Examining groundwater-flood and soil moisture-flood relationships across scales using national-scale data mining, deep learning and knowledge distillation

使用国家规模的数据挖掘、深度学习和知识蒸馏来检查跨尺度的地下水-洪水和土壤水分-洪水关系

• 批准号: 1832294
• 负责人: Chaopeng Shen
• 金额: $ 24.99万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1832294&HistoricalAwards=false
• 关键词: Examining; groundwater; flood; soil; moisture

In many parts of the United States, it has been shown that groundwater levels and soil moisture, which quantifies the wetness of the soil, are connected via the mechanism of flood production. Water cannot infiltrate into the ground when groundwater is close to the surface and is thus forced to quickly run off to rivers, creating higher flooding risks. However, the relationship between groundwater and floods has been found to be highly diverse and difficult to predict. Depending on terrain, groundwater depth, and many other factors, floods lead groundwater increase in some cases while groundwater can lead floods in others. Previous research from selected experimental watersheds have not resulted in a comprehensive and transferable understanding of the controlling processes. This project will take a big-data, machine learning approach to enhance our understanding of this relationship, allowing us to heuristically exploit previously under-utilized groundwater data for flood predictions and reducing damages. Using learning patterns from national-scale groundwater and streamflow data, the machine learning algorithms will create plausible groundwater-flood relationships. Taking advantage of the big hydrologic data from available satellite missions, this project will create shared undergraduate course modules to enhance student's ability to work with big data and increase their awareness of global water issues.This research advances hydrologic science by answering the following overarching question: at catchment scales, do groundwater levels in the catchment provide predictive power for flood threshold functions and baseflow? We will address this question in multiple small steps. We will identify the kinds of groundwater-rainfall-runoff (GW-P-Q) relations that can be found over the Continental United States. These relations are quantified by the correlations between water table depths and flood thresholds (and baseflow) at different lags and time scales. We will seek the factors dictate the type of GW-P-Q relations and whether these relations are stable across seasons and years. We will employ two approaches: a human-directed classification analysis, and a knowledge distillation scheme based on deep learning (DL), a rapidly advancing group of techniques supporting the recent surge in artificial intelligence. In the first approach, we will use classification and regression tree to identify factors that could explain the GW-P-Q relations. In the DL-based approach, we will train continental-scale time series DL models using all available data to forecast discharge. This approach addresses the issue with classification trees in which not enough data are available for branch nodes. Through a novel knowledge distillation procedure, we transfer the knowledge gained in the deep network to more interpretable formats, including explicit mathematical formula. Results from the study will provide a comprehensive understanding of GW-P-Q relations where regional patterns and physical controls emerge. Besides gaining new knowledge, a significant by-product is the trained DL models. They can be used as a flood forecasting tool to integrate recent soil moisture and groundwater observations, which have not been exploited until now. The educational activity will mesh with the research activity by engaging undergraduate students in handling, visualizing and interpreting big hydrologic data.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.

在美国的许多地方，已经表明地下水位和土壤湿度（量化土壤湿度）通过洪水产生机制联系在一起。当地下水接近地表时，水无法渗入地下，因此被迫迅速流入河流，造成更高的洪水风险。然而，地下水和洪水之间的关系被发现是高度多样化的，难以预测。根据地形、地下水深度和许多其他因素，洪水在某些情况下导致地下水增加，而地下水在其他情况下可能导致洪水。以前的研究，从选定的实验流域没有导致控制过程的全面和可转移的理解。该项目将采用大数据、机器学习的方法来增强我们对这种关系的理解，使我们能够探索性地利用以前未充分利用的地下水数据进行洪水预测并减少损失。使用来自全国范围的地下水和径流数据的学习模式，机器学习算法将创建合理的地下水-洪水关系。利用现有卫星任务的大水文数据，该项目将创建共享的本科课程模块，以提高学生使用大数据的能力，并提高他们对全球水问题的认识。这项研究通过回答以下首要问题推进水文科学：在流域尺度上，流域地下水位是否为洪水阈值函数和基流提供预测能力？我们将通过多个小步骤来解决这个问题。我们将确定的地下水降雨径流（GW-P-Q）的关系，可以在美国大陆。这些关系量化的地下水位深度和洪水阈值（和基流）在不同的滞后和时间尺度之间的相关性。我们将寻求决定GW-P-Q关系类型的因素，以及这些关系在季节和年份之间是否稳定。我们将采用两种方法：一个是人工指导的分类分析，另一个是基于深度学习（DL）的知识蒸馏方案，这是一组支持最近人工智能激增的快速发展的技术。在第一种方法中，我们将使用分类和回归树来识别可以解释GW-P-Q关系的因素。在基于DL的方法中，我们将使用所有可用数据训练大陆尺度时间序列DL模型来预测流量。这种方法解决了分类树中没有足够的数据可用于分支节点的问题。通过一种新的知识蒸馏过程，我们将深度网络中获得的知识转换为更可解释的格式，包括显式的数学公式。研究结果将提供一个全面的了解GW-P-Q关系的区域模式和物理控制出现。除了获得新知识外，一个重要的副产品是训练的DL模型。它们可以用作洪水预报工具，将最近的土壤湿度和地下水观测结果结合起来，这些观测结果迄今尚未得到利用。该教育活动将通过让本科生参与处理、可视化和解释大型水文数据来与研究活动相结合。该奖项反映了NSF的法定使命，并通过使用基金会的知识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

Near-Real-Time Forecast of Satellite-Based Soil Moisture Using Long Short-Term Memory with an Adaptive Data Integration Kernel

• DOI: 10.1175/jhm-d-19-0169.1
• 发表时间: 2020-03-01
• 期刊: JOURNAL OF HYDROMETEOROLOGY
• 影响因子: 3.8
• 作者: Fang, Kuai;Shen, Chaopeng
• 通讯作者: Shen, Chaopeng

HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community

• DOI: 10.5194/hess-22-5639-2018
• 发表时间: 2018-11-01
• 期刊: HYDROLOGY AND EARTH SYSTEM SCIENCES
• 影响因子: 6.3
• 作者: Shen, Chaopeng;Laloy, Eric;Tsai, Wen-Ping
• 通讯作者: Tsai, Wen-Ping

Differentiable, Learnable, Regionalized Process‐Based Models With Multiphysical Outputs can Approach State‐Of‐The‐Art Hydrologic Prediction Accuracy

• DOI: 10.1029/2022wr032404
• 发表时间: 2022-03
• 期刊: Water Resources Research
• 影响因子: 5.4
• 作者: D. Feng;Jiangtao Liu;K. Lawson;Chaopeng Shen

The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment

• DOI: 10.5194/hess-27-2357-2023
• 发表时间: 2023-06
• 期刊: Hydrology and Earth System Sciences
• 影响因子: 6.3
• 作者: D. Feng;H. Beck;K. Lawson;Chaopeng Shen

Evaluating the Potential and Challenges of an Uncertainty Quantification Method for Long Short‐Term Memory Models for Soil Moisture Predictions

• DOI: 10.1029/2020wr028095
• 发表时间: 2020-06
• 期刊: Water Resources Research
• 影响因子: 5.4
• 作者: K. Fang;Daniel Kifer;K. Lawson;Chaopeng Shen