Machine Learning-Based Water Quality Classification Assessment

Water is a vital resource, and its quality has a direct impact on human health. Groundwater, as one of the primary water sources, requires careful monitoring to ensure its safety. Although manual methods for testing water quality are accurate, they are often time-consuming, costly, and inefficient when dealing with large and complex data sets. In recent years, machine learning has become an effective alternative for water quality assessment. However, current approaches still face challenges, such as the limited performance of individual models, minimal improvements from optimization algorithms, lack of dynamic feature weighting mechanisms, and potential information loss when simplifying model inputs. To address these challenges, this paper proposes a hybrid model, BS-MLP, which combines GBDT (gradient-boosted decision tree) and MLP (multilayer perceptron). The model leverages GBDT's strength in feature selection and MLP's capability to manage nonlinear relationships, enabling it to capture complex interactions between water quality parameters. We employ Bayesian optimization to fine-tune the model's parameters and introduce a feature-weighting attention mechanism to develop the BS-FAMLP model, which dynamically adjusts feature weights, enhancing generalization and classification accuracy. In addition, a comprehensive parameter selection strategy is employed to maintain data integrity. These innovations significantly improve the model's classification performance and efficiency in handling complex water quality environments and imbalanced datasets. This model was evaluated using a publicly available groundwater quality dataset consisting of 188,623 samples, each with 15 water quality parameters and corresponding labels. The BS-FAMLP model shows strong classification performance, with optimized hyperparameters and an adjusted feature-weighting attention mechanism. Specifically, it achieved an accuracy of 0.9616, precision of 0.9524, recall of 0.9655, F1 Score of 0.9589, and an AUC score of 0.9834 on the test set. Compared to single models, classification accuracy improved by approximately 10%, and when compared to other hybrid models with additional attention mechanisms, BS-FAMLP achieved an optimal balance between classification performance and computational efficiency. The core objective of this study is to utilize the acquired water quality parameter data for efficient classification and assessment of water samples, with the aim of streamlining traditional laboratory-based water quality analysis processes. By developing a reliable water quality classification model, this research provides robust technical support for water safety management.