Seasonal water quality index prediction in Guilin karst water system: an explainable machine learning approach.

Kun Dong, Yang Huang, Jiayu Yang, Haixiang Li, Sze-Mun Lam, Jin-Chung Sin, Dunqiu Wang

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In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

7 authors.

Kun DongCollege of Environmental Science and Engineering, Guilin University of Technology, Guilin, 541006, China.

Yang HuangCollege of Environmental Science and Engineering, Guilin University of Technology, Guilin, 541006, China.

Jiayu YangCollege of Environmental Science and Engineering, Guilin University of Technology, Guilin, 541006, China.

Haixiang LiCollege of Environmental Science and Engineering, Guilin University of Technology, Guilin, 541006, China.

Sze-Mun LamCollege of Environmental Science and Engineering, Guilin University of Technology, Guilin, 541006, China.

Jin-Chung SinCollege of Environmental Science and Engineering, Guilin University of Technology, Guilin, 541006, China.

Dunqiu WangCollege of Environmental Science and Engineering, Guilin University of Technology, Guilin, 541006, China. wangdunqiu@glut.edu.cn.

Funding

the Guangxi Key Research and Development Program GuikeAB24010118the Guangxi Natural Science Foundation 2025GXNSFDA069043the Natural Science Foundation of China 52260023

6 · The paper itself

Abstract

Accurate prediction of the water quality index (WQI) in karst water systems remains challenging due to pronounced seasonal hydrological heterogeneity, dual-porosity flow regimes, and complex multi-factor pollution mechanisms. This study developed a season-stratified, explainable machine-learning framework for the Guilin karst water system (Guangxi, China), based on 208 samples from 104 stations covering the wet (June to August) and dry (December to February) seasons. A sample-adaptive feature engineering step, followed by a three-stage selection pipeline (Pearson correlation filtering, variance inflation factor (VIF) screening, and Random Forest importance ranking), reduced 16 candidate features to ten predictors per season. Ten base learners (Ridge, Lasso, ElasticNet, support vector regression (SVR), random forest (RF), gradient boosting (GBM), eXtreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), categorical boosting (CatBoost) and adaptive boosting (AdaBoost)) were trained separately for each season, and the top three per season were stacked through a regularised Ridge meta-learner. XGBoost performed best in the wet season (R

Indexed as

Explainable machine learningKarst water systemsRegression receiver operating characteristicSeasonal differentiationSHapley additive exPlanationsWater quality index

Identifiers

PMID42321306

DOI10.1038/s41598-026-55212-6

PMCPMC13550486

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.