![](/app/static/1.0/img/scholars/default_bg.jpg)
![](/app/static/1.0/img/scholar/male.jpg)
郑一
博士 教授 博士生导师
南方科技大学 环境科学与工程学院
从事水资源与水环境、生态水文、水-农业-环境系统关联、环境大数据与人工智能等方面研究,在水-农业-环境集成模拟、水循环可解释人工智能等前沿交叉领域获得开创性成果。
个性化签名
- 姓名:郑一
- 目前身份:在职研究人员
- 担任导师情况:博士生导师
- 学位:博士
-
学术头衔:
- 职称:高级-教授
-
学科领域:
环境科学技术基础学科
- 研究兴趣:从事水资源与水环境、生态水文、水-农业-环境系统关联、环境大数据与人工智能等方面研究,在水-农业-环境集成模拟、水循环可解释人工智能等前沿交叉领域获得开创性成果。
郑一博士于美国加州大学圣巴巴拉分校(University of California, Santa Barbara)获博士学位,现任南方科技大学环境科学与工程学院教授、党委书记;曾任北京大学能源与资源工程系特聘研究员、副教授、副系主任。
兼任国际水资源领域顶尖期刊 Water Resources Research 副主编、SCI期刊 Journal of Hydrologic Engineering-ASCE 副主编、全国中文核心期刊《安全与环境工程》编委、中国自然资源学会理事及青年工作委员会副主任、中国地理学会水文地理专业委员会委员、中国水利学会青年科技工作委员会委员、深圳市水务学会第七届理事会副理事长、深圳市环境物联网技术与应用工程实验室主任等职。
从事水资源与水环境、生态水文、水-农业-环境系统关联、环境大数据与人工智能等方面研究,在水-农业-环境集成模拟、水循环可解释人工智能等前沿交叉领域获得开创性成果。已在Nature Food, Nature Geoscience, Nature Sustainability, Nature Reviews Earth & Environment,Environmental Science & Technology, Water Resources Research, Geophysical Research Letters 等期刊发表 SCI 论文 110 多篇,出版“十三五”国家重点出版物出版规划著作 1 部,获得软件著作权 12 项,申请国家发明专利 6 项(已授权 4 项)。获中国自然资源学会优秀科技奖、广东省环境保护科学技术奖一等奖(排名第一)等奖励。
本科、研究生教学成果突出,在北京大学和南方科技大学分别获得年度最高个人教学奖励——北京大学“优秀教学奖”和南方科技大学“杰出教学奖”。
-
主页访问
0
-
关注数
0
-
成果阅读
0
-
成果数
3
WATER RESOURCES RESEARCH,2023,59(4):
2023年05月06日
Uncertainty reduction in watershed water quality (WWQ) modeling remains a major challenge. One important reason is the lack of sufficient available water quality observations because traditional laboratory analysis of water samples has high labor, financial and time costs. Low-cost high-frequency water quality data from in-situ sensors provide an opportunity to solve this problem. However, long-term sensing in complex natural environments usually suffers more significant errors. This study aimed to develop a novel method to utilize in-situ sensor data in WWQ modeling, namely, the Bayesian calibration using multisource observations (BCMSO), which can simultaneously assimilate laboratory-based observations and in-situ sensor data. Both synthetic and real-world cases of nitrate modeling were used to demonstrate the methodology, and the Soil and Water Assessment Tool was employed as the WWQ model. The results indicated that direct assimilation of sensor data using traditional Bayesian calibration generated obvious deviations in parameter inference and model simulation, which could consequently bias future predictions and affect management decision correctness. However, after proper treatment of errors in sensor data, the BCMSO method could extract meaningful information from sensor data and prevent negative impacts of errors. The modeling uncertainty was also greatly reduced. In the real-world case, with 1 yr of subhourly electrical conductivity sensor data incorporated, the modeling uncertainty of nitrate concentration and management cost of controlling nitrate pollution were reduced by 70%. The BCMSO method provides a flexible framework to accommodate nonconventional observations in environmental modeling and can be easily extended to other modeling fields.
sensor data, water quality, Bayesian calibration, uncertainty, SWAT, nitrate
0
-
0浏览
-
0点赞
-
0收藏
-
0分享
-
0下载
-
0评论
-
引用
GEOPHYSICAL RESEARCH LETTERS,2023,50(11):
2023年06月14日
This study develops a convolutional recurrent deep learning model to accurately predict fine-resolution spatiotemporal changes in grass coverage in arid regions. Applying the model to the Gobi Desert reveals that ecological flow regulation contributes to 61.8% of the total increase in grass cover (130.6 km(2)) in the study area (40,423 km(2)) over 2005-2015, nearly triple the contribution of local climate change (+23.0%). The transboundary hydrological impact (+32.4%) and interactions between drivers (-17.2%) are also significant. In an intermediate future climate change scenario, we found no statistically significant trend for the total grass-covered area due to the counteracting effects among different drivers. The study findings suggest that timely, adaptive and spatially heterogeneous ecological flow management is crucial for addressing grassland degradation in arid regions. This study provides a promising approach to land surface modeling under climate change and human disturbance and expands the existing understanding of the global greening process.
deep learning, grassland, ecological flow, drylands, ecohydrology, artificial intelligence
0
-
0浏览
-
0点赞
-
0收藏
-
0分享
-
0下载
-
0评论
-
引用
【期刊论文】Differentiable modelling to unify machine learning and physical models for geosciences
NATURE REVIEWS EARTH & ENVIRONMENT,2023,4(8):552-567
2023年07月23日
Differentiable modelling is an approach that flexibly integrates the learning capability of machine learning with the interpretability of process-based models. This Perspective highlights the potential of differentiable modelling to improve the representation of processes, parameter estimation, and predictive accuracy in the geosciences. Process-based modelling offers interpretability and physical consistency in many domains of geosciences but struggles to leverage large datasets efficiently. Machine-learning methods, especially deep networks, have strong predictive skills yet are unable to answer specific scientific questions. In this Perspective, we explore differentiable modelling as a pathway to dissolve the perceived barrier between process-based modelling and machine learning in the geosciences and demonstrate its potential with examples from hydrological modelling. 'Differentiable' refers to accurately and efficiently calculating gradients with respect to model variables or parameters, enabling the discovery of high-dimensional unknown relationships. Differentiable modelling involves connecting (flexible amounts of) prior physical knowledge to neural networks, pushing the boundary of physics-informed machine learning. It offers better interpretability, generalizability, and extrapolation capabilities than purely data-driven machine learning, achieving a similar level of accuracy while requiring less training data. Additionally, the performance and efficiency of differentiable models scale well with increasing data volumes. Under data-scarce scenarios, differentiable models have outperformed machine-learning models in producing short-term dynamics and decadal-scale trends owing to the imposed physical constraints. Differentiable modelling approaches are primed to enable geoscientists to ask questions, test hypotheses, and discover unrecognized physical relationships. Future work should address computational challenges, reduce uncertainty, and verify the physical significance of outputs.
ARTIFICIAL NEURAL-NETWORKS, WATER-QUALITY, GENETIC ALGORITHM, SURFACE-WATER, OPTIMIZATION, STREAMFLOW, ADJOINT, REPRESENTATION, UNCERTAINTY, PREDICTIONS
0
-
0浏览
-
0点赞
-
0收藏
-
0分享
-
0下载
-
0评论
-
引用