Toward Effective Pattern Recognition Based on Enhanced Weighted K-Mean Clustering Algorithm for Groundwater Resource Planning in Point Cloud

Recently groundwater scarcity has accelerated drilling operations worldwide as drilled boreholes are substantial for replenishing the needs of safe drinking water and achieving long-term sustainable development goals. However, the quest for achieving optimal drilling efficiency is ever continued. This paper aims to provide valuable insights into borehole drilling data utilizing the potential of advanced analytics by employing several enhanced cluster analysis techniques to propel drilling efficiency optimization and knowledge discovery. The study proposed an L2-weighted K-mean clustering algorithm in which the mean is computed from transformed weighted feature space. To verify the effectiveness of our proposed L2-weighted K-mean algorithm, we performed a comparative analysis of the proposed work with traditional clustering algorithms to estimate the digging time and depth for different soil materials and land layers. The proposed clustering scheme is evaluated widely used evaluation metrics such as Dunn Index, Davies–Bouldin index (DBI), Silhouette coefficient (SC), and Calinski–Harabaz Index (CHI). The study results highlight the significance of the proposed clustering algorithm as it achieved better clustering results than conventional clustering approaches. Moreover, for facilitation of subsequent learning, achievement of reliable classification, and generalization, we performed feature extraction based on the time interval of the drilling process according to soil material and land layer. We formulated the solution by grouping the extracted features into six different blocks to achieve our desired objective. Each block corresponds to various characteristics of soil materials and land layers. Extracted features are examined and visualized in point cloud space to analyze the water level patterns, depth, and days required to complete the drilling operations.