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.

该项目的目的是研究地球科学应用中纪律知识的数据科学方法的新型计算技术。在过去的十年中，数据科学领域取得了巨大的成功，不仅在商业中，而且在科学和工程学方面。数据驱动的方法已被认为是科学发现的“第四个范式”（实验，理论和计算模拟之后）。但是，在解决跨学科问题时，纯粹的数据驱动方法通常会面临缺乏与该学科中现有理论和知识一致性的显着差距，如著名的Google Flue趋势示例所示。拟议的项目旨在通过利用纪律知识来指导数据驱动的模型来提高可解释性，一致性和预测准确性来填补空白。具体而言，团队将在地球科学应用的空间结构模型的背景下研究问题。该团队将研究纪律知识在构建新型空间依赖结构中的利用，并探索用于模型学习和推理的有效算法。建议的方法将通过水文学中的跨学科应用来验证。如果成功的话，该项目将为21世纪美国的下一代水资源管理做出贡献。拟议的研究不仅可以提高对灾害反应机构的情境意识，而且还可以增强国家水上水模型的洪水预测能力。提议的算法将被实施到开源工具中，以增强地球科学社区的研究基础设施。教育活动包括课程开发，在阿拉巴马州计算机科学训练营的数据科学研讨会上指导一群高中生，以及为地区科学公平竞争选定的高中学生提供长达一年的项目。该项目预计将导致以下计算机科学创新。首先，将研究一个新型的空间结构模型，称为“隐藏马尔可夫地形树”（HMTT），该模型将概括从总阶序列到部分级级聚树的现有隐藏模型。与现有的空间结构化模型（例如，马尔可夫随机场，空间自回归回归）相比，基于空间邻近性捕获依赖性，HMTT可以通过更复杂的结构约束来减少噪声和大障碍物在水文学中的更为复杂的结构约束（例如，流动方向）的影响。其次，将探索从大量位置构造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，并探索计算修剪以减少类推理现有动态编程方法中回溯的数量。将纪律知识（例如结构性约束）与数据驱动方法整合的想法可以通过增强模型的可解释性和一致性来改变数据科学研究。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来获得支持的。

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

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

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

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