Treffer: Implementing machine learning methods for in-depth analysis and classification of surface water quality in Central Java.
Title:
Implementing machine learning methods for in-depth analysis and classification of surface water quality in Central Java.
Authors:
Perdana VCP; Diponegoro University: Universitas Diponegoro, Semarang, Central Java, Indonesia., Suherman S; Diponegoro University: Universitas Diponegoro, Semarang, Central Java, Indonesia., Purba DGD; Universitas Sumatera Utara, Medan, North Sumatra, Indonesia., Sinuhaji TRF; Diponegoro University: Universitas Diponegoro, Semarang, Central Java, Indonesia. eaglefansrayyan@gmail.com., Wijaya AK; Bina Nusantara University, West Jakarta, DKI Jakarta, Indonesia.
Source:
Environmental science and pollution research international [Environ Sci Pollut Res Int] 2025 Sep; Vol. 32 (44), pp. 25319-25338. Date of Electronic Publication: 2025 Nov 03.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Springer Country of Publication: Germany NLM ID: 9441769 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1614-7499 (Electronic) Linking ISSN: 09441344 NLM ISO Abbreviation: Environ Sci Pollut Res Int Subsets: MEDLINE
Imprint Name(s):
Publication: <2013->: Berlin : Springer
Original Publication: Landsberg, Germany : Ecomed
Original Publication: Landsberg, Germany : Ecomed
MeSH Terms:
References:
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Akhlaq M, Ellahi A, Niaz R, Khan M, Sammen SSh, Scholz M (2024) Comparative analysis of machine learning algorithms for water quality prediction. Tellus A Dyn Meteorol Oceanogr 76:177–192. https://doi.org/10.16993/tellusa.4069. (PMID: 10.16993/tellusa.4069)
Al-Ali IA, Al-Dabbas MA (2022) Assessment of some organic and inorganic pollution Indices / Euphrates River/ Iraq. Int J Health Sci (Qassim) 12395–12417. https://doi.org/10.53730/ijhs.v6nS3.9484.
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Alshamrani SS (2025) Machine learning techniques improving the Box-Cox transformation in breast cancer prediction. Electronics 14:3173. https://doi.org/10.3390/electronics14163173. (PMID: 10.3390/electronics14163173)
Baron G, Stańczyk U (2021) Standard vs. non-standard cross-validation: evaluation of performance in a space with structured distribution of datapoints. Procedia Comput Sci 192:1245–1254. https://doi.org/10.1016/j.procs.2021.08.128. (PMID: 10.1016/j.procs.2021.08.128)
Biswas S, Grundlingh N, Boardman J, White J, Le L (2025) A target permutation test for statistical significance of feature importance in differentiable models. Electronics (Basel) 14:571. https://doi.org/10.3390/electronics14030571. (PMID: 10.3390/electronics14030571)
Blum L, Elgendi M, Menon C (2022) Impact of Box-Cox transformation on machine-learning algorithms. Front Artif Intell. https://doi.org/10.3389/frai.2022.877569. (PMID: 10.3389/frai.2022.877569)
Catajan Jr AL, Fajardo AC, Limbago JS (2023) Classification of water quality index in laguna de bay using XGBoost. In: 2023 20th International joint conference on computer science and software engineering (JCSSE). IEEE, pp 403–408. https://doi.org/10.1109/JCSSE58229.2023.10202029.
Chen Y, Zhang X, Karimian H, Xiao G, Huang J (2021) A novel framework for prediction of dam deformation based on extreme learning machine and Lévy flight bat algorithm. J Hydroinformatics. https://doi.org/10.2166/hydro.2021.178. (PMID: 10.2166/hydro.2021.178)
Chowdhury MdDA, Billah T, Rahman MdR, Bakri MK Bin, Barua S, Morshed AJM, Uddin E, Uddin MM (2024) Evaluation of water quality indexes and heavy metal pollution indexes of different industrial effluents and Karnaphuli river water in Chattogram, Bangladesh. Environ Qual Manag 34. https://doi.org/10.1002/tqem.22290.
Dehdarirad T (2025) Evaluating explainability in language classification models: a unified framework incorporating feature attribution methods and key factors affecting faithfulness. Data Inf Manag 100101. https://doi.org/10.1016/j.dim.2025.100101.
Demircioğlu A (2024) Applying oversampling before cross-validation will lead to high bias in radiomics. Sci Rep 14:11563. https://doi.org/10.1038/s41598-024-62585-z. (PMID: 10.1038/s41598-024-62585-z)
Dritsas E, Trigka M (2023) Efficient data-driven machine learning models for water quality prediction. Computation 11:16. https://doi.org/10.3390/computation11020016. (PMID: 10.3390/computation11020016)
du Plessis A (2022) Persistent degradation: global water quality challenges and required actions. One Earth 5:129–131. https://doi.org/10.1016/j.oneear.2022.01.005. (PMID: 10.1016/j.oneear.2022.01.005)
Elhassan TA, Mahmoud A, Futwan A-M, Mohamed S (2016) Classification of imbalance data using Tomek Link (T-Link) combined with random under-sampling (RUS) as a data reduction method. Glob J Technol Optim 01. https://doi.org/10.4172/2229-8711.s1111.
Elmotawakkil A, Enneya N, Bhagat SK, Ouda MM, Kumar V (2025) Advanced machine learning models for robust prediction of water quality index and classification. J Hydroinformatics 27:299–319. https://doi.org/10.2166/hydro.2025.290. (PMID: 10.2166/hydro.2025.290)
Elreedy D, Atiya AF, Kamalov F (2024) A theoretical distribution analysis of synthetic minority oversampling technique (SMOTE) for imbalanced learning. Mach Learn 113:4903–4923. https://doi.org/10.1007/s10994-022-06296-4. (PMID: 10.1007/s10994-022-06296-4)
Farhadpour S, Warner TA, Maxwell AE (2024) Selecting and interpreting multiclass loss and accuracy assessment metrics for classifications with class imbalance: guidance and best practices. Remote Sens (Basel) 16:533. https://doi.org/10.3390/rs16030533. (PMID: 10.3390/rs16030533)
Gajjalavari S, Rudravaram VV (2022) Multi-class classification using mixtures of univariate and multivariate ROC curves. J biostat epidemiol. https://doi.org/10.18502/jbe.v8i2.10418.
Hamzah FB, Mohamad Hamzah F, Mohd Razali SF, El-Shafie A (2022) Multiple imputations by chained equations for recovering missing daily streamflow observations: a case study of Langat River basin in Malaysia. Hydrol Sci J. https://doi.org/10.1080/02626667.2021.2001471. (PMID: 10.1080/02626667.2021.2001471)
Imran M, Zhang D, Zaman M, Parveen S, Mishu NEJ (2025) Water quality classification in terms of WQI using machine learning algorithms in Keenjhar Lake, Pakistan. In: The 8th International electronic conference on water sciences. MDPI, Basel Switzerland p 13. https://doi.org/10.3390/eesp2025032013.
Jafarigol E, Trafalis T (2023) A review of machine learning techniques in imbalanced data and future trends. arXiv preprint arXiv:231007917. https://doi.org/10.48550/arXiv.2310.07917.
Jarvie HP, Worrall F, Burt TP, Howden NJK (2025) A 150-year river water quality record shows reductions in phosphorus loads but not in algal growth potential. Commun Earth Environ 6:62. https://doi.org/10.1038/s43247-024-01978-4. (PMID: 10.1038/s43247-024-01978-4)
Karthick K, Krishnan S, Manikandan R (2024) Water quality prediction: a data-driven approach exploiting advanced machine learning algorithms with data augmentation. J Water Clim Change 15:431–452. https://doi.org/10.2166/wcc.2023.403. (PMID: 10.2166/wcc.2023.403)
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Grant Information:
No. 894.1/04951 Kepala Badan Kepegawaian Daerah Provinsi Jawa Tengah
Contributed Indexing:
Keywords: Environmental regulation; Machine learning; Support vector machine; Water quality classification; XGBoost
Entry Date(s):
Date Created: 20251103 Date Completed: 20251125 Latest Revision: 20251125
Update Code:
20251126
DOI:
10.1007/s11356-025-37040-9
PMID:
41182597
Database:
MEDLINE
Weitere Informationen
Water quality monitoring plays a critical role in environmental protection and public health, particularly in the context of growing ecological challenges and the need for sustainable resource management. This study proposes and evaluates a predictive classification framework for assessing water pollution levels using machine learning techniques-support vector machine (SVM) and extreme gradient boosting (XGBoost)-within the Pollution Mitigation Classification (PMC) scheme. The models were optimized using the Synthetic Minority Over-sampling Technique (SMOTE-Tomek) resampling technique to address data imbalance. XGBoost demonstrated superior performance with an accuracy of 98.76% and an F1-Macro score of 97.62%, while SVM achieved an accuracy of 90.25% and an F1-Macro score of 83.57%. Interpretability analyses via SHAP and LIME revealed that biological and chemical indicators, such as fecal coliform, BOD, and COD, had the highest feature importance. Validation using dummy features confirmed that both models learned meaningful patterns rather than fitting to noise or spurious correlations. Beyond statistical accuracy, this research integrates a regulatory compliance validation against Indonesia's Government Regulation No. 22/2021 (Class II water quality standards). Findings indicate that several predictions labeled as "Safe" by the models violated one or more legal thresholds, raising concerns over potential false-safe classifications. To mitigate this risk, the study proposes the implementation of a regulatory-aware layer, comprising rule-based validation modules, probabilistic calibration methods (e.g., Platt Scaling), and early warning systems to enhance real-world applicability. The proposed framework underscores the importance of harmonizing predictive performance with legal compliance, offering a scalable, interpretable, and policy-aligned solution for AI-driven environmental monitoring systems.
(© 2025. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)
Declarations. Ethical approval: This research has confirmed that no ethical approval is required. Consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: Suherman Suherman reports that administrative support was provided by Diponegoro University. Valentine Conny Putri Perdana reports a relationship with Dinas Lingkungan Hidup dan Kehutanan Provinsi Jawa Tengah (The Environmental and Forestry Agency of Central Java Province) that includes employment.