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Wireless sensor networks support decision-making in diverse environmental contexts. Adoption of these networks has increased dramatically due to technological advances that have increased value while lowering cost. However, real-time information only allows for reactive management. As most interventions take time, predictions across these sensor networks enable better planning and decision making. Prediction models across large water level and discharge sensor networks do exist. However, they have limitations in their accessibility, automaticity, and data requirements. We present an open-source method for automatically generating computationally cheap rainfall-runoff models for any depth or discharge sensor given only its measurements and location. We characterize reliability in a real-world case study across 200,000 km, evaluate long-term accuracy, and assess sensitivity to measurement noise and errors in catchment delineation. The method’s accuracy, computational efficiency, and automaticity make it a valuable asset to support operational decision making for diverse stakeholders including bridge inspectors and utilities.
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OPRA: An Open-Source Online Preference Reporting and Aggregation System
We introduce the Online Preference Reporting and Aggregation (OPRA) system, an open-source online system that aims at providing support for group decision-making. We illustrate OPRA's distinctive features: UI for reporting rankings with ties, comprehensive analytics of preferences, and group decision-making in combinatorial domains. We also discuss our work in an automatic mentor matching system. We hope that the open-source nature of OPRA will foster development of computerized group decision support systems.
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- PAR ID:
- 10382697
- Date Published:
- Journal Name:
- Proceedings of the AAAI Conference on Artificial Intelligence
- Volume:
- 35
- Issue:
- 18
- ISSN:
- 2159-5399
- Page Range / eLocation ID:
- 16011 to 16013
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1609/aaai.v35i18.17996
