A Multi-Stage Data-Driven Approach for Identifying Lake Stratification and Mixing Regimes by Integration of Machine Learning and Physical Indicators

• Interpretable ML framework detects shallow-lake stratification and mixing regimes. • Data-driven ΔT threshold for regime separation is physically validated using Ri scaling. • Off-the-shelf ML models classify regimes from standard high-frequency sensor data. • SHAP analysis reveals key hydrometeorological drivers of regime transitions. • Pipeline is reusable and implementable in real-time shallow-lake monitoring networks. Thermal stratification and mixing control vertical heat, oxygen, and nutrient exchange in lakes, shaping ecological functioning, biogeochemical processes, and ecosystem resilience. Operational detection of these regime shifts in shallow lakes remains challenging, as many existing methods rely on fixed, site-independent thresholds or computationally demanding hydrodynamic models that are difficult to deploy for routine monitoring. Here, we introduce and test a multi-stage, data-driven framework that combines unsupervised clustering, supervised classification, and simple physical diagnostics to identify mixing-stratification regimes in a small, shallow lake. Using one year of high-frequency measurements from Lake Eymir (Türkiye), k-means clustering of vertical temperature differences yields an empirical stratification threshold of approximately 2.4°C between 0.25 m and 4 m, which is consistent with reported criteria for shallow lakes and with gradient Richardson number scaling for this site. The resulting mixed and stratified labels are then used to train supervised models (XGBoost, K-nearest neighbours, Gradient Boosting, Decision Tree), with XGBoost achieving a classification accuracy of about 95% when dissolved oxygen is included as a predictor and around 90% when it is excluded. SHAP analysis indicates that dissolved-oxygen differences act as a strong proxy for the stratified state, while irradiation and surface temperature emerge as the main physical predictors once outcome variables are removed. The framework therefore offers a physically interpretable, computationally efficient way to derive site-specific temperature thresholds and to track stratification–mixing regimes from standard monitoring data, supporting applications such as near-real-time lake phase tracking and early warning for stratification-driven water-quality events in shallow, rapidly responding systems.