方卫华1 2，张 慧1 2，夏童童1 2

(1.水利部南京水利水文自动化研究所，江苏 南京 210012;2.水利部水文水资源监控工程技术研究中心，江苏 南京 210012)

　　摘 要：针对水工程现场运行环境恶劣，监测仪器损坏率高、实时性分析不及时，不具备大型大型计算分析设备，从而导致统计分析和建模样本容量不够的问题，基于有限稀疏样本采用轻量化生成式对抗网络(GANs)进行数据现场生成试验研究。在优化生成器和判别网络结构及优选激活函数的基础上，搭建轻量化GANs模型，采用笔记本电脑在工程现地实现数据快速生成。KL散度及Wasserstein距离分析表明，与时间序列预测方法相比，生成数据与原始数据概率分布之间的距离最高减少33.3%。研究表明：采用基于轻量化GANs的数据生成方法可用于现场数据便捷生成，解决样本数量不够问题，为今后应用大样本建模和实测数据总体特征分析提供有效的解决方法，对进一步推广人工智能在水利工程上的应用具有重要意义。

　　关键词：轻量化GANs;现场数据生成;充水试验;时间序列;概率距离;引水隧洞

　　Generation analysis of water filling data of diversion tunnel based on light GANs

　　FANG Weihua1，2, ZHANG Hui1，2, XIA Tongtong1 2

　　(1. Nanjing Automation Institute of Water Conservancy and Hydrology, Ministry of Water Resources, Nanjing 210012, China;

　　2. Research Center on Hydrology and Water Resources Monitoring, Ministry of Water Resources, Nanjing 210012, China)

　　Abstract: To solve the problem of insufficient sample size during statistical analysis and modeling, this paper conducts experimental research using light Generative Adversarial Networks(GANs ) based on finite sparse samples, against the problems in hydraulic engineering site such as poor operation environment, high damage rate of monitoring instruments, no timely real-time analysis and no large computing and analysis instruments. A light GANs model is built to realize fast filling of engineering field data by using laptop, based on the optimization of generator and discriminant network structure and the activation function. Compared with the time series prediction method, the KL divergence and Wasserstein distance analysis can reduce the distance between generated data and original data up to 33.3% in probability distribution. Research indicates that the proposed method is suitable for convenient and efficient filling of field water hammer data and able to solve the problem of insufficient sample size. It provides an effective solution for the future application of large sample modeling analysis and the overall characteristics analysis of measured data, which is of great significance to further popularize the application of artificial intelligence in water conservancy projects.

　　Key words: light GANs; field data generation; water filling test; time series; probability distance; diversion tunnel