CRII: III: Disciplinary Knowledge Guided Big Spatial Structured Models for Geoscience Applications

CRII：III：学科知识引导的地球科学应用大空间结构化模型

• 批准号: 1850546
• 负责人: Zhe Jiang
• 金额: $ 17.5万
• 依托单位: University of Alabama Tuscaloosa
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2021-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1850546&HistoricalAwards=false
• 关键词: CRII; III; Disciplinary; Knowledge; Guided

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.

该项目的目标是研究地球科学应用中学科知识引导的数据科学方法的新型计算技术。数据科学领域在过去十年中取得了巨大的成功，不仅在商业领域，而且在科学和工程领域。数据驱动方法已被公认为科学发现的“第四范式”（继实验、理论和计算模拟之后）。然而，在解决跨学科问题时，纯粹的数据驱动方法往往面临着缺乏可解释性以及与学科现有理论和知识的一致性的巨大差距，正如著名的谷歌烟气趋势例子所示。该项目旨在通过利用学科知识指导数据驱动模型来增强可解释性、一致性和预测准确性来填补这一空白。具体来说，该团队将在地球科学应用的空间结构化模型的背景下研究该问题。该团队将研究学科知识在构建新型空间依赖结构中的利用，并探索模型学习和推理的有效算法。拟议的方法将通过水文学的跨学科应用进行验证。该项目如果成功，将为美国21世纪下一代水资源管理做出贡献。拟议的研究不仅可以提高灾害响应机构的态势感知能力，还可以增强国家水模型的洪水预报能力。拟议的算法将被实施到开源工具中，这将增强地球科学界的研究基础设施。教育活动包括课程开发、在阿拉巴马州计算机科学营的数据科学研讨会上指导广大高中生，以及为部分高中生参加地区科学博览会竞赛提供为期一年的项目。该项目预计将带来以下计算机科学创新。首先，将研究一种称为隐马尔可夫拓扑树（HMTT）的新型空间结构模型，它将现有的隐马尔可夫模型从全序序列推广到偏序多树。与基于空间邻近性捕获依赖性的现有空间结构化模型（例如马尔可夫随机场、空间自回归）相比，HMTT 可以通过来自水文学学科知识（例如流向）的更复杂的结构约束来潜在地减少样本特征中噪声和大型障碍物的影响。其次，将探索从大量位置构建地形树的有效计算算法。最后，团队将利用隐藏类层中的多树结构，并探索计算剪枝以减少现有动态规划方法中用于类推理的回溯次数。将学科知识（例如结构约束）与数据驱动方法相结合的想法可以通过增强模型的可解释性和一致性来潜在地改变数据科学研究。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

An elevation-guided annotation tool for flood extent mapping on earth imagery (demo paper)

用于在地球图像上绘制洪水范围的高程引导注释工具（演示论文）

• DOI: 10.1145/3557915.3560962
• 发表时间: 2022
• 期刊: SIGSPATIAL '22: Proceedings of the 30th International Conference on Advances in Geographic Information Systems
• 作者: Adhikari, Saugat;Yan, Da;Sami, Mirza Tanzim;Khalil, Jalal;Yuan, Lyuheng;Joy, Bhadhan Roy;Jiang, Zhe;Sainju, Arpan Man
• 通讯作者: Sainju, Arpan Man

Deep Neural Network for 3D Surface Segmentation based on Contour Tree Hierarchy

• DOI: 10.1137/1.9781611976700.29
• 发表时间: 2020-08
• 期刊: ArXiv
• 作者: Wenchong He;Arpan Man Sainju;Zhe Jiang;Da Yan

Semi-supervised Learning with the EM Algorithm: A Comparative Study between Unstructured and Structured Prediction

EM 算法的半监督学习：非结构化和结构化预测的比较研究

• DOI: 10.1109/tkde.2020.3019038
• 发表时间: 2020
• 期刊: IEEE Transactions on Knowledge and Data Engineering
• 影响因子: 8.9
• 作者: He, Wenchong;Jiang, Zhe
• 通讯作者: Jiang, Zhe

Spatial Structured Prediction Models: Applications, Challenges, and Techniques

• DOI: 10.1109/access.2020.2975584
• 发表时间: 2020-02
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Zhe Jiang

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

基于高分辨率航空图像和 DEM 的植被茂密地区洪水范围测绘的隐马尔可夫树模型：飓风马修洪水案例研究

• DOI: 10.1080/01431161.2020.1823514
• 发表时间: 2021
• 期刊: International Journal of Remote Sensing
• 影响因子: 3.4
• 作者: Jiang, Zhe;Sainju, Arpan Man
• 通讯作者: Sainju, Arpan Man