Award Abstract # 1850546
CRII: III: Disciplinary Knowledge Guided Big Spatial Structured Models for Geoscience Applications

NSF Org: IIS
Division of Information & Intelligent Systems
Recipient: UNIVERSITY OF ALABAMA
Initial Amendment Date: July 29, 2019
Latest Amendment Date: February 20, 2020
Award Number: 1850546
Award Instrument: Standard Grant
Program Manager: Wei-Shinn Ku
IIS
 Division of Information & Intelligent Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: August 1, 2019
End Date: October 31, 2021 (Estimated)
Total Intended Award Amount: $175,000.00
Total Awarded Amount to Date: $191,000.00
Funds Obligated to Date: FY 2019 = $83,199.00
FY 2020 = $16,000.00
History of Investigator:
  • Zhe Jiang (Principal Investigator)
    zhe.jiang@ufl.edu
Recipient Sponsored Research Office: University of Alabama Tuscaloosa
801 UNIVERSITY BLVD
TUSCALOOSA
AL  US  35401
(205)348-5152
Sponsor Congressional District: 07
Primary Place of Performance: University of Alabama Tuscaloosa
801 University Blvd
Tuscaloosa
AL  US  35087-0005
Primary Place of Performance
Congressional District:
04
Unique Entity Identifier (UEI): RCNJEHZ83EV6
Parent UEI: RCNJEHZ83EV6
NSF Program(s): Info Integration & Informatics
Primary Program Source: 01001920DB NSF RESEARCH & RELATED ACTIVIT
01002021DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 7364, 8228, 9150, 9251
Program Element Code(s): 736400
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

The goal of this project is to investigate novel computational techniques for disciplinary knowledge guided data science methods in geoscience applications. The field of data science has achieved tremendous success over the last decade, not only in business but also in science and engineering. The data-driven approach has been recognized as the "fourth paradigm" of scientific discovery (after experimental, theoretical, and computational simulation). However, when solving interdisciplinary problems, a purely data-driven approach often faces a significant gap in lacking interpretability and consistency with existing theories and knowledge in the discipline, as shown by the famous Google Flue Trend example. The proposed project aims to fill the gap by utilizing disciplinary knowledge to guide data-driven models to enhance interpretability, consistency, as well as prediction accuracy. Specifically, the team will study the problem in the context of spatial structured models for geoscience applications. The team will investigate the utilization of disciplinary knowledge in constructing novel spatial dependency structure and explore efficient algorithms for model learning and inference. Proposed approaches will be validated with interdisciplinary applications in hydrology. The project, if successful, will contribute towards the next generation water resource management for the U.S. in the 21st century. Proposed research can not only improve the situational awareness for disaster response agencies but also enhance the flood forecasting capabilities of the National Water Model. Proposed algorithms will be implemented into open source tools that will enhance the research infrastructure for geoscience communities. Educational activities include curriculum development, mentoring a broad group of high school students in data science seminars at Alabama Computer Science Camps, as well as year-long project for a selected number of high school students for regional Science Fair competition.

The project is expected to result in the following computer science innovations. First, a novel spatial structured model called hidden Markov topography tree (HMTT) will be investigated, which generalizes existing hidden Markov models from total order sequences to partial order poly-trees. Compared with existing spatial structured models (e.g., Markov random field, spatial autoregressive regression) that captures dependency based on spatial proximity, HMTT can potentially reduce the impacts of noise and large obstacles in sample features via more complex structural constraints from disciplinary knowledge in hydrology (e.g., flow directions). Second, efficient computational algorithms to construct topography tree from a large number of locations will be explored. Finally, the team will leverage the poly-tree structure in the hidden class layer, and explore computational pruning to reduce the number of backtracking in existing dynamic programming method for class inference. The idea of integrating disciplinary knowledge (e.g., structural constraints) with data-driven methods can potentially transform data science research by enhancing model interpretability and consistency.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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(Showing: 1 - 10 of 13)
Adhikari, Saugat and Yan, Da and Sami, Mirza Tanzim and Khalil, Jalal and Yuan, Lyuheng and Joy, Bhadhan Roy and Jiang, Zhe and Sainju, Arpan Man "An elevation-guided annotation tool for flood extent mapping on earth imagery (demo paper)" SIGSPATIAL '22: Proceedings of the 30th International Conference on Advances in Geographic Information Systems , 2022 https://doi.org/10.1145/3557915.3560962 Citation Details
He, Wenchong and Jiang, Zhe "Semi-supervised Learning with the EM Algorithm: A Comparative Study between Unstructured and Structured Prediction" IEEE Transactions on Knowledge and Data Engineering , 2020 https://doi.org/10.1109/TKDE.2020.3019038 Citation Details
He, Wenchong and Jiang, Zhe and Kriby, Marcus and Xie, Yiqun and Jia, Xiaowei and Yan, Da and Zhou, Yang "Quantifying and Reducing Registration Uncertainty of Spatial Vector Labels on Earth Imagery" Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , 2022 https://doi.org/10.1145/3534678.3539410 Citation Details
He, Wenchong and Sainju, Arpan Man and Jiang, Zhe and Yan, Da "Deep Neural Network for 3D Surface Segmentation based on Contour Tree Hierarchy" Proceedings of the 2021 SIAM International Conference on Data Mining (SDM) , 2021 https://doi.org/10.1137/1.9781611976700.29 Citation Details
Jiang, Zhe "Spatial Structured Prediction Models: Applications, Challenges, and Techniques" IEEE Access , v.8 , 2020 https://doi.org/10.1109/ACCESS.2020.2975584 Citation Details
Jiang, Zhe and Sainju, Arpan Man "A Hidden Markov Tree Model for Flood Extent Mapping in Heavily Vegetated Areas based on High Resolution Aerial Imagery and DEM: A Case Study on Hurricane Matthew Floods" International Journal of Remote Sensing , v.42 , 2021 https://doi.org/10.1080/01431161.2020.1823514 Citation Details
Jiang, Zhe and Sainju, Arpan Man "Hidden Markov Contour Tree: A Spatial Structured Model for Hydrological Applications" Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2019 https://doi.org/10.1145/3292500.3330878 Citation Details
Jiang, Zhe and Sainju, Arpan Man and Li, Yan and Shekhar, Shashi and Knight, Joseph "Spatial Ensemble Learning for Heterogeneous Geographic Data with Class Ambiguity" ACM Transactions on Intelligent Systems and Technology , v.10 , 2019 10.1145/3337798 Citation Details
Sainju, Arpan Man and He, Wenchong and Jiang, Zhe "A Hidden Markov Contour Tree Model for Spatial Structured Prediction" IEEE Transactions on Knowledge and Data Engineering , 2020 https://doi.org/10.1109/TKDE.2020.3002887 Citation Details
Sainju, Arpan Man and He, Wenchong and Jiang, Zhe and Yan, Da "Spatial Classification with Limited Observations Based on Physics-Aware Structural Constraint" Proceedings of the AAAI Conference on Artificial Intelligence , v.34 , 2020 https://doi.org/10.1609/aaai.v34i01.5436 Citation Details
Sainju, Arpan Man and He, Wenchong and Jiang, Zhe and Yan, Da and Chen, Haiquan "Flood Inundation Mapping with Limited Observations Based on Physics-Aware Topography Constraint" Frontiers in Big Data , v.4 , 2021 https://doi.org/10.3389/fdata.2021.707951 Citation Details
(Showing: 1 - 10 of 13)

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