Jing Yang

Jing Yang

Associate Professor, Northwest A&F University

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Pages

Posts

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

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Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

Combining AHP and genetic algorithms approaches to modify DRASTIC model to assess groundwater vulnerability: a case study from Jianghan Plain, China

Published in Environmental Earth Sciences, 2017

Recommended citation: Jing Yang, Zhonghua Tang*, Tian Jiao, Akhtar Malik Muhammad. "Combining AHP and genetic algorithms approaches to modify DRASTIC model to assess groundwater vulnerability: a case study from Jianghan Plain, China." Environmental Earth Sciences. 2017, 76: 426. https://doi.org/10.1007/s12665-017-6759-6

Using cluster analysis for understanding spatial and temporal patterns and controlling factors of groundwater geochemistry in a regional aquifer

Published in Journal of Hydrology, 2020

Recommended citation: Jing Yang, Ming Ye*, Zhonghua Tang*, Tian Jiao, Xiaoyu Song, Yongzhen Pei, Honghua Liu. "Using cluster analysis for understanding spatial and temporal patterns and controlling factors of groundwater geochemistry in a regional aquifer." Journal of Hydrology. 2020, 583: 124594. https://doi.org/10.1016/j.jhydrol.2020.124594

Using t-distributed Stochastic Neighbor Embedding (t-SNE) for cluster analysis and spatial zone delineation of groundwater geochemistry data

Published in Journal of Hydrology, 2021

Recommended citation: Honghua Liu, Jing Yang*, Ming Ye*, Scott C. James, Zhonghua Tang, Jie Dong, Tongju Xing. "Using one-way and co-clustering methods to reveal spatio-temporal patterns and controlling factors of groundwater geochemistry." Journal of Hydrology. 2021, 603: 127085. https://doi.org/10.1016/j.jhydrol.2021.126146

Using self-organizing map and multivariate statistical methods for groundwater quality assessment in the urban area of Linyi city, China

Published in Water, 2023

Recommended citation: Shiqiang LIu, Haibo Li, Jing Yang*, Mingqiang Ma, Jiale Shang, Zhonghua Tang, Geng Liu. "Using self-organizing map and multivariate statistical methods for groundwater quality assessment in the urban area of Linyi city, China." Water. 2023, 15(19): 3463. https://www.mdpi.com/2073-4441/15/19/3463 https://www.mdpi.com/2073-4441/15/19/3463

Using clustering, geochemical modeling, and a decision tree for the hydrogeochemical characterization of groundwater in an in situ leaching uranium deposit in Bayan-Uul, northern China

Published in Water, 2023

Recommended citation: Haibo Li, Mengqi Liu, Tian Jiao, Dongjin Xiang*, Xiaofei Yan*, Zhonghua Tang, Jing Yang. "Using clustering, geochemical modeling, and a decision tree for the hydrogeochemical characterization of groundwater in an in situ leaching uranium deposit in Bayan-Uul, northern China." Water. 2023, 15(24): 4234. https://www.mdpi.com/2073-4441/15/24/4234 https://www.mdpi.com/2073-4441/15/24/4234

Comparative assessment of two global sensitivity approaches considering model and parameter uncertainty

Published in Water Resources Research, 2024

Recommended citation: Heng Dai, Yujiao Liu, Alberto Guadagnini*, Jing Yang, Ming Ye. "Comparative assessment of two global sensitivity approaches considering model and parameter uncertainty." Water Resources Research. 2024, 60(2): e2023WR036096. https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023WR036096 https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023WR036096

Global sensitivity analysis with deep learning-based surrogate models for unraveling key parameters and processes governing redox zonation in riparian zone

Published in Journal of Hydrology, 2024

Recommended citation: Zhejiong Yu, Heng Dai*, Jing Yang, Yonghui Zhu, Songhu Yuan. "Global sensitivity analysis with deep learning-based surrogate models for unraveling key parameters and processes governing redox zonation in riparian zone." Journal of Hydrology. 2024, 638: 131442. https://www.sciencedirect.com/science/article/pii/S0022169424008370 https://www.sciencedirect.com/science/article/pii/S0022169424008370

Development of an integrated global sensitivity analysis strategy for evaluating process sensitivities across single- and multi-models

Published in Journal of Hydrology, 2024

Recommended citation: Jing Yang, Yujiao Liu, Heng Dai*, Songhu Yuan, Tian Jiao, Zhang Wen, Ming Ye. "Development of an integrated global sensitivity analysis strategy for evaluating process sensitivities across single- and multi-models." Journal of Hydrology. 2024, 643: 132014. https://www.sciencedirect.com/science/article/abs/pii/S0022169424014100 https://www.sciencedirect.com/science/article/abs/pii/S0022169424014100

talks

teaching

Water Laws and Engineering Ethics

Undergraduate course, Northwest A&F Univeristy, College of Water Resources and Architectural Engineering, 2023

This course is an elective course for undergraduate students majoring in Agricultural Water Conservancy Engineering, Hydrology and Water Resources Engineering, Water Conservancy and Hydropower Engineering, and Energy and Power Engineering. The main content includes the Water Law, the Soil and Water Conservation Law, the Flood Control Law, the Water Pollution Prevention and Control Law, basic issues in engineering ethics, core values of engineering ethics, and basic norms of engineering ethics. Through this course, students will understand the basic knowledge of water conservancy laws and engineering ethics, improve their theoretical level of water conservancy laws and regulations and moral judgment ability, and increase their moral sensitivity and ethical standards in water conservancy engineering practice and academic activities.

Surveying

Undergraduate course, Northwest A&F University, College of Water Resources and Architectural Engineering, 2024

This course is compulsory for undergraduate students in nearly 20 non-surveying majors such as Forestry, Resources and Environmental Science, Geographic Information Science, Landscape Architecture, Grassland Science, and Soil and Water Conservation at Northwest A&F University. It is a provincial-level quality course in Shaanxi Province’s higher education system. The theoretical content is divided into ten chapters: basic knowledge of surveying, leveling, angle measurement and straight line orientation, total station surveying, global satellite navigation positioning systems, surveying error theory, control surveying, topographic mapping, topographic map applications, and basic work of construction staking. The laboratory sessions include familiarization and use of the DS3 level, fourth-order leveling with a digital level, familiarization with the total station and data collection, familiarization with RTK and data collection, large-scale digital mapping, total station construction staking, and RTK construction staking.

Hydrological Models and Intelligent Forecasting

Undergraduate course, Northwest A&F University, College of Water Resources and Architectural Engineering, 2024

The elective course “Hydrological Models and Intelligent Forecasting” is designed for students specializing in Intelligent Water Conservancy and Hydrology and Water Resources Engineering. This course serves to provide a comprehensive overview of key topics in hydrological forecasting, including the foundational theories, classifications, and characteristics of watershed hydrological models. Specifically, emphasis is placed on the Xin’anjiang and SWAT models, widely recognized within the field. Additionally, the curriculum covers real-time flood forecasting, dry-season runoff prediction, machine learning methodologies for time series forecasting, and intelligent forecasting techniques integrating big data analytics and deep learning methodologies. Upon completion of this course, students are expected to demonstrate a proficient understanding of both lumped and distributed watershed hydrological models, alongside a familiarity with their practical applications. Furthermore, students will have acquired a mastery of the fundamental principles, methodologies, and modeling techniques essential for hydrological forecasting. Armed with this knowledge, students will be equipped to utilize hydrological models and machine learning methods effectively for the purposes of forecasting. Ultimately, this course aims to establish a robust foundation for students, enabling them to engage confidently in the planning, construction, and management of water projects, as well as initiatives pertaining to flood control and drought management.