RIKEN Center for Computational Science, Data Assimilation Research Team, 7-1-26, Minatojima-minami-machi, Chuo-ku, Kobe, Hyogo 650-0047, Japan.
Department of Earth and Environmental Science, Faculty of Science, Kumamoto University, 2-39-1 Kurokami, Kumamoto 860-8555, Japan.
Sci Total Environ. 2022 Nov 10;846:157281. doi: 10.1016/j.scitotenv.2022.157281. Epub 2022 Jul 12.
The holistic understanding of hydrochemical features is a crucial task for management and protection of water resources. However, it is challenging for a complex region, where multiple factors can cause hydrochemical changes in studied catchment. We collected 208 groundwater samples from such region in Kumamoto, southern Japan to explicitly characterize these processes by applying machine learning technique. The analyzed groundwater chemistry data like major cations and anions were fed to the self-organizing map (SOM) and the results were compared with classical classification approaches like Stiff diagram, standalone cluster analysis and score plots of principal component analysis (PCA). The SOM with integrated application of clustering divided the data into 11 clusters in this complex region. We confirmed that the results provide much greater details for the associated hydrochemical and contamination processes than the traditional approaches, which show quite good correspondence with the recent high resolution hydrological simulation model and aspects from geochemical modeling. However, the careful application of the SOM is necessary for obtaining accurate results. This study tested different normalization approaches for selecting the best SOM map and found that the topographic error (TE) was more important over the quantization error (QE). For instance, the lower QE obtained from min-max and log normalizations showed problems after clustering the SOM map, since the QE did not confirm the topological preservation. In contrast, the lowest TE obtained from Z-transformation data showed better spatial matching of the clusters with relevant hydrochemical characteristics. The results from this study clearly demonstrated that the SOM is a helpful approach for explicit understanding of the hydrochemical processes on reginal scale that may capably facilitate better groundwater resource management.
全面了解水化学特征对于水资源的管理和保护至关重要。然而，在一个复杂的地区，多个因素可能导致研究流域的水化学变化，这是一项具有挑战性的任务。我们从日本南部熊本县的一个这样的地区收集了 208 个地下水样本，通过应用机器学习技术来明确表征这些过程。所分析的地下水化学数据（如主要阳离子和阴离子）被输入到自组织映射（SOM）中，并将结果与经典分类方法（如刚性图、独立聚类分析和主成分分析（PCA）得分图）进行比较。SOM 与聚类的综合应用将数据分为 11 个集群，在这个复杂的地区。我们确认，与传统方法相比，该结果为相关的水文化学和污染过程提供了更多的细节，与最近的高分辨率水文模拟模型和地球化学模型的方面非常吻合。然而，为了获得准确的结果，需要仔细应用 SOM。本研究测试了不同的归一化方法来选择最佳的 SOM 图，并发现地形误差（TE）比量化误差（QE）更为重要。例如，来自最小-最大和对数正态化的较低 QE 在聚类 SOM 图后显示出问题，因为 QE 没有确认拓扑保存。相比之下，来自 Z 变换数据的最低 TE 显示出与相关水文化学特征更好的聚类空间匹配。本研究的结果清楚地表明，SOM 是一种有助于明确理解区域尺度水文化学过程的有用方法，可能能够更好地促进地下水资源管理。
