一、个人概况
王兆才，男，山东潍坊人，副教授，硕士生导师，2006年硕士毕业于上海交通大学数学系，获应用数学硕士学位。后进入威尼斯wns8885566任教，并于2012年博士毕业于复旦大学经济学院，获计量经济学博士学位。2016年度北京大学信息科学技术学院高级访问学者。现主要研究方向为流域水文预报、水库群联合调度、水质水量生态调控等。近年来来主持上海市青年教师培养计划1项，中国水利水电科学院开放基金3项（优秀结题），参与国家自然科学基金4项。在多尺度水文水资源预报预测预警、梯级水库群联合优化调度模型、水资源多目标优化配置等方面取得了一系列突出研究成果。先后在《Journal of Hydrology》、《Journal of Hydrology-Region study》、《Resources Policy》、《Environmental Modelling & Software》、《Frontiers of Environmental Science & Engineering》、《Ecological Indicators》、《Water Resources Management》等权威期刊以第一或通讯发表SCI论文50余篇，ESI高被引论文2篇，指导学生获得全国数学建模二等奖，上海市数学建模一等奖等多项，获得上海市优秀数学建模指导老师，微课比赛华东赛区二等奖等荣誉称号。指导本科生发表多篇SCI论文，指导硕士研究生毕业2名，均获得国家一等奖学金和上海市优秀毕业生等荣誉称号。
近三年第一或通讯发表SCI论文：
[1] Tan, R., Wang, Z.*, Wu, T., & Wu, J. (2023). A data-driven model for water quality prediction in Tai Lake, China, using secondary modal decomposition with multidimensional external features, Journal of Hydrology-Region study, 47, 101435. https://doi.org/10.1016/j.ejrh.2023.101435
[2] Wang, Z., Wang, Q., & Wu, T. (2023). A novel hybrid model for water quality prediction based on VMD and IGOA optimized for LSTM, Frontiers of Environmental Science & Engineering, 17(7), 88. https://doi.org/10.1007/s11783-023-1688-y
[3] Wu, J., Dong, J., Wang, Z.*, Hu, Y., & Dou, W. (2023). A novel hybrid model based on deep learning and error correction for crude oil futures prices forecast. Resources Policy, 83, 103602. https://doi.org/10.1016/j.resourpol.2023.103602
[4] Cui, X., Wang, Z.*, & Pei, R. (2023). A VMD-MSMA-LSTM-ARIMA model for precipitation prediction. Hydrological Sciences Journal, 68(6), 810-839. https://doi.org/10.1080/02626667.2023.2190896
[5] Wu, J., Wang, Z.*, Hu, Y., Tao, S. & Dong, J. (2023). Runoff Forecasting using Convolutional Neural Networks and optimized Bi-directional Long Short-term Memory, Water Resources Management, 37 (2), 937-953. https://doi.org/10.1007/s11269-022-03414-8
[6] Chen, L., Wu, T., Wang, Z.*, Lin, X., & Cai, Y. (2023). A novel hybrid BPNN model based on adaptive evolutionary Artificial Bee Colony Algorithm for water quality index prediction. Ecological Indicators, 146, 109882. https://doi.org/10.1016/j.ecolind.2023.109882
[7] Tan, R., Hu, Y., Wang, Z.* (2023), A multi-source data-driven model of lake water level based on variational modal decomposition and external factors with optimized bi-directional long short-term memory neural network, Environmental Modelling & Software.
[8] Wu, J., & Wang, Z.* (2022). A hybrid model for water quality prediction based on an artificial neural network, wavelet transform, and long short-term memory. Water, 14(4), 610. https://doi.org/10.3390/w14040610
[9] Guo, N., & Wang, Z.* (2022). A combined model based on sparrow search optimized BP neural network and Markov chain for precipitation prediction in Zhengzhou City, China. AQUA—Water Infrastructure, Ecosystems and Society, 71(6), 782-800. https://doi.org/10.2166/aqua.2022.047
[10] Wu, X., Wang, Z.*, Wu, T., & Bao, X. (2022). Solving the Family Traveling Salesperson Problem in the Adleman–Lipton Model Based on DNA Computing. IEEE Transactions on NanoBioscience, 21(1), 75-85. https://doi.org/10.1109/TNB.2021.3109067
[11] Wang, Z., Deng, A., Wang, D., & Wu, T.* (2022). A parallel algorithm to solve the multiple travelling salesmen problem based on molecular computing model, International Journal of Bio-Inspired Computation, 20(3), 160-171. https://doi.org/10.1504/ijbic.2022.127504
[12] Wang, Z., Wu, X., & Wu, T.* (2022). A Parallel DNA Algorithm for Solving the Quota Traveling Salesman Problem Based on Biocomputing Model, Computational Intelligence and Neuroscience, 2022, 1450756. https://doi.org/10.1155/2022/1450756
[13] Wu, X., & Wang, Z.* (2022). Multi-objective optimal allocation of regional water resources based on slime mould algorithm. The Journal of Supercomputing, 78 (16), 18288-18317. https://doi.org/10.1007/s11227-022-04599-w
[14] Wang, Z., Wu, X., Wang, H., & Wu, T. (2021). Prediction and analysis of domestic water consumption based on optimized grey and Markov model. Water Supply, 21(7), 3887-3899. https://doi.org/10.2166/ws.2021.146
[15] Wang, Z., Wang, D., Bao, X., & Wu, T.* (2021). A parallel biological computing algorithm to solve the vertex coloring problem with polynomial time complexity. Journal of Intelligent & Fuzzy Systems, 40(3), 3957-3967. https://doi.org/10.3233/JIFS-200025
[16] Ren, X., Wang, X., Wang, Z.*, & Wu, T. (2021). Parallel DNA algorithms of generalized traveling salesman problem based bioinspired computing model. International Journal of Computational Intelligence Systems, 14(1), 228-237. https://doi.org/10.2991/ijcis.d.201127.001
[17] Wu, J., Wang, Z.*, & Dong, L. (2021). Prediction and analysis of water resources demand in Taiyuan City based on principal component analysis and BP neural network. AQUA—Water Infrastructure, Ecosystems and Society, 70(8), 1272-1286. https://doi.org/10.2166/aqua.2021.205
[18] Wang, Z., Bao, X., & Wu, T.* (2021). A parallel bioinspired algorithm for Chinese postman problem based on molecular computing. Computational Intelligence and Neuroscience, 2021, 8814947. https://doi.org/10.1155/2021/8814947
[19] Wang, Z., Wu, X., Wang, H., & Wu, T.* (2021). Prediction and analysis of domestic water consumption based on optimized grey and Markov model. Water Supply, 21(7), 3887-3899. https://doi.org/10.2166/ws.2021.146
[20] Li, R., Chang, Y., & Wang, Z.* (2021). Study of optimal allocation of water resources in Dujiangyan irrigation district of China based on an improved genetic algorithm. Water Supply, 21(6), 2989-2999. https://doi.org/10.2166/ws.2020.302
二、联系方式
办公室：威尼斯wns8885566401
电子邮箱：zcwang@shou.edu.cn
手机：15692166813