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Jia-Qiang Lv

Showing results (1-10 of 9) with videos related to

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The Science of the Total Environment|July 7, 2024
Machine learning for high-precision simulation of dissolved organic matter in sewer: Overcoming data restrictions with generative adversarial networksFeng Hou, Shuai Liu, Wan-Xin Yin, et al.
Environmental Science and Ecotechnology|December 11, 2024
Augmented machine learning for sewage quality assessment with limited dataJia-Qiang Lv, Wan-Xin Yin, Jia-Min Xu, et al.
Journal of Hazardous Materials|April 9, 2026
From pollutant profiling to source attribution: An interpretable staged machine learning framework for sewer surveillanceJia-Qiang Lv, Yanchen Liu, Bo Li, et al.
Bioresource Technology|October 16, 2024
Microbial-Guided prediction of methane and sulfide production in Sewers: Integrating mechanistic models with Machine learningWan-Xin Yin, Jia-Qiang Lv, Shuai Liu, et al.
Water Research|January 31, 2026
In-Situ sulfur implantation efficiently promoting nitrogen removal in low-carbon anoxic-oxic systemsJia-Min Xu, Miao Gu, Yi-Fan Zhang, et al.
Environmental Science & Technology|May 16, 2025
Federated Machine Learning Enables Risk Management and Privacy Protection in Water QualityYu-Qi Wang, Hong-Cheng Wang, Wen-Zhe Wang, et al.
Environmental Science & Technology|February 12, 2025
Deciphering and Mitigating of Dynamic Greenhouse Gas Emission in Urban Drainage Systems with Knowledge-Infused Graph Neural NetworkWan-Xin Yin, Ke-Hua Chen, Jia-Qiang Lv, et al.
Water Research|June 24, 2026
Machine learning-driven prediction and control system for practical application of sulfur-based autotrophic denitrification biofiltersJia-Qiang Lv, Jia-Min Xu, Wen-Ke He, et al.
Water Research|June 27, 2026
Machine learning-integrated multi-objective application and optimization framework for sulfur-based reactive filler towards nutrient removalJia-Min Xu, Jia-Qiang Lv, Wen-Ke He, et al.
Pageof 1

Showing results (1-10 of 9) with videos related to

Sort By:
Pageof 1
The Science of the Total Environment|July 7, 2024
Machine learning for high-precision simulation of dissolved organic matter in sewer: Overcoming data restrictions with generative adversarial networksFeng Hou, Shuai Liu, Wan-Xin Yin, et al.
Environmental Science and Ecotechnology|December 11, 2024
Augmented machine learning for sewage quality assessment with limited dataJia-Qiang Lv, Wan-Xin Yin, Jia-Min Xu, et al.
Journal of Hazardous Materials|April 9, 2026
From pollutant profiling to source attribution: An interpretable staged machine learning framework for sewer surveillanceJia-Qiang Lv, Yanchen Liu, Bo Li, et al.
Bioresource Technology|October 16, 2024
Microbial-Guided prediction of methane and sulfide production in Sewers: Integrating mechanistic models with Machine learningWan-Xin Yin, Jia-Qiang Lv, Shuai Liu, et al.
Water Research|January 31, 2026
In-Situ sulfur implantation efficiently promoting nitrogen removal in low-carbon anoxic-oxic systemsJia-Min Xu, Miao Gu, Yi-Fan Zhang, et al.
Environmental Science & Technology|May 16, 2025
Federated Machine Learning Enables Risk Management and Privacy Protection in Water QualityYu-Qi Wang, Hong-Cheng Wang, Wen-Zhe Wang, et al.
Environmental Science & Technology|February 12, 2025
Deciphering and Mitigating of Dynamic Greenhouse Gas Emission in Urban Drainage Systems with Knowledge-Infused Graph Neural NetworkWan-Xin Yin, Ke-Hua Chen, Jia-Qiang Lv, et al.
Water Research|June 24, 2026
Machine learning-driven prediction and control system for practical application of sulfur-based autotrophic denitrification biofiltersJia-Qiang Lv, Jia-Min Xu, Wen-Ke He, et al.
Water Research|June 27, 2026
Machine learning-integrated multi-objective application and optimization framework for sulfur-based reactive filler towards nutrient removalJia-Min Xu, Jia-Qiang Lv, Wen-Ke He, et al.
Pageof 1