Spatially constrained geodesign optimization (GOP) for improving agricultural watershed sustainability

Yiqun Xie, Kwang Soo Yang, Shashi Shekhar, Brent J Dalzell, D J Mulla

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


Given an agricultural watershed containing a set of spatial units, and a set of land management practices, the Geodesign Optimization (GOP) aims to find a land management practice for each spatial unit that optimizes overall water quality improvements in the watershed under both budget constraint and spatial constraints (e.g., minimum contiguous area, shape) arising from farm equipment operation practicalities. GOP is important for redesign of agricultural watersheds in Midwestern US to mitigate soil and water quality degradation and loss of habitat. The problem is computationally challenging as a large-scale combinatorial problem (NP-hard) under spatial constraints. Existing optimization techniques do not address spatial constraints, and lead to impractical solutions requiring frequent farm equipment reconfiguration. In this paper, we formalize the spatially-constrained GOP and propose a novel spatial optimizer which explores optimal solution without constraint violations. Our approach is further validated through a Geodesign case study at Seven Mile Creek watershed in Midwestern US.

Original languageEnglish (US)
Title of host publicationWS-17-01
Subtitle of host publicationArtificial Intelligence and Operations Research for Social Good; WS-17-02: Artificial Intelligence, Ethics, and Society; WS-17-03: Artificial Intelligence for Connected and Automated Vehicles; WS-17-04: Artificial Intelligence for Cyber Security; WS-17-05: Artificial Intelligence for Smart Grids and Buildings; WS-17-06: Computer Poker and Imperfect Information Games; WS-17-07: Crowdsourcing, Deep Learning and Artificial Intelligence Agents; WS-17-08: Distributed Machine Learning; WS-17-09: Joint Workshop on Health Intelligence; WS-17-10: Human-Aware Artificial Intelligence; WS-17-11: Human-Machine Collaborative Learning; WS-17-12: Knowledge-Based Techniques for Problem Solving and Reasoning; WS-17-13: Plan, Activity, and Intent Recognition; WS-17-14: Symbolic Inference and Optimization; WS-17-15: What's Next for AI in Games?
PublisherAI Access Foundation
Number of pages7
ISBN (Electronic)9781577357865
StatePublished - Jan 1 2017
Event31st AAAI Conference on Artificial Intelligence, AAAI 2017 - San Francisco, United States
Duration: Feb 4 2017Feb 10 2017

Publication series

NameAAAI Workshop - Technical Report
VolumeWS-17-01 - WS-17-15


Other31st AAAI Conference on Artificial Intelligence, AAAI 2017
Country/TerritoryUnited States
CitySan Francisco


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