|
水位预测的机器学习方法
|
Abstract:
水是所有生命的物质基础,与人类的生活、生产息息相关,水资源的开发和利用对生活生产、生态环境和社会经济发展具有重要的影响。研究水位动态变化规律以及提前预测水位,对于水资源的开发利用、湿地保护、防洪减灾、船舶配载和安全通航等具有重要的现实意义。水位的高低受到天气、水文等各种复杂因素的影响,水位预测是水文、气象、航道和海事等部门关注的难题和研究热点。本文对近年来机器学习方法在水位预测方面的研究进展和所取得的成果进行分析和评述,分析了现有方法存在的问题和面临的挑战,指出水位预测未来的主要研究方向。
Water is the basis for all life and is closely related to human life and production. The development and utilization of water resources have a significant impact on life, production, ecological environment, and social and economic development. Investigating the dynamic patterns of water level changes and forecasting water levels ahead of time are of great practical importance. They play a crucial role in multiple aspects, such as the exploitation and utilization of water resources, the conservation of wetlands, the prevention of floods and reduction of disasters, the loading arrangements of ships, and the guarantee of safe navigation. The water level is affected by various complex factors such as weather and hydrology. The prediction of water level is a difficult thing concerned by departments such as hydrology, meteorology, etc. hence it has become the research focuses in the past years. In this paper, various machine learning algorithms used for predicting water level are reviewed and commented, and the challenge that researchers in this field faced is then pointed out, in the end, the perspectives in this realm are also proposed.
[1] | Mosavi, A., Ozturk, P. and Chau, K. (2018) Flood Prediction Using Machine Learning Models: Literature Review. Water, 10, Article 1536. https://doi.org/10.3390/w10111536 |
[2] | 周涵. 多模型融合的黑河流域地下水水位预测方法研究[D]: [硕士学位论文]. 北京: 中国石油大学, 2020. |
[3] | 马森标, 唐卫明, 陈春强. LSTM优化模型的水库水位预测研究[J]. 福建电脑, 2022, 38(5): 1-8. |
[4] | Zhao, M. and Hendon, H.H. (2009) Representation and Prediction of the Indian Ocean Dipole in the POAMA Seasonal Forecast Model. Quarterly Journal of the Royal Meteorological Society, 135, 337-352. https://doi.org/10.1002/qj.370 |
[5] | 李铭鹏. 覆盖型岩溶地基基础处理方法的探索[D]: [硕士学位论文]. 深圳: 深圳大学, 2017. |
[6] | 许江涛. 地下水数值模拟及其三维可视化技术的研究与应用[D]: [硕士学位论文]. 焦作: 河南理工大学, 2009. |
[7] | Cunningham, A.B. and Sinclair, P.J. (1979) Application and Analysis of a Coupled Surface and Groundwater Model. In: Developments in Water Science, Elsevier, 129-148. https://doi.org/10.1016/s0167-5648(09)70014-0 |
[8] | 李永凯, 王拓, 肖晒. 水力学耦合水文学模型在沮漳河河溶水文站洪水水位预报的应用研究[J]. 水资源开发与管理, 2020(6): 4-8. |
[9] | 李加龙, 李慧赟, 罗潋葱, 等. 抚仙湖历史水位反演与未来30年水位变化预测[J]. 湖泊科学, 2022, 34(3): 958-971. |
[10] | Lai, X., Jiang, J., Liang, Q. and Huang, Q. (2013) Large-Scale Hydrodynamic Modeling of the Middle Yangtze River Basin with Complex River-Lake Interactions. Journal of Hydrology, 492, 228-243. https://doi.org/10.1016/j.jhydrol.2013.03.049 |
[11] | Rajaee, T., Ebrahimi, H. and Nourani, V. (2019) A Review of the Artificial Intelligence Methods in Groundwater Level Modeling. Journal of Hydrology, 572, 336-351. https://doi.org/10.1016/j.jhydrol.2018.12.037 |
[12] | Sánchez-Maroño, N., Alonso-Betanzos, A. and Tombilla-Sanromán, M. (2007) Filter Methods for Feature Selection—A Comparative Study. In: Lecture Notes in Computer Science, Springer, 178-187. https://doi.org/10.1007/978-3-540-77226-2_19 |
[13] | Kohavi, R. and John, G.H. (1997) Wrappers for Feature Subset Selection. Artificial Intelligence, 97, 273-324. https://doi.org/10.1016/s0004-3702(97)00043-x |
[14] | Blum, A.L. and Langley, P. (1997) Selection of Relevant Features and Examples in Machine Learning. Artificial Intelligence, 97, 245-271. https://doi.org/10.1016/s0004-3702(97)00063-5 |
[15] | Lal, T.N., Chapelle, O., Weston, J. and Elisseeff, A. (2006) Embedded Methods. In: Studies in Fuzziness and Soft Computing, Springer, 137-165. https://doi.org/10.1007/978-3-540-35488-8_6 |
[16] | Das, M., Ghosh, S.K., Chowdary, V.M., Saikrishnaveni, A. and Sharma, R.K. (2016) A Probabilistic Nonlinear Model for Forecasting Daily Water Level in Reservoir. Water Resources Management, 30, 3107-3122. https://doi.org/10.1007/s11269-016-1334-6 |
[17] | Liu, H. and Motoda, H. (1998) Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers. |
[18] | Choi, C., Kim, J., Han, H., Han, D. and Kim, H.S. (2019) Development of Water Level Prediction Models Using Machine Learning in Wetlands: A Case Study of Upo Wetland in South Korea. Water, 12, Article 93. https://doi.org/10.3390/w12010093 |
[19] | 王迎飞, 黄应平, 肖敏, 等. 基于注意力机制的LSTM长江汛期水位预测方法研究[J]. 三峡大学学报: 自然科学版, 2022, 44(3): 13-19. |
[20] | El-Diasty, M., Al-Harbi, S. and Pagiatakis, S. (2018) Hybrid Harmonic Analysis and Wavelet Network Model for Sea Water Level Prediction. Applied Ocean Research, 70, 14-21. https://doi.org/10.1016/j.apor.2017.11.007 |
[21] | Hou, T.H. (1991) Sequential Tidal Analysis and Prediction. Ph.D. Thesis, University of New Brunswick. |
[22] | Li, Y., Peng, G., Chen, P., Chen, K., Li, R. and Song, Z. (2022) Harmonic Analysis of Short-Term Tidal Level Prediction Model for Tidal Reaches. Arabian Journal of Geosciences, 15, Article No. 473. https://doi.org/10.1007/s12517-022-09757-1 |
[23] | Tu, Z., Gao, X., Xu, J., Sun, W., Sun, Y. and Su, D. (2021) A Novel Method for Regional Short-Term Forecasting of Water Level. Water, 13, Article 820. https://doi.org/10.3390/w13060820 |
[24] | Wang, B., Wang, B., Wu, W., Xi, C. and Wang, J. (2020) Sea-Water-Level Prediction via Combined Wavelet Decomposition, Neuro-Fuzzy and Neural Networks Using SLA and Wind Information. Acta Oceanologica Sinica, 39, 157-167. https://doi.org/10.1007/s13131-020-1569-1 |
[25] | Malekzadeh, M., Kardar, S., Saeb, K., Shabanlou, S. and Taghavi, L. (2019) A Novel Approach for Prediction of Monthly Ground Water Level Using a Hybrid Wavelet and Non-Tuned Self-Adaptive Machine Learning Model. Water Resources Management, 33, 1609-1628. https://doi.org/10.1007/s11269-019-2193-8 |
[26] | Adamowski, J. and Chan, H.F. (2011) A Wavelet Neural Network Conjunction Model for Groundwater Level Forecasting. Journal of Hydrology, 407, 28-40. https://doi.org/10.1016/j.jhydrol.2011.06.013 |
[27] | 倪汉杰, 蒋仲廉, 初秀民, 钟诚. 基于DWT-LSTM的航道水位智能预测模型研究[J]. 中国航海, 2021, 44(2): 97-102. |
[28] | 刘立燕. 基于Copula函数和神经网络模型的洪水预测[D]: [硕士学位论文]. 南京: 南京邮电大学, 2018. |
[29] | Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., et al. (1998) The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-Stationary Time Series Analysis. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 454, 903-995. https://doi.org/10.1098/rspa.1998.0193 |
[30] | 王亦斌, 孙涛, 梁雪春, 谢海洋. 基于EMD-LSTM模型的河流水量水位预测[J]. 水利水电科技进展, 2020, 40(6): 40-47. |
[31] | 余周, 姜涛, 范鹏辉, 牛超群, 陈兵. 基于EMD-DELM-LSTM组合模型的湖泊水位多时间尺度预测[J]. 长江科学院院报, 2024, 41(6): 28-35. |
[32] | 潘志浩, 陈森林, 梁斌, 等. 基于EMD-LSTM模型的右江水库日入库流量预测[J]. 水资源研究, 2022, 11(1): 20-29. |
[33] | Wu, Z. And Huang, N.E. (2009) Ensemble Empirical Mode Decomposition: A Noise-Assisted Data Analysis Method. Advances in Adaptive Data Analysis, 1, 1-41. https://doi.org/10.1142/s1793536909000047 |
[34] | Gong, Y., Wang, Z., Xu, G. and Zhang, Z. (2018) A Comparative Study of Groundwater Level Forecasting Using Data-Driven Models Based on Ensemble Empirical Mode Decomposition. Water, 10, Article 730. https://doi.org/10.3390/w10060730 |
[35] | Wang, W., Chau, K., Xu, D. and Chen, X. (2015) Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition. Water Resources Management, 29, 2655-2675. https://doi.org/10.1007/s11269-015-0962-6 |
[36] | Rajaee, T. and Boroumand, A. (2015) Forecasting of Chlorophyll-A Concentrations in South San Francisco Bay Using Five Different Models. Applied Ocean Research, 53, 208-217. https://doi.org/10.1016/j.apor.2015.09.001 |
[37] | Hodgson, F.D.I. (1978) The Use of Multiple Linear Regression in Simulating Ground-Water Level Responses. Groundwater, 16, 249-253. https://doi.org/10.1111/j.1745-6584.1978.tb03232.x |
[38] | 陈志宏. 多元线性回归方法在地下水水位预测中的应用[J]. 北京地质, 1999, 11(3): 20-26. |
[39] | 王盼秋, 曾丹, 孟凡宇. 回归分析法在水位预测中的应用[J], 山东水利, 2015(3): 29-31. |
[40] | Ebtehaj, I., Sammen, S.S., Sidek, L.M., Malik, A., Sihag, P., Al-Janabi, A.M.S., et al. (2021) Prediction of Daily Water Level Using New Hybridized GS-GMDH and ANFIS-FCM Models. Engineering Applications of Computational Fluid Mechanics, 15, 1343-1361. https://doi.org/10.1080/19942060.2021.1966837 |
[41] | Yu, Z., Lei, G., Jiang, Z. and Liu, F. (2017) ARIMA Modelling and Forecasting of Water Level in the Middle Reach of the Yangtze River. 2017 4th International Conference on Transportation Information and Safety (ICTIS), Banff, 8-10 August 2017, 172-177. https://doi.org/10.1109/ictis.2017.8047762 |
[42] | 陈家辉, 李敏. 基于ARIMA模型的西江梧州下游段通航水位预测研究[J]. 西部交通科技, 2022(7): 200-203. |
[43] | 余珍. 基于时间序列分析的航道水位预测研究[J]. 中国水运(下半月), 2018, 18(10): 148-150. |
[44] | Wang, Q. and Wang, S. (2020) Machine Learning-Based Water Level Prediction in Lake Erie. Water, 12, Article 2654. https://doi.org/10.3390/w12102654 |
[45] | Castillo-Botón, C., Casillas-Pérez, D., Casanova-Mateo, C., Moreno-Saavedra, L.M., Morales-Díaz, B., Sanz-Justo, J., et al. (2020) Analysis and Prediction of Dammed Water Level in a Hydropower Reservoir Using Machine Learning and Persistence-Based Techniques. Water, 12, Article 1528. https://doi.org/10.3390/w12061528 |
[46] | 苏国韶, 张研, 张小飞. 高斯过程机器学习方法在地下水位预测中的应用[J]. 中国农村水利水电, 2008(12): 48-50. |
[47] | Ju-Long, D. (1982) Control Problems of Grey Systems. Systems & Control Letters, 1, 288-294. https://doi.org/10.1016/s0167-6911(82)80025-x |
[48] | 周振民, 赵明亮, 李玲. GM(1,1)模型在滦河下游地区地下水位预测中的应用[J]. 中国农村水利水电, 2011(2): 50-52. |
[49] | 卞宁. 改进型灰色系统在航道水位预测中的应用[J]. 中国水运, 2018(5): 75-80. |
[50] | 刘越. 白洋淀周边地下水数量分析及水位预测[D]: [硕士学位论文]. 保定: 河北农业大学, 2010. |
[51] | 杨建飞, 刘俊民, 陈琳. 基于灰色残差模型的灌区地下水最小埋深预测[J]. 人民黄河, 2011, 33(7): 107-108. |
[52] | 闫英男. 基于灰色理论与机器学习的黑河地下水水位预测研究[D]: [硕士学位论文]. 兰州: 兰州大学, 2018. |
[53] | Ibrahim, N., Wibowo, A. (2014) Support Vector Regression with Missing Data Treatment Based Variables Selection for Water Level Prediction of Galas River in Kelantan Malaysia. WSEAS Transactions on Mathematics Archive, 13, 69-78. |
[54] | Yoon, H., Hyun, Y., Ha, K., Lee, K. and Kim, G. (2016) A Method to Improve the Stability and Accuracy of ANN-and SVM-Based Time Series Models for Long-Term Groundwater Level Predictions. Computers & Geosciences, 90, 144-155. https://doi.org/10.1016/j.cageo.2016.03.002 |
[55] | 刘成忠, 黄高宝, 韩建民. 基于PCA特征提取的SVR地下水位动态预测方法[J]. 工程勘察, 2011, 39(3): 36-39. |
[56] | Kisi, O., Shiri, J., Karimi, S., Shamshirband, S., Motamedi, S., Petković, D., et al. (2015) A Survey of Water Level Fluctuation Predicting in Urmia Lake Using Support Vector Machine with Firefly Algorithm. Applied Mathematics and Computation, 270, 731-743. https://doi.org/10.1016/j.amc.2015.08.085 |
[57] | 朱颖洁. 西江梧州站基于支持向量机的水位预测模型研究[J]. 广东水利水电, 2022(11): 39-42. |
[58] | 赵新宇, 费良军. LM算法的神经网络在灌区地下水位预测中的应用研究[J]. 沈阳农业大学学报, 2006, 37(2): 213-216. |
[59] | Panyadee, P., Champrasert, P. and Aryupong, C. (2017) Water Level Prediction Using Artificial Neural Network with Particle Swarm Optimization Model. 2017 5th International Conference on Information and Communication Technology (ICoIC7), Melaka, 17-19 May 2017, 1-6. https://doi.org/10.1109/icoict.2017.8074670 |
[60] | Phitakwinai, S., Auephanwiriyakul, S. and Theera-Umpon, N. (2016) Multilayer Perceptron with Cuckoo Search in Water Level Prediction for Flood Forecasting. 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, 24-29 July 2016, 519-524. https://doi.org/10.1109/ijcnn.2016.7727243 |
[61] | 高学平, 闫晨丹, 张岩, 孙博闻. 基于BP神经网络的调水工程调蓄水位预测模型[J]. 南水北调与水利科技, 2018, 16(1): 8-13. |
[62] | 余开华. 小波神经网络模型在河道流量水位预测中的应用[J]. 水资源与水工程学报, 2013(2): 204-208. |
[63] | 李欣, 王超, 赵虎川. 基于时空序列模型的RBF神经网络在河流水位预测中的应用[J]. 城市勘测, 2016(5): 34-39. |
[64] | Huang, G., Zhu, Q. and Siew, C. (2006) Extreme Learning Machine: Theory and Applications. Neurocomputing, 70, 489-501. https://doi.org/10.1016/j.neucom.2005.12.126 |
[65] | Shiri, J., Shamshirband, S., Kisi, O., Karimi, S., Bateni, S.M., Hosseini Nezhad, S.H., et al. (2016) Prediction of Water-Level in the Urmia Lake Using the Extreme Learning Machine Approach. Water Resources Management, 30, 5217-5229. https://doi.org/10.1007/s11269-016-1480-x |
[66] | Yadav, B., Ch, S., Mathur, S. and Adamowski, J. (2017) Assessing the Suitability of Extreme Learning Machines (ELM) for Groundwater Level Prediction. Journal of Water and Land Development, 32, 103-112. https://doi.org/10.1515/jwld-2017-0012 |
[67] | Hochreiter, S. and Schmidhuber, J. (1997) Long Short-Term Memory. Neural Computation, 9, 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735 |
[68] | Cho, K., van Merrienboer, B., Bahdanau, D. and Bengio, Y. (2014). On the Properties of Neural Machine Translation: Encoder-Decoder Approaches. Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation, Doha, October 2014, 103-111. https://doi.org/10.3115/v1/w14-4012 |
[69] | Zhao, Z., Chen, W., Wu, X., Chen, P.C.Y. and Liu, J. (2017) LSTM Network: A Deep Learning Approach for Short-Term Traffic Forecast. IET Intelligent Transport Systems, 11, 68-75. https://doi.org/10.1049/iet-its.2016.0208 |
[70] | Anusha, N. (2019) Data Driven Approaches for Water Level Prediction. Ph.D. Thesis, University of Houston. |
[71] | Zhang, J., Zhu, Y., Zhang, X., Ye, M. and Yang, J. (2018) Developing a Long Short-Term Memory (LSTM) Based Model for Predicting Water Table Depth in Agricultural Areas. Journal of Hydrology, 561, 918-929. https://doi.org/10.1016/j.jhydrol.2018.04.065 |
[72] | 陈睿鹤. 基于小波变换和GRU深度神经网络的地下水位预测研究[D]: [硕士学位论文]. 武汉: 华中科技大学, 2018. |
[73] | 潘明阳, 周海南, 李增辉, 等. 智能水位预测服务系统研究[J]. 大连海事大学学报, 2020, 46(3): 31-37. |
[74] | Sutskever, I., Vinyals, O. and Le, Q.V. (2014) Sequence to Sequence Learning with Neural Networks. |
[75] | 刘艳, 张婷, 康爱卿, 等. 基于Seq2Seq模型的短期水位预测[J]. 水利水电科技进展, 2022(3): 57-63. |
[76] | Noor, F., Haq, S., Rakib, M., Ahmed, T., Jamal, Z., Siam, Z.S., et al. (2022) Water Level Forecasting Using Spatiotemporal Attention-Based Long Short-Term Memory Network. Water, 14, Article 612. https://doi.org/10.3390/w14040612 |
[77] | Wang, Y., Huang, Y., Xiao, M., Zhou, S., Xiong, B. and Jin, Z. (2023) Medium-Long-Term Prediction of Water Level Based on an Improved Spatio-Temporal Attention Mechanism for Long Short-Term Memory Networks. Journal of Hydrology, 618, Article 129163. https://doi.org/10.1016/j.jhydrol.2023.129163 |
[78] | 李烈. 机器学习方法在淮河水位预测中的应用[D]: [硕士学位论文]. 蚌埠: 安徽财经大学, 2022. |
[79] | 王迎飞. 基于自适应时空注意力机制的河流水位预测研究[D]: [硕士学位论文]. 宜昌: 三峡大学, 2022. |
[80] | Changjun Zhu, and Qin Ju, (2009) United Grey System-Neural Network Model and Its Application in Prediction of Groundwater Level. 2009 International Conference on Industrial Mechatronics and Automation, Chengdu, 15-16 May 2009, 434-437. https://doi.org/10.1109/icima.2009.5156656 |
[81] | Barzegar, R., Aalami, M.T. and Adamowski, J. (2021) Coupling a Hybrid CNN-LSTM Deep Learning Model with a Boundary Corrected Maximal Overlap Discrete Wavelet Transform for Multiscale Lake Water Level Forecasting. Journal of Hydrology, 598, Article 126196. https://doi.org/10.1016/j.jhydrol.2021.126196 |
[82] | Pan, M., Zhou, H., Cao, J., Liu, Y., Hao, J., Li, S., et al. (2020) Water Level Prediction Model Based on GRU and CNN. IEEE Access, 8, 60090-60100. https://doi.org/10.1109/access.2020.2982433 |
[83] | Miau, S. and Hung, W. (2020) River Flooding Forecasting and Anomaly Detection Based on Deep Learning. IEEE Access, 8, 198384-198402. https://doi.org/10.1109/access.2020.3034875 |
[84] | 孙英军, 唐为昊, 王成, 李英德. 基于CNN-Seq2seq的河道水位区间预测方法[J]. 浙江工业大学学报, 2022, 50(4): 381-392. |
[85] | Nie, Q., Wan, D. and Wang, R. (2021) CNN-BiLSTM Water Level Prediction Method with Attention Mechanism. Journal of Physics: Conference Series, 2078, Article 012032. https://doi.org/10.1088/1742-6596/2078/1/012032 |
[86] | 刘青松, 严华, 卢文龙. 基于AR-RNN的多变量水位预测模型研究[J]. 人民长江, 2020, 51(10): 94-99. |
[87] | 陈帅宇, 赵龑骧, 蒋磊. 基于ARIMA-CNN-LSTM模型的黄河开封段水位预测研究[J]. 水利水电快报, 2023, 44(1): 15-22. |