教育背景
2019-10至2021-9, 日本大阪大学, 環境・エネルギー工学専攻, 博士
2016-9至2019-7, 华南师范大学, 地图学与地理信息系统, 硕士
2012-9至2016-7, 安徽农业大学, 地理信息科学, 学士
工作履历
2021-11至今, 华南农业大学, 资源环境学院地理信息系, 副教授
学术兼职
国际期刊Environmental Research letters, Catena, Journal of Hydrology, Science of the Total Environment, Geoscience Frontiers, Water Resources Management, Geocarto International等审稿人
日本地理情報システム学会，日本農業気象学会会员
研究领域
城市内涝易发性估算与致灾因素分析；农业面源污染负荷时空模拟
奖励和荣誉
2020年，2021年获大阪大学工学院博士生奖学金，院级
2020年获国家优秀自费留学生奖学金，国家级
主持项目
1. 国家自然科学基金-青年项目：粤港澳大湾区城市内涝易发性时空建模与精准评估研究，2024/01-2026/12，30万元，在研，主持。
2. 广东省自然科学基金-青年项目：基于社交媒体数据的城市内涝风险时空评估模式研究，2023/01-2025/12，10万元，在研，主持
3. 广州市基础研究计划基础与应用基础研究项目：广州城市地表热环境下的内涝热点区挖掘研究，2023/04-2025/03，5万元，在研，主持
4. 华南农业大学高层次人才引进青年才俊项目，2021/11-2026/10，40万元，在研，主持
学术成果
一、发表论文：
以唯一第一或唯一通讯作者身份，在地理信息与城市（农业）灾害相关SCI期刊共发表论文13篇。
第一作者（12篇）：
Tang, X., Huang, X., Tian, J., Pan, S., Ding, X., Zhou, Q., & Sun, C. (2024). A novel framework for the spatiotemporal assessment of urban flood vulnerability. Sustainable Cities and Society, 109, 105523. (SCI一区)
Tang, X., Huang, X., Tian, J., Jiang, Y., Ding, X., & Liu, W. (2024). A spatiotemporal framework for the joint risk assessments of urban flood and urban heat island. International Journal of Applied Earth Observation and Geoinformation, 127, 103686. (SCI一区)
Tang, X., Tian, J., Huang, X., Shu, Y., Liu, Z., Long, S., ... & Liu, W. (2024). A novel machine learning-based framework to extract the urban flood susceptible regions. International Journal of Applied Earth Observation and Geoinformation, 132, 104050. (SCI一区)
Tang, X., Wu, Z., Liu, W., Tian, J., & Liu, L. (2023). Exploring effective ways to increase reliable positive samples for machine learning-based urban waterlogging susceptibility assessments. Journal of Environmental Management, 344, 118682. (SCI二区)
Tang, X., Machimura, T., Li, J., Yu, H., & Liu, W. (2022). Evaluating Seasonal Wildfire Susceptibility and Wildfire Threats to Local Ecosystems in the Largest Forested Area of China. Earth's Future, e2021EF002199. (SCI一区)
Tang, X., Machimura, T., Liu, W., Li, J., & Hong, H. (2021). A novel index to evaluate discretization methods: A case study of flood susceptibility assessment based on random forest. Geoscience Frontiers, 12(6), 101253. (SCI一区)
Tang, X., Li, J., Liu, W., Yu, H., & Wang, F. (2021). A method to increase the number of positive samples for machine learning-based urban waterlogging susceptibility assessments. Stochastic Environmental Research and Risk Assessment, 1-18. (SCI三区)
Tang, X., Shu, Y., Liu, W., Li, J., Liu, M., & Yu, H. (2021). An optimized weighted Naïve Bayes method for flood risk assessment. Risk analysis, 41(12), 2301-2321. (SCI二区)
Tang, X., Machimura, T., Li, J., Liu, W., & Hong, H. (2020). A novel optimized repeatedly random undersampling for selecting negative samples: A case study in an SVM-based forest fire susceptibility assessment. Journal of Environmental Management, 271, 111014. (SCI二区)
Tang, X., Li, J., Liu, M., Liu, W., & Hong, H. (2020). Flood susceptibility assessment based on a novel random Naïve Bayes method: A comparison between different factor discretization methods. Catena, 190, 104536. (SCI一区)
Tang, X., Hong, H., Shu, Y., Tang, H., Li, J., & Liu, W. (2019). Urban waterlogging susceptibility assessment based on a PSO-SVM method using a novel repeatedly random sampling idea to select negative samples. Journal of Hydrology, 576, 583-595. (SCI一区)
Tang, X., Shu, Y., Lian, Y., Zhao, Y., & Fu, Y. (2018). A spatial assessment of urban waterlogging risk based on a Weighted Naïve Bayes classifier. Science of the total environment, 630, 264-274. (SCI一区)
通讯作者（1篇）：
Xian, W., Liu, H., Yang, X., Huang, X., Huang, H., Li, Y., ... & Tang, X.* (2024). An ensemble framework for farmland quality evaluation based on machine learning and physical models. Science of The Total Environment, 168914. (SCI一区)
