Water Potability Classification by Using Deep Residual Networks with Sum-Product-Difference Feature Matrices
Water Resources Management, cilt.40, sa.10, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 40 Sayı: 10
- Basım Tarihi: 2026
- Doi Numarası: 10.1007/s11269-026-04853-3
- Dergi Adı: Water Resources Management
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, CAB Abstracts, Compendex, Environment Index, Geobase, INSPEC, Natural Science Collection (ProQuest), Biological Science Database (ProQuest), Earth, Atmospheric, & Aquatic Science Collection (ProQuest), Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest)
- Anahtar Kelimeler: Classification, Deep learning, Potability, Residual neural networks, Water quality
- İnönü Üniversitesi Adresli: Evet
Özet
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.