towards a sustainable future. Problems in Computational Sustainability often require finding a balance between conflicting concerns, as captured for example, in the Sustainable Development Goals that aim to ensure economic and social equity across all strata of society while Copyright © 2024, Association for the Advancement of Artificial Intelligence (www.aaai.org).…”

Section: Introductionmentioning

confidence: 99%

Scaling Up Pareto Optimization for Tree Structures with Affine Transformations: Evaluating Hybrid Floating Solar-Hydropower Systems in the Amazon

Grimson,

Almeida,

Shi

et al. 2024

AAAI

View full text Add to dashboard Cite

Sustainability challenges inherently involve the consideration of multiple competing objectives. The Pareto frontier – the set of all optimal solutions that cannot be improved with respect to one objective without negatively affecting another – is a crucial decision-making tool for navigating sustainability challenges as it highlights the inherent trade-offs among conflicting objectives. Our research is motivated by the strategic planning of hydropower in the Amazon basin, one of the earth’s largest and most biodiverse river systems, where the need to increase energy production coincides with the pressing requirement of minimizing detrimental environmental impacts. We investigate an innovative strategy that pairs hydropower with Floating Photovoltaic Solar Panels (FPV). We provide a new extended multi-tree network formulation, which enables the consideration of multiple dam configurations. To address the computational challenge of scaling up the Pareto optimization framework to tackle multiple objectives across the entire Amazon basin, we further enhance the state-of-the-art algorithm for Pareto frontiers in tree-structured networks with two improvements. We introduce affine transformations induced by the sub-frontiers to compute Pareto dominance and provide strategies for merging sub-trees, significantly increasing the pruning of dominated solutions. Our experiments demonstrate considerable speedups, in some cases by more than an order of magnitude, while maintaining optimality guarantees, thus allowing us to more effectively approximate the Pareto frontiers. Moreover, our findings suggest significant shifts towards higher energy values in the Pareto frontier when pairing hybrid hydropower with FPV solutions, potentially amplifying energy production while mitigating adverse impacts.

show abstract

Computational sustainability

Abstract: Computer and information scientists join forces with other fields to help solve societal and environmental challenges facing humanity, in pursuit of a sustainable future.

Cited by 57 publications

References 27 publications

Supporting Land Reuse of Former Open Pit Mining Sites using Text Classification and Active Learning

Supporting Land Reuse of Former Open Pit Mining Sites using Text Classification and Active Learning

Will AI write scientific papers in the future?

Scaling Up Pareto Optimization for Tree Structures with Affine Transformations: Evaluating Hybrid Floating Solar-Hydropower Systems in the Amazon

Contact Info

Product

Resources

About