卢文喜论文发表

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基本信息

卢文喜Lu Wenxi

(教授)

学位：博士
性别：男
学历：博士研究生毕业
在职信息：在职
所在单位：新能源与环境学院

论文发表

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Wang Zibo; Lu Wenxi*; Chang Zhenbo; Zhang Tao. Joint identification of groundwater pollution source information, model parameters, and boundary conditions based on a novel ES-MDA with a wheel battle strategy [J]. Journal of Hydrology,2024, 636:131320. (SCI收录)
Xu Yaning; Lu Wenxi*; Pan Zidong; Wang Zibo; Luo Chengming; Bai Yukun. Intelligent enhanced particle filter with deep residual network surrogate for accurate groundwater pollution source characterization [J]. Journal of Hydrology,2024, 642:131904. (SCI收录)
Wang Zibo; Lu Wenxi*; Chang Zhenbo. Application of observed data denoising based on variational mode decomposition in groundwater pollution source recognition [J]. Science of the Total Environment,2024, 946:174374. (SCI收录)
Xu Yaning; Lu Wenxi*; Pan Zidong; Luo Chengming; Bai Yukun; Qiu Shuwei. Groundwater contaminant source identification considering unknown boundary condition based on an automated machine learning surrogate [J]. Geoscience Frontiers,2024, 15(1):101732. (SCI收录)
Wang Zibo; Lu Wenxi*; Chang Zhenbo. Joint inverse estimation of groundwater pollution source characteristics and model parameters based on an intelligent particle filter [J]. Journal of Hydrology,2023, 625: 129965. (SCI收录)
Pan Zidong; Lu Wenxi*; Bai Yukun. Groundwater contaminated source estimation based on adaptive correction iterative ensemble smoother with an auto lightgbm surrogate [J]. Journal of Hydrology, 2023, 620: 129502. (SCI收录)
Wang Zibo; Lu Wenxi*; Chang Zhenbo; Luo Jiannan. A combined search method based on a deep learning combined surrogate model for groundwater DNAPL contamination source identification [J]. Journal of Hydrology, 2023, 616: 128854. (SCI收录)
Pan Zidong; Lu Wenxi*; Wang Han; Bai Yukun. Groundwater contaminant source identification based on an ensemble learning search framework associated with an auto xgboost surrogate [J]. Environmental Modelling & Software, 2023, 159: 105588. (SCI收录)
Pan Zidong; Lu Wenxi*; Wang Han; Bai Yukun. Fast inverse estimation of hydraulic conductivity field based on a deep convolutional-cycle generative adversarial neural network [J]. Journal of Hydrology, 2022, 613: 128420. (SCI收录)
Chang Zhenbo; Lu Wenxi*; Wang Zibo. Study on source identification and source-sink relationship of LNAPLs pollution in groundwater by the adaptive cyclic improved iterative process and Monte Carlo stochastic simulation [J]. Journal of Hydrology, 2022, 612: 128109. (SCI收录)
Wang Zibo; Lu Wenxi*; Chang Zhenbo; Wang Han. Simultaneous identification of groundwater contaminant source and simulation model parameters based on an ensemble Kalman filter - Adaptive step length ant colony optimization algorithm [J]. Journal of Hydrology, 2022, 605: 127352. (SCI收录)
Wang Zibo; Lu Wenxi*; Chang Zhenbo*; Bai Yukun; Xu Yaning. Simultaneous identification of groundwater contamination source information, model parameters, and boundary conditions under an unknown boundary mode [J]. Stochastic Environmental Research and Risk Assessment,2024, 38(10):4085-4106. (SCI收录)
Zhu Liuzhi; Lu Wenxi*; Luo Chengming; Xu Yaning; Wang Zibo. An ensemble optimizer with a stacking ensemble surrogate model for identification of groundwater contamination source [J]. Journal of Contaminant Hydrology,2024, 267:104437. (SCI收录)
Pan Zidong; Lu Wenxi*; Bai Yukun. Groundwater contamination source estimation based on a refined particle filter associated with a deep residual neural network surrogate [J]. Hydrogeology Journal, 2022, 30(3): 881-897. (SCI收录)
Luo Chengming; Lu Wenxi*; Pan Zidong; Bai Yukun; Dong Guangqi; Simultaneous identification of groundwater pollution source and important hydrogeological parameters considering the noise uncertainty of observational data [J]. Environmental Science and Pollution Research, 2023, 30 (35): 84267-84282. (SCI收录)
Li Yidan; Lu Wenxi*; Pan Zidong; Wang Zibo; Dong Guangqi; Simultaneous identification of groundwater contaminant source and hydraulic parameters based on multilayer perceptron and flying foxes optimization [J]. Environmental Science and Pollution Research, 2023, 30 (32): 78933-78947. (SCI收录)

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