Abstract
Major societal and environmental challenges involve complex systems that have diverse multi-scale interacting processes. Consider, for example, how droughts and water reserves affect crop production and how agriculture and industrial needs affect water quality and availability. Preventive measures, such as delaying planting dates and adopting new agricultural practices in response to changing weather patterns, can reduce the damage caused by natural processes. Understanding how these natural and human processes affect one another allows forecasting the effects of undesirable situations and study interventions to take preventive measures. For many of these processes, there are expert models that incorporate state-of-the-art theories and knowledge to quantify a system's response to a diversity of conditions. A major challenge for efficient modeling is the diversity of modeling approaches across disciplines and the wide variety of data sources available only in formats that require complex conversions. Using expert models for particular problems requires integration of models with third-party data as well as integration of models across disciplines. Modelers face significant heterogeneity that requires resolving semantic, spatiotemporal, and execution mismatches, which are largely done by hand today and may take more than 2 years of effort. We are developing a modeling framework that uses artificial intelligence (AI) techniques to reduce modeling effort while ensuring utility for decision making. Our work to date makes several innovative contributions: (1) an intelligent user interface that guides analysts to frame their modeling problem and assists them by suggesting relevant choices and automating steps along the way; (2) semantic metadata for models, including their modeling variables and constraints, that ensures model relevance and proper use for a given decision-making problem; and (3) semantic representations of datasets in terms of modeling variables that enable automated data selection and data transformations. This framework is implemented in the MINT (Model INTegration) framework, and currently includes data and models to analyze the interactions between natural and human systems involving climate, water availability, agricultural production, and markets. Our work to date demonstrates the utility of AI techniques to accelerate modeling to support decision-making and uncovers several challenging directions for future work.
| Original language | English (US) |
|---|---|
| Article number | 11 |
| Journal | ACM Transactions on Interactive Intelligent Systems |
| Volume | 11 |
| Issue number | 2 |
| DOIs | |
| State | Published - Jul 19 2021 |
Bibliographical note
Funding Information:This research was funded by the Defense Advanced Research Projects Agency with grant no. W911NF-18-1-0027, the Planet Texas 2050 program of The University of Texas at Austin, and the National Science Foundation with grant no. ICER-1440323. Authors’ addresses: Y. Gil (corresponding author), D. Garijo, D. Khider, C. A. Knoblock, V. Ratnakar, M. Osorio, H. Vargas, M. Pham, J. Pujara, B. Shbita, B. Vu, E. Deelman, R. F. Da Silva, and R. Mayani, Information Sciences Institute, University of Southern California, Marina del Rey, CA 90292; emails: {gil, dgarijo, dkhider, knoblock, varunr, mosorio}@isi.edu, [email protected], [email protected], [email protected], {shbita, binhlvu}@usc.edu; Y.-Y. Chiang, D. Feldman, Y. Lin, and H. Song, Spatial Sciences Institute, University of Southern California, Los Angeles, CA 90089; emails: {yaoyic, danf, yijunlin, [email protected]; V. Kumar, A. Khandelwal, M. Steinbach, K. Tayal, and S. Xu, Department of Computer Science, University of Minnesota, Minneapolis, MN 55455; emails: [email protected], {khand035, stei0062, tayal, xu000114}@umn.edu; S. A. Pierce, L. Pearson and D. Hardesty-Lewis, Texas Advanced Computing Center, University of Texas at Austin, Austin, TX 78758; emails: [email protected], [email protected], [email protected]; A. R. Kemanian, Y. Shi, and L. Leonard, Department of Plant Science, The Pennsylvania State University, University Park, PA 16802; emails: {kxa15, yshi, lnl3}@psu.edu; S. Peckham and M. Stoica, Institute of Arctic and Alpine Research, University of Colorado, Boulder, CO 80309; emails: {Scott.Peckham, Stoica}@colorado.edu; K. Cobourn and Z. Zhang,Department of Forest Resources and Environmental Conservation, Virginia Tech, Blacksburg, VA 24061; emails: {kellyc13, zzeya7}@vt.edu; C. Duffy, Department of Civil Engineering, The Pennsylvania State University, University Park, PA 16802; email: [email protected]; L. Shu, Department of Land, Air and Water Resources, University of California Davis, Davis, CA 95616; email: [email protected]. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]. © 2021 Association for Computing Machinery. 2160-6455/2021/06-ART11 $15.00 https://doi.org/10.1145/3453172
Publisher Copyright:
© 2021 Association for Computing Machinery.
Keywords
- Intelligent user interfaces
- integrated modeling
- model metadata
- regional-level decision-making
- remote sensing data
