王兆才

上海海洋大学信息学院教师
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王兆才，男，1979年2月出生，山东潍坊人，[1]汉族。2006年3月毕业于上海交通大学应用数学专业。主要研究领域为生物数学和DNA计算。在本科教学方面，王兆才教授高等数学、线性代数、概率论与数理统计等多门数学基础课程。[2]

发表论文

主持省部级项目8项，第一（通讯）作者发表SCI论文50余篇，近3年第一（通讯）论文如下：
  1. - Yao, Z., Wang, Z., Cui, X., & Zhao, H. (2023). Research on multi-objective optimal allocation of regional water resources based on improved sparrow search algorithm. Journal of Hydroinformatics. https://doi.org/10.2166/hydro.2023.037
  2. - 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, 167, 105766. https://doi.org/10.1016/j.envsoft.2023.105766
  3. - 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
  4. - 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
  5. - 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
  6. - 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
  7. - 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
  8. - 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
  9. - 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
  10. - 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
  11. - 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
  12. - 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
  13. - 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
  14. - 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
  15. - 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
  16. - 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
  17. - 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
  18. - 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
  19. - 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
  20. - 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
  21. - 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