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<p>Due to the large variety of water contaminants, the accurate classification of water quality necessitates extensive chemical analyses and consideration of multi-contaminant effects on water potability. In this perspective, we propose a ResNet-based deep learning model with three-channel feature augmentation that is designed to improve the potability assessment. Conventional chemical and physical analyses produce only a limited number of features for determining the potability. Evaluating them solely based on their standard threshold values may not be sufficient for accurate classification because feature interactions -both linear and nonlinear- can play a role in the potability. To address this, the features are transformed into Gramian-inspired matrices by using Sum, Product and Subtraction operations of features, which are called Gramian Operation Fields (GOF), and this spatial augmentation can represent inter-feature dependencies within the spatial domain. This enables effective processing and capturing relationships among these dependencies within channels of two-dimensional CNN models and enhances the representation of linear and nonlinear inter-feature relationships. To demonstrate the effectiveness of the proposed method, we conducted numerical experiments on the publicly available Water Potability dataset. Experimental results indicated that the ResNet-18 model with 3-channel feature matrices achieved higher performance compared to other machine learning methods.</p>

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Water Potability Classification by Using Deep Residual Networks with Sum-Product-Difference Feature Matrices

  • 期刊论文

  • 发表日期：

    2026年7月23日

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  • Sevilay Tufenkci,
  • Baris Baykant Alagoz

摘要

Due to the large variety of water contaminants, the accurate classification of water quality necessitates extensive chemical analyses and consideration of multi-contaminant effects on water potability. In this perspective, we propose a ResNet-based deep learning model with three-channel feature augmentation that is designed to improve the potability assessment. Conventional chemical and physical analyses produce only a limited number of features for determining the potability. Evaluating them solely based on their standard threshold values may not be sufficient for accurate classification because feature interactions -both linear and nonlinear- can play a role in the potability. To address this, the features are transformed into Gramian-inspired matrices by using Sum, Product and Subtraction operations of features, which are called Gramian Operation Fields (GOF), and this spatial augmentation can represent inter-feature dependencies within the spatial domain. This enables effective processing and capturing relationships among these dependencies within channels of two-dimensional CNN models and enhances the representation of linear and nonlinear inter-feature relationships. To demonstrate the effectiveness of the proposed method, we conducted numerical experiments on the publicly available Water Potability dataset. Experimental results indicated that the ResNet-18 model with 3-channel feature matrices achieved higher performance compared to other machine learning methods.