Optimized machine learning models for water quality prediction: Integrating support vector machines and random forest through nonlinear functional analysis
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Keywords:
water quality prediction, nonlinear functional analysis, random forest, support vector machine, environmental monitoring, hybrid machine learning modelAbstract
It is imperative to predict water quality accurately to monitor the environment, human health, and smart water management. Conventional empirical evaluation techniques are inadequate in the description of nonlinear relationships of physicochemical parameters (pH, dissolved oxygen, turbidity, nitrate concentration, and conductivity). This paper hypothesizes a streamlined hybrid machine learning model, which combines Support Vector Machines (SVM) with Random Forest (RF) with nonlinear functional analysis to add predictive accuracy. The model uses nonlinear mapping of kernels, ranking of the importance of variables and functional decomposition to approximate interactions involving complex parameters. It also introduces a multi-stage optimization process that incorporates grid search and cross-validation with nonlinear functional transformation in order to find the best hyperparameters to use in SVM and RF models. Multiyear datasets (collected at freshwater sources) were experimented, and it was found that predictive accuracy improved significantly, with the hybrid model showing that the RMSE was 14.2% lower, and the Pearson correlation coefficient was 9.1 times higher than baseline ML models. The analysis of feature sensitivity and functional interaction demonstrates that nutrient load and dissolved oxygen have a strong nonlinear relationship, which confirms the potential of the proposed framework to represent the ecological relationships. These results indicate that nonlinear functional analysis can allow more consistent and interpretable machine learning models to predict water quality to support the environmental monitoring system in a sustainable way.
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