{"id":25733,"date":"2026-09-03T11:59:14","date_gmt":"2026-09-03T11:59:14","guid":{"rendered":"https:\/\/scientificassociation.org\/?post_type=journal-paper&#038;p=25733"},"modified":"2026-09-03T11:59:14","modified_gmt":"2026-09-03T11:59:14","slug":"evaluating-water-potability-a-comparative-analysis-of-machine-and-deep-learning-models-for-binary-classification","status":"publish","type":"journal-paper","link":"https:\/\/scientificassociation.org\/ar\/journal-paper\/evaluating-water-potability-a-comparative-analysis-of-machine-and-deep-learning-models-for-binary-classification\/","title":{"rendered":"Evaluating water potability: a comparative analysis of machine and deep learning models for binary classification"},"content":{"rendered":"<div class=\"padding_abstract justify ltr\">Ensuring the safety of drinking water remains a global public health priority, which requires the development of robust data-driven predictive tools to augment traditional laboratory test-ing. This study presents a rigorous comparative evaluation of various Machine Learning (ML) and Deep Learning (DL) architectures to facilitate the binary classification of water potability. Using a comprehensive, publicly available open-source benchmark data set comprising 3,276 unique water quality profiles, the research methodology employed standardized preprocessing pipelines and stratified 5-fold cross-validation to ensure model generalizability and robustness. Our quantitative results demonstrate that non-linear models significantly outperform linear baselines, with the Support Vector Machine (SVM) utilizing a Radial Basis Function (RBF) kernel emerging as the top performer, achieving an accuracy of 0.6713 and an Area Under the Curve (AUC) of 0.6661. However, the analysis uncovers a critical \u201cperformance ceiling\u201d inherent in the current feature set. All models exhibited a systemic bias characterized by high Preci-sion but notably low Recall (peaking at 0.3490), driven by significant class imbalance and weak feature-to-target separation within the raw chemical parameters. To resolve this, future deploy-ments must prioritize imbalance mitigation strategies, such as Synthetic Minority Over-sampling Technique (SMOTE), and advanced feature engineering. Incorporating these recommendations will allow water management authorities to transition from reactive testing to real-time preven-tative risk mitigation, ultimately safeguarding public health by reliably identifying hazardous water sources prior to distribution.<\/div>\n","protected":false},"featured_media":25688,"template":"","meta":{"_acf_changed":false},"journal-name":[219],"paper-tag":[274],"class_list":["post-25733","journal-paper","type-journal-paper","status-publish","has-post-thumbnail","hentry","journal-name-jcese","paper-tag--5--4--2026"],"acf":[],"_links":{"self":[{"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/journal-paper\/25733","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/journal-paper"}],"about":[{"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/types\/journal-paper"}],"wp:attachment":[{"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/media?parent=25733"}],"wp:term":[{"taxonomy":"journal-name","embeddable":true,"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/journal-name?post=25733"},{"taxonomy":"paper-tag","embeddable":true,"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/paper-tag?post=25733"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}