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教育经历：
(1) 2018-09 至 2022-06，同济大学，环境科学与工程，博士
(2) 2015-09 至 2018-06，武汉大学，市政工程，硕士
(3) 2011-09 至 2015-06，武汉大学，给排水科学与工程，学士
研究工作经历：
(1) 2022-09 至 2025-06，西安建筑科技大学，师资博士后
(2) 2025-07 至今，西安建筑科技大学，环境与市政工程学院，准聘副教授
主要从事城市雨洪管理、排水系统优化控制、雨水面源污染控制等领域的研究工作。主持国家自然科学基金青年科学基金项目1项，陕西省“校招共用”引才用才专项1项，企业技术咨询项目3项，企业合作课题1项。作为项目技术负责人完成企业技术服务项目10余项。参与国家“十二五”重点研发计划项目、国家“十三五”水专项子课题等多项国家级与省部级科研项目。相关研究成果在Water Researc、Journal of Environmental Management等期刊发表学术论文20余篇，申请发明专利3项，授权软件著作权1项。作为主要作者起草的调查报告《解决我省城市内涝问题势在必行》获陕西省委领导批示及相关部门采纳。
[1] 王渲, 刘志鹏, 卢金锁. 城市雨水管网集蓄雨水径流资源潜能分析[J]. 给水排水, 2023, 49(3): 134-140.
[2] WANG X, WANG J, YAN J, et al. Sources and Transformations of Dissolved Organic Matter in Urban Impervious Surface Sediments: Insights from Optical Signatures and Environmental Drivers. Water Research, 2025.
[3] WANG X, YAN J, LIU Y, et al. A Hybrid-fidelity Modeling Approach for Evaluating Flooding Risk in Large Urban Infrastructures. Water Resources Management, 2025, 39(7): 3053-3069.
[4] WANG X, GAO Q, WANG W, et al. Determining priority control factors for heavy metal management in urban road dust based on source-oriented probabilistic ecological-health risk assessment: A study in Xi’an during peak pollution season. Journal of Environmental Management, 2024, 369: 122105.
[5] LIAO Z, WANG X *, TIAN W, et al. Enhancing surface water quality prediction in data-scarce sites using transfer learning and neural networks. Journal of Water Process Engineering, 2025, 75: 107923.
[6] WANG X, DONG Y, YANG J, et al. A benchmark-based method for evaluating hyperparameter optimization techniques of neural networks for surface water quality prediction. Frontiers of Environmental Science & Engineering, 2024, 18(5): 54.
[7] Wang, X., Tian, W. & Liao, Z. Statistical comparison between SARIMA and ANN’s performance for surface water quality time series prediction. Environ Sci Pollut Res 28, 33531–33544 (2021).
[8] Wang, X., Tian, W. & Liao, Z. Offline Optimization of Sluice Control Rules in the Urban Water System for Flooding Mitigation. Water Resour Manage 35, 949–962 (2021).
[9] Tian, W., Liao, Z. & Wang, X. Transfer learning for neural network model in chlorophyll-a dynamics prediction. Environ Sci Pollut Res 26, 29857–29871 (2019).
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西安建筑科技大学