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Engineering and Technology

D-Index
46
Citations
7371
World Ranking
5210
National Ranking
1473

Overview

Ming Ye is a researcher affiliated with Florida State University in the United States. Their work spans multiple fields of study including Medicine, Engineering, and Environmental Science. Within these broader areas, Ming Ye has contributed substantially to subfields such as Neurology, Environmental Engineering, Civil and Structural Engineering, Public Health, Environmental and Occupational Health, and Global and Planetary Change.

The researcher's main topics of work encompass Soil and Unsaturated Flow, Groundwater flow and contamination studies, Mathematical and Theoretical Epidemiology and Ecology Models, Vascular Malformations Diagnosis and Treatment, Hydrology and Watershed Management Studies, Intracranial Aneurysms: Treatment and Complications, and Groundwater and Isotope Geochemistry.

Among Ming Ye's recent notable papers are:

  • Groundwater sustainability: a review of the interactions between science and policy, 2020, Environmental Research Letters
  • Using t-distributed Stochastic Neighbor Embedding (t-SNE) for cluster analysis and spatial zone delineation of groundwater geochemistry data, 2021, Journal of Hydrology
  • Using cluster analysis for understanding spatial and temporal patterns and controlling factors of groundwater geochemistry in a regional aquifer, 2020, Journal of Hydrology
  • GW-PINN: A deep learning algorithm for solving groundwater flow equations, 2022, Advances in Water Resources
  • Performance analysis and optimization of a novel cooling plate with non-uniform pin-fins for lithium battery thermal management, 2021, Applied Thermal Engineering

The primary publication venues where Ming Ye's work appears include the Journal of Hydrology, SSRN Electronic Journal, Water Resources Research, Agricultural Water Management, and Sensors. The most frequent venue is the Journal of Hydrology, with 24 publications.

Ming Ye often collaborates with other researchers, with frequent coauthors being Qimin Zhang, Yan Zhu, Jinzhong Yang, Jing Yang, and Heng Dai. These collaborations highlight a network of scientific relationships mainly within the fields of environmental and engineering sciences.

Best Publications

  • Developing a Long Short-Term Memory (LSTM) based model for predicting water table depth in agricultural areas.

    Jianfeng Zhang;Yan Zhu;Xiaoping Zhang;Ming Ye

  • Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications

    Xiaomeng Song;Jianyun Zhang;Chesheng Zhan;Yunqing Xuan

  • Towards a comprehensive assessment of model structural adequacy

    Hoshin V. Gupta;Martyn P. Clark;Jasper A. Vrugt;Jasper A. Vrugt;Gab Abramowitz

  • Spatiotemporal variations of hydrogeochemistry and its controlling factors in the Gandaki River Basin, Central Himalaya Nepal.

    Ramesh Raj Pant;Fan Zhang;Faizan Ur Rehman;Guanxing Wang

  • Maximum likelihood Bayesian averaging of spatial variability models in unsaturated fractured tuff

    Ming Ye;Shlomo P. Neuman;Philip D. Meyer

  • A model-averaging method for assessing groundwater conceptual model uncertainty.

    Ming Ye;Karl F. Pohlmann;Jenny B. Chapman;Greg M. Pohll

  • Bayesian analysis of data-worth considering model and parameter uncertainties

    Shlomo P. Neuman;Liang Xue;Ming Ye;Dan Lu

  • Estimating daily air temperatures over the Tibetan Plateau by dynamically integrating MODIS LST data

    Hongbo Zhang;Hongbo Zhang;Fan Zhang;Fan Zhang;Ming Ye;Tao Che;Tao Che

  • Sensitivity analysis and assessment of prior model probabilities in MLBMA with application to unsaturated fractured tuff

    Ming Ye;Shlomo P. Neuman;Philip D. Meyer;Karl Pohlmann

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

    Honghua Liu;Jing Yang;Jing Yang;Ming Ye;Scott C. James

  • Snow cover and runoff modelling in a high mountain catchment with scarce data: effects of temperature and precipitation parameters

    Fan Zhang;Hongbo Zhang;Scott C. Hagen;Ming Ye

  • An adaptive sparse-grid high-order stochastic collocation method for Bayesian inference in groundwater reactive transport modeling

    Guannan Zhang;Dan Lu;Ming Ye;Max Gunzburger

  • Fume transports in a high rise industrial welding hall with displacement ventilation system and individual ventilation units

    Han-Qing Wang;Chun-Hua Huang;Di Liu;Fu-Yun Zhao;Fu-Yun Zhao

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

    Jing Yang;Jing Yang;Ming Ye;Zhonghua Tang;Tian Jiao

  • Identification of sorption processes and parameters for radionuclide transport in fractured rock

    Zhenxue Dai;Andrew Wolfsberg;Paul Reimus;Hailin Deng

  • Nonlocal and localized analyses of conditional mean transient flow in bounded, randomly heterogeneous porous media

    Ming Ye;Ming Ye;Shlomo P. Neuman;Alberto Guadagnini;Daniel M. Tartakovsky

  • Assessment of parametric uncertainty for groundwater reactive transport modeling

    Xiaoqing Shi;Xiaoqing Shi;Ming Ye;Gary P. Curtis;Geoffery L. Miller

  • Estimation of effective unsaturated hydraulic conductivity tensor using spatial moments of observed moisture plume

    Tian Chyi J. Yeh;Ming Ye;Raziuddin Khaleel

  • Quantifying model structural error: Efficient Bayesian calibration of a regional groundwater flow model using surrogates and a data-driven error model

    Tianfang Xu;Tianfang Xu;Albert J. Valocchi;Ming Ye;Feng Liang

  • Numerical Comparison of Iterative Ensemble Kalman Filters for Unsaturated Flow Inverse Modeling

    Xuehang Song;Liangsheng Shi;Ming Ye;Jinzhong Yang

  • Practical Use of Computationally Frugal Model Analysis Methods

    Mary C. Hill;Dmitri Kavetski;Martyn Clark;Ming Ye

  • Groundwater Quality: Analysis of Its Temporal and Spatial Variability in a Karst Aquifer.

    Roger Pacheco Castro;Julia Pacheco Ávila;Ming Ye;Armando Cabrera Sansores

  • Expert elicitation of recharge model probabilities for the Death Valley regional flow system

    Ming Ye;Karl F. Pohlmann;Jenny B. Chapman

Frequent Co-Authors

Shlomo P. Neuman
Shlomo P. Neuman University of Arizona
Yu-Shu Wu
Yu-Shu Wu Colorado School of Mines
Martyn P. Clark
Martyn P. Clark University of Saskatchewan
Dmitri Kavetski
Dmitri Kavetski University of Adelaide
Mazdak Arabi
Mazdak Arabi Colorado State University
Zhenxue Dai
Zhenxue Dai Los Alamos National Laboratory
Anthony P. Walker
Anthony P. Walker Oak Ridge National Laboratory
Jichun Wu
Jichun Wu Nanjing University
Guo-Yue Niu
Guo-Yue Niu University of Arizona
Greg A. Barron-Gafford
Greg A. Barron-Gafford University of Arizona

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