主要研究领域：
水力学、水资源管理、水沙动力学
代表性科研项目：
[1]国家自然科学青年基金，电解质及水流剪切对高浓度粘性沙悬浮液流变参数的影响， 2016-2018，主持
[2]水利部河口动力学及伴生过程调控重点实验室基金，信息熵理论在水沙科学的几个应用，2019-2020，主持
[3]教育部留学回国人员科研启动基金，粘性细颗粒泥沙悬浮体系特性研究，2015-2018，主持
[4]北京师范大学理科自主科研基金, 粘性细颗粒泥沙悬浮体系形成絮网结构的临界条件，2013-2015，主持
[5]科技部“十三五”国家重点研发计划课题，全国水资源承载力大数据平台构建，2016-2019，参与
[6]国家高端智库理事会-推动“一带一路”建设研究课题，“一带一路”国家水能资源合作研究，2018.1-2018.12，参与
[7]北京师范大学自主科研基金(学科交叉重点项目), 高浓度粘性沙悬浮液流变特性：机理分析与数学模拟，2015-2018，技术负责
[8]水利部综合事业局水资源管理中心中央分成水资源费，非常规水资源利用分析报告(2015、2016、2017年度) ，2016-2018，技术负责
[9]国家自然科学基金面上项目，变化环境下拉萨河流域水文过程演变机理研究，2018-2021，参与
[10]水利部水资源费项目，农业计划用水方案编制与管理制度研究，2015.1-2015.12，参与
代表性论文：
[1]Zhu, Z., Yu, J., Dou, J., Peng, D. 2019. An expression for velocity lag in sediment-laden open-channel flows based on Tsallis entropy together with the principle of maximum entropy. Entropy, 21, 522, 1-19. doi:10.3390/e2 1050522
[2]Zhu, Z. 2019. A formula for the settling velocity of cohesive sediment flocs in water. Water Science and Technology: Water Supply, 19(5), 1442-1428, doi: 10.2166/ws. 2019.007
[3]Yang, T.T., Hei, P.F., Song, J.D., Zhang, J., Zhu, Z. Zhang, Y.Y., Yang, J., Liu, C.L., Jin, J., Quan, J. 2019. Nitrogen variations during the ice-on season in the eutrophic lakes. Environmental Pollution, 247, 1089-1099. doi:10.1016/j.envpol.2018.12.088.
[4]Zhu, Z. 2019. A simple sensitivity analysis of the turbulence-induced flocculation model of cohesive sediment. Water Science and Technology, 79(6), 1144-1151. doi: 10.2166/wst.2019.112
[5]Zhu, Z., Wang, H., Peng, D., Dou, J. 2019. Modelling the hindered settling velocity of a falling particle in a particle-fluid mixture by the Tsallis entropy theory. Entropy, 21, 1-15. doi:10.3390/e21010055
[6]Dou, J., Yunus, A.P., Tien Bui, D., Merghadi, A., Sahana, M., Zhu, Z., Chen, C.W., Khosravi, K., Yang, Y., Pham, B.T., 2019. Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan. Science of the Total Environment, 662, 332-346. doi: 10.1016/j.scitotenv.2019.01.221.
[7]Zhu, Z., Yu, J. 2019. Estimating the bed-load layer thickness in open channels by Tsallis entropy. Entropy, 21(123), 1-15. doi:10.3390/e21020123
[8]Gong, S., Wang, H., Zhu, Z., Bai, Q., Wang, C. 2019. Comprehensive utilization of seawater in China: A description of the present situation, restrictive factors and potential countermeasures. Water, 11, 397. doi:10.3390/ w11020397
[9]Dou, J., Yunus, A.P., Xu, Y.R., Zhu, Z., Chen, C.W., Sahana, M., Yang, Y., Pham, B.T., Khosravi, K. 2019. Torrential rainfall-triggered shallow landslide characteristics and susceptibility assessment using ensemble data-driven models in the Dongjiang reservoir watershed, China. Natural Hazards, 97(2), 579-609. doi: 10.1007/ s110 69-019-03659-4
[10]Zhu, Z., Wang, H., Pang, B, Dou, J, Peng, D. 2019. Comparison of conventional deterministic and the entropy-based methods for predicting sediment concentration in debris flow. Water, 11, 1-16. doi:10.3390/ w11030439
[11]Dou, J., Yunus, A.P., Tien Bui, D., Sahana, M., Chen, C.-W., Zhu, Z., Wang, W., Pham, B.T. 2019. Evaluating GIS-based multiple statistical models and data mining for earthquake and rainfall-induced landslide susceptibility using the LiDAR DEM. Remote Sensing, 11, 638, 1-30. doi: 10.3390/rs11060638
[12]Zhu, Z., Wang, H., Li, A. 2018. On the factors influencing public knowledge and acceptance of reclaimed water from a survey of three cities in Northern China. Journal of Water Reuse and Desalination, 9(2), 193-202. doi: 10.2166/wrd.2018.049
[13]Zhu, Z., Peng, D., Wang, H. 2018. Seawater desalination in China: An overview. Journal of Water Reuse and Desalination, 9(2), 115-132. doi: 10.2166/wrd.2018.034
[14]Zhu, Z., Li, F. 2018. A simple laboratory experiment of the hindered settling process of high-concentration sediment suspension. Water Science and Technology: Water Supply, 19(4), 1144-1151. doi: 10.2166/ws.2018.170
[15]Zhu, Z., Xiong, X., Liang, C., Zhao, M. 2018. On the flocculation and settling characteristics of low- and high-concentration sediment suspensions: Effects of particle concentration and salinity conditions. Environmental Science and Pollution Research, 25(14), 14226-14243. doi: 10.1007/s11356-018-1668-0
[16]Dou, J., Yamagishi, H., Zhu, Z., Yunus, A.P., Chen, C.W. 2018. A comparative study of the binary logistic regression and artificial neural network models for GIS-based spatial predicting landslides at a regional scale, Sassa, K. et al. (Eds) Landslide dynamics: ISDR-ICL Landslide Interactive Teaching Tools. 139-152. Tokyo, Springer, ISBN 978-3-319-57773-9. doi: 10.1007/978-3-319-57774-6-10
[17]Zhu, Z. 2018. A simple explicit expression for the flocculation dynamics modelling of cohesive sediment based on entropy considerations. Entropy, 20, 1-20. doi:10.3390/e20110845
[18]Zhu, Z., Wang, H., Peng, D. 2017. Dependence of sediment suspension viscosity on solid concentration: A simple general equation. Water, 9, 474. doi:10.3390/w9070474
[19]Zhu, Z., Li, A., Wang, H. 2017. Public perception and acceptability of reclaimed water: The case of Shandong province, China. Journal of Water Reuse and Desalination, 8(3), 308-330. doi: 10.2166/wrd.2017.022
[20]Dou, J., Yamagishi, H., Xu, Y., Zhu, Z., Yunus, A.P. 2017. Characteristics of the torrential rainfall-induced shallow landslide by typhoon bilis, in July 2006, using remote sensing and GIS, in Yamagishi, H., Bhandary, N.P. (Eds), GIS-Landslide, 221-230, Tokyo, Springer, ISBN 978-4-431-54390-9. doi: 10.1007/978-4-431-54391-6
[21]Zhu, Z., Dou, J. 2017. Current status of reclaimed water in China: An overview. Journal of Water Reuse and Desalination, 8(3), 293-307. doi: 10.2166/wrd.2018.070
[22]Dou, J., Li, X, Yunus, A.P., Paudel, U., Chang, K.T., Zhu, Z., Pourghasemi, H. R 2015. Automatic detection of sinkhole collapses at finer resolutions using a multi-component remote sensing approach. Natural Hazards, 78(2), 1021-1044. doi: 10.1007/s11069-015-1756-0
[23]Dou, J., Yamagishi, H., Pourghasemi, H. R., Yunus, A.P., Song, X., Xu, Y., Zhu, Z. 2015. An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan. Natural Hazards, 78(3), 1749-1776. doi: 10.1007/s11069-015-1799-2
[24]Zhu, Z., Yu, J., Wang, H., Dou, J, Wang, C. 2015. Fractal dimension of cohesive sediment flocs at steady state under seven shear flow conditions. Water, 7, 4385-4408. doi: 10.33 90 /w7084385
[25]Dou, J., Chang, K.T., Chen, S., Yunus, A.P., Liu, J.K., Xia, H., Zhu, Z. 2015. Automatic case-based reasoning approach for landslide detection: integration of object-oriented image analysis and a genetic algorithm. Remote Sensing, 7(4), 4318-4342. doi: 10.3390/rs70404318
[26]Dou, J., Bui, D.T., Yunus, A.P., Jia, K., Song, X., Revhaug, I., Xia, H., Zhu, Z. 2015. Optimization of causative factors for landslide susceptibility evaluation using remote sensing and GIS data in parts of Niigata, Japan. PLOS One, 10(7):e0133262. doi: 10.1371/journal.pone.0133262
[27]Zhu, Z. 2014. Theory on orthokinetic flocculation of cohesive sediment: A review. Journal of Geoscience and Environment Protection, 2(5), 13-23. doi: 10.4236/gep.2014.25003
[28]Zhu, Z., Yu, J. 2014. Estimating the occurrence of wind-driven coastal upwelling associated with "Aoshio" on the northeast shore of Tokyo Bay, Japan: An analytical model. The Scientific World Journal, 2014, Article ID 769823, 1-11. doi:10.1155/2014/769823
[29]Zhu, Z., Isobe, M. 2012. Criteria for the occurrence of wind-driven coastal upwelling associated with "Aoshio" on the southeast shore of Tokyo Bay. Journal of Oceanography, 68(4),561-574.doi:1007/s10872-012-0119-7
[30]于忱，陈隽，王红瑞，朱中凡，来文立. 2018．多变量Copula函数在干旱风险分析中的应用进展，南水北调与水利科技， 16(1)，14-21
[31]洪思扬，王红瑞，朱中凡，韩鲁杰. 2018．基于栖息地指标法的生态流量研究，长江流域资源与环境，27(1)，168-175
[32]白琪阶，焦志倩，王红瑞，许新宜，朱中凡．2018．自然资源资产负债表编制实证研究，南水北调与水利科技，16(2)，7-13
[33]洪思扬，王红瑞，来文立，朱中凡. 2017．我国能源耗水空间特征及其协调发展脱钩分析，自然资源学报，32(5)，715-728
[34]洪思扬，王红瑞，朱中凡，丁建新. 2016．辽宁省水资源生态足迹与生态承载力分析，水利经济， 34(3)，46-52