A Comparative Study of Advanced Transformer Learning Frameworks for Water Potability Analysis Using Physicochemical Parameters
Keeping drinking water safe is a critical aspect of protecting public health. Traditional laboratory-based methods for evaluating water potability are often time-consuming, costly, and labour-intensive. This paper presents a comparative analysis of four transformer-based deep learning models in the...
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MDPI AG
2025-06-01
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| author | Enes Algül Saadin Oyucu Onur Polat Hüseyin Çelik Süleyman Ekşi Faruk Kurker Ahmet Aksoz |
| author_facet | Enes Algül Saadin Oyucu Onur Polat Hüseyin Çelik Süleyman Ekşi Faruk Kurker Ahmet Aksoz |
| author_sort | Enes Algül |
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| description | Keeping drinking water safe is a critical aspect of protecting public health. Traditional laboratory-based methods for evaluating water potability are often time-consuming, costly, and labour-intensive. This paper presents a comparative analysis of four transformer-based deep learning models in the development of automatic classification systems for water potability based on physicochemical attributes. The models examined include the enhanced tabular transformer (ETT), feature tokenizer transformer (FTTransformer), self-attention and inter-sample network (SAINT), and tabular autoencoder pretraining enhancement (TAPE). The study utilized an open-access water quality dataset that includes nine key attributes such as pH, hardness, total dissolved solids (TDS), chloramines, sulphate, conductivity, organic carbon, trihalomethanes, and turbidity. The models were evaluated under a unified protocol involving 70–15–15 data partitioning, five-fold cross-validation, fixed random seed, and consistent hyperparameter settings. Among the evaluated models, the enhanced tabular transformer outperforms other models with an accuracy of 95.04% and an F1 score of 0.94. ETT is an advanced model because it can efficiently model high-order feature interactions through multi-head attention and deep hierarchical encoding. Feature importance analysis consistently highlighted chloramines, conductivity, and trihalomethanes as key predictive features across all models. SAINT demonstrated robust generalization through its dual-attention mechanism, while TAPE provided competitive results with reduced computational overhead due to unsupervised pretraining. Conversely, FTTransformer showed limitations, likely due to sensitivity to class imbalance and hyperparameter tuning. The results underscore the potential of transformer-based models, especially ETT, in enabling efficient, accurate, and scalable water quality monitoring. These findings support their integration into real-time environmental health systems and suggest approaches for future research in explainability, domain adaptation, and multimodal fusion. |
| format | Article |
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| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-ce7fbdc2f1da494887375a290a8d84712025-08-20T03:28:36ZengMDPI AGApplied Sciences2076-34172025-06-011513726210.3390/app15137262A Comparative Study of Advanced Transformer Learning Frameworks for Water Potability Analysis Using Physicochemical ParametersEnes Algül0Saadin Oyucu1Onur Polat2Hüseyin Çelik3Süleyman Ekşi4Faruk Kurker5Ahmet Aksoz6Department of Computer Engineering, Bingöl University, Bingol 12000, TurkeyDepartment of Computer Engineering, Faculty of Technology, Gazi University, Ankara 06560, TürkiyeDepartment of Computer Engineering, Bingöl University, Bingol 12000, TurkeyManas Enerji Yonetimi Sanayi ve Ticaret A.S., Ankara 06935, TürkiyeManas Enerji Yonetimi Sanayi ve Ticaret A.S., Ankara 06935, TürkiyeDepartment of Electrical and Electronic Engineering, Faculty of Engineering, Adıyaman University, Adıyaman 02040, TürkiyeDepartment of Electrical and Electronics Engineering, Faculty of Engineering, Architecture and Design, Kayseri University, Kayseri 38380, TürkiyeKeeping drinking water safe is a critical aspect of protecting public health. Traditional laboratory-based methods for evaluating water potability are often time-consuming, costly, and labour-intensive. This paper presents a comparative analysis of four transformer-based deep learning models in the development of automatic classification systems for water potability based on physicochemical attributes. The models examined include the enhanced tabular transformer (ETT), feature tokenizer transformer (FTTransformer), self-attention and inter-sample network (SAINT), and tabular autoencoder pretraining enhancement (TAPE). The study utilized an open-access water quality dataset that includes nine key attributes such as pH, hardness, total dissolved solids (TDS), chloramines, sulphate, conductivity, organic carbon, trihalomethanes, and turbidity. The models were evaluated under a unified protocol involving 70–15–15 data partitioning, five-fold cross-validation, fixed random seed, and consistent hyperparameter settings. Among the evaluated models, the enhanced tabular transformer outperforms other models with an accuracy of 95.04% and an F1 score of 0.94. ETT is an advanced model because it can efficiently model high-order feature interactions through multi-head attention and deep hierarchical encoding. Feature importance analysis consistently highlighted chloramines, conductivity, and trihalomethanes as key predictive features across all models. SAINT demonstrated robust generalization through its dual-attention mechanism, while TAPE provided competitive results with reduced computational overhead due to unsupervised pretraining. Conversely, FTTransformer showed limitations, likely due to sensitivity to class imbalance and hyperparameter tuning. The results underscore the potential of transformer-based models, especially ETT, in enabling efficient, accurate, and scalable water quality monitoring. These findings support their integration into real-time environmental health systems and suggest approaches for future research in explainability, domain adaptation, and multimodal fusion.https://www.mdpi.com/2076-3417/15/13/7262water potabilitytransformer modelsdeep learningphysicochemical featurestabular data classificationattention mechanism |
| spellingShingle | Enes Algül Saadin Oyucu Onur Polat Hüseyin Çelik Süleyman Ekşi Faruk Kurker Ahmet Aksoz A Comparative Study of Advanced Transformer Learning Frameworks for Water Potability Analysis Using Physicochemical Parameters Applied Sciences water potability transformer models deep learning physicochemical features tabular data classification attention mechanism |
| title | A Comparative Study of Advanced Transformer Learning Frameworks for Water Potability Analysis Using Physicochemical Parameters |
| title_full | A Comparative Study of Advanced Transformer Learning Frameworks for Water Potability Analysis Using Physicochemical Parameters |
| title_fullStr | A Comparative Study of Advanced Transformer Learning Frameworks for Water Potability Analysis Using Physicochemical Parameters |
| title_full_unstemmed | A Comparative Study of Advanced Transformer Learning Frameworks for Water Potability Analysis Using Physicochemical Parameters |
| title_short | A Comparative Study of Advanced Transformer Learning Frameworks for Water Potability Analysis Using Physicochemical Parameters |
| title_sort | comparative study of advanced transformer learning frameworks for water potability analysis using physicochemical parameters |
| topic | water potability transformer models deep learning physicochemical features tabular data classification attention mechanism |
| url | https://www.mdpi.com/2076-3417/15/13/7262 |
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