Conference proceeding
Decision-Making for Land Conservation: A Derivative-Free Optimization Framework with Nonlinear Inputs
Proceedings of the ... AAAI Conference on Artificial Intelligence, v 38(20), pp 21932-21939
25 Mar 2024
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Protected areas (PAs) are designated spaces where human activities are restricted to preserve critical habitats. Decision-makers are challenged with balancing a trade-off of financial feasibility with ecological benefit when establishing PAs. Given the long-term ramifications of these decisions and the constantly shifting environment, it is crucial that PAs are carefully selected with long-term viability in mind.
Using AI tools like simulation and optimization is common for designating PAs, but current decision models are primarily linear. In this paper, we propose a derivative-free optimization framework paired with a nonlinear component, population viability analysis (PVA). Formulated as a mixed integer nonlinear programming (MINLP) problem, our model allows for linear and nonlinear inputs. Connectivity, competition, crowding, and other similar concerns are handled by the PVA software, rather than expressed as constraints of the optimization model. In addition, we present numerical results that serve as a proof of concept, showing our models yield PAs with similar expected risk to that of preserving every parcel in a habitat, but at a significantly lower cost.
The overall goal is to promote interdisciplinary work by providing a new mathematical programming tool for conservationists that allows for nonlinear inputs and can be paired with existing ecological software. The code and data are available at https://github.com/cassiebuhler/conservation-dfo.
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Details
- Title
- Decision-Making for Land Conservation: A Derivative-Free Optimization Framework with Nonlinear Inputs
- Creators
- Cassidy K. Buhler - Drexel UniversityHande Y. Benson - Drexel University
- Contributors
- M Wooldridge (Editor)J Dy (Editor)S Natarajan (Editor)
- Publication Details
- Proceedings of the ... AAAI Conference on Artificial Intelligence, v 38(20), pp 21932-21939
- Series
- AAAI Conference on Artificial Intelligence
- Publisher
- Assoc Advancement Artificial Intelligence
- Number of pages
- 8
- Grant note
- LeBow College of Business Innovation Micro-Grant for DEI & Environmental Sustainability at Drexel University
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Decision Sciences (and Management Information Systems)
- Web of Science ID
- WOS:001239985800007
- Scopus ID
- 2-s2.0-85189610533
- Other Identifier
- 991022202516504721
UN Sustainable Development Goals (SDGs)
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Source: SDGs in the Output
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Interdisciplinary Applications
- Computer Science, Theory & Methods