王兆才(上海海洋大學信息學院教師)

發表論文

主訂連匪持省部級項目8項，第一（通訊）作者發提章紋捉表SCI論嫌抹文50餘篇，近3年第一（催她擔愉通訊）論櫻匙蒸文洪嫌故如下：

[1] Yao, Z., Wang, Z.*, Cui, X., & Zhao, H. (2023). Research on multi-objectiveoptimal allocation of regional water resources based on improved sparrow searchalgorithm. Journal of Hydroinformatics. https://doi.org/10.2166/hydro.2023.037

[2] Tan, R., Hu, Y., &獄采應 Wang, Z.* (2023). A multi-source data-driven model of lake water level basedon variational modal decomposition and external factors with optimized bi-directionallong 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 qualityprediction in Tai Lake, China, using secondary modal decomposition withmultidimensional 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 deeplearning and error correction for crude oil futures prices forecast. ResourcesPolicy, 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 forprecipitation 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 waterquality 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 ConvolutionalNeural 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 onadaptive evolutionary Artificial Bee Colony Algorithm for water quality indexprediction. 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 anartificial 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 BPneural 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 resourcesdemand in Taiyuan City based on principal component analysis and BP neuralnetwork. 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). Solvingthe Family Traveling Salesperson Problem in the Adleman–Lipton Model Based onDNA 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). Aparallel algorithm to solve the multiple travelling salesmen problem based on molecularcomputing model, InternationalJournal 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 forSolving the Quota Traveling Salesman ProblemBased 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 waterresources 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 domesticwater 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 problemwith 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 salesmanproblem based bioinspired computing model. International Journal of ComputationalIntelligence 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 onmolecular 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 domesticwater consumption based on optimized grey and Markovmodel. 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 irrigationdistrict of China based on an improved genetic algorithm. Water Supply, 21(6),2989-2999. https://doi.org/10.2166/ws.2020.302

王兆才(上海海洋大學信息學院教師)

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