机器学习在地下水环境背景值与污染风险评价的应用和展望

地下水资源在世界各国水资源中占有举足轻重的地位，对人类生存发展、维系生态系统健康发挥着重要作用。现阶段地下水污染日益严重，地下水环境背景值研究和污染风险评价对地下水污染防治工作具有重要意义。由于地下水污染影响因素复杂，地下水化学组分空间分布的非均质性、地下水样品采集的小样本问题与大尺度区域的高计算代价，都对传统的污染风险评价方法构成了极大挑战。机器学习作为人工智能的核心，已成为水文地质领域研究的前沿热点，通过智能高效的数据处理和挖掘，在地下水化学组分的分布、变化以及赋存机制等方向已得到探索和尝试。本文全面介绍了近年来在地下水污染研究方面应用的机器学习方法，涵盖了以聚类为主的非监督学习算法，以回归为主的监督学习算法，以提升算法效率为目标的混合算法，以及以神经网络为核心的深度结构算法，展示了不同类型算法在地下水污染研究方面的成果，详细归纳了各种算法的机理，对算法的技术优劣及适用方向进行了探讨；最后对机器学习在地下水污染方面的应用发展趋势进行了展望，建议探索高效集成学习模型，以弥补单一算法的不足，同时发展面向小样本的深度学习建模技术，提高地下水污染评价精度，拓展和丰富新方法新技术在地下水污染研究方面的应用。

Machine learning model in groundwater background value and pollution riskassessment: Application and prospects

Groundwater resources play an important role in water resources of all countries in the world. They are essential for human survival, development and maintenance of health of ecosystem. At this stage, groundwater pollution is becoming increasingly serious. The research on groundwater environmental background value and pollution risk assessment is of great significance for the prevention and control of groundwater pollution. The factors influencing groundwater pollution are complex, including heterogeneity in spatial distribution of groundwater chemical components, small groundwater sample size and high calculation cost of large scale areas. These limitations pose a great challenge in the traditional pollution risk assessment methods. As the core of artificial intelligence, machine learning has recently become a frontier hot spot in hydrogeology research, newer approaches using intelligent and efficient data processing and mining have explored the distribution, variation and occurrence mechanism of groundwater chemical components. This paper comprehensively introduces the machine learning methods applied in groundwater pollution research in recent years, including unsupervised learning algorithm based on clustering, supervised learning algorithms based on regression, hybrid algorithm aimed at improving algorithm efficiency, and depth structure algorithm with neural network as the core, showing the achievements of different types of algorithms in groundwater pollution research. The mechanism of each algorithm is summarized in detail, the technical advantages and disadvantages of the algorithm and the applicable direction are discussed. Finally, the application and development trend of machine learning in the field of groundwater pollution are evaluated. It is suggested to explore an efficient integrated learning model to make up for the shortcomings of a single algorithm. At the same time, the deep learning modeling technology for small samples is developed to improve the accuracy of groundwater pollution assessment, and expand and enrich the application of new methods and technologies in groundwater pollution research.