A hybrid machine learning approach for imbalanced irrigation water quality classification

Global food security is increasingly dependent on irrigation, particularly in regions experiencing freshwater scarcity. Conventional laboratory methods for assessing Irrigation Water Quality Index (IWQI) are often slow and inaccessible to small-scale farmers, especially in developing countries. This study proposes an efficient machine learning (ML) approach to enhance the classification performance of IWQI into five classes: no restriction, low restriction, moderate restriction, high restriction, and severe restriction. A dataset comprising 62,499 samples with six hydrochemical parameters (EC, Cl⁻, HCO₃⁻, Na⁺, Ca²⁺, and Mg²⁺) was collected, preprocessed, and labeled. Missing values were imputed using a Random Forest model, achieving an R² of 0.98. To address class imbalance, synthetic resampling, class weighting, and apportioned margins were employed to train three ML models: two stacked ensembles and an Apportioned Margin Support Vector Machine (AMSVM). Class weighting was applied to the first ensemble, adaptive synthetic sampling (ADASYN) resampling was utilized for the second, and AMSVM was adjusted for class imbalance through apportioned margins and class weighting. The class-weighted ensemble achieved 98.5% accuracy, precision, recall, and F1-score, while the ADASYN ensemble attained 97.5% accuracy and recall, with 97.4% precision and F1-score. AMSVM recorded 86.8% accuracy, 74.7% precision, 83.6% recall, and a 79% F1-score. This study improves IWQI classification, explores trade-offs between accuracy and class balance, and provides information on the effectiveness of class weighting, apportioned margins, and resampling techniques for the development of the ML model. The proposed models can facilitate the development of a low-cost IoT-based IWQI assessment system, supporting sustainable irrigation management to enhance agricultural productivity.