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<p>The progressive degradation of water quality in Lake Titicaca, driven primarily by untreated wastewater discharges, diffuse agricultural runoff, and urban expansion, poses increasing risks to ecosystem integrity and public health. This study presents a hybrid framework integrating multi-campaign in situ monitoring (2011–2024), descriptive spatial mapping via Inverse Distance Weighting (IDW), and machine-learning regression to model two policy-relevant indicators: chlorophyll-a (Chl-a) and dissolved oxygen (DO). Four algorithms were benchmarked under grouped tenfold cross-validation to reduce spatial leakage: Random Forest, XGBoost, LightGBM, and an artificial neural network (MLP). XGBoost achieved the best out-of-fold performance for both targets, reaching mean R<sup>2</sup> = 0.92 for Chl-a (testing MAPE = 10.89%) and mean R<sup>2</sup> = 0.96 for DO (testing RMSE = 0.12&#xa0;mg/L, MAE = 0.08&#xa0;mg/L, MAPE = 1.25%), with small Train–Test gaps indicating good generalization. Spatiotemporal mapping and distributional analyses consistently identified persistent eutrophication pressure in Puno Bay and the Minor Lake, expressed as recurrent elevated Chl-a and localized low-DO events. Overall, the proposed workflow provides a transparent and scalable decision-support tool for monitoring design and targeted management in high-altitude Andean lakes under increasing anthropogenic pressure.</p>

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Spatiotemporal analysis of water quality in Lake Titicaca using in situ physicochemical parameters and machine learning predictive models

  • 期刊论文

  • 发表日期：

    2026年5月18日

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  • Jose N. Mamani-Quispe,
  • Brandon D. Pinto-Mamani,
  • Bernarda Quispe-Alania,
  • Lucila Machaca-Hallasi,
  • Jairo J. Marquina-Araujo,
  • Marco A. Cotrina-Teatino

摘要

The progressive degradation of water quality in Lake Titicaca, driven primarily by untreated wastewater discharges, diffuse agricultural runoff, and urban expansion, poses increasing risks to ecosystem integrity and public health. This study presents a hybrid framework integrating multi-campaign in situ monitoring (2011–2024), descriptive spatial mapping via Inverse Distance Weighting (IDW), and machine-learning regression to model two policy-relevant indicators: chlorophyll-a (Chl-a) and dissolved oxygen (DO). Four algorithms were benchmarked under grouped tenfold cross-validation to reduce spatial leakage: Random Forest, XGBoost, LightGBM, and an artificial neural network (MLP). XGBoost achieved the best out-of-fold performance for both targets, reaching mean R2 = 0.92 for Chl-a (testing MAPE = 10.89%) and mean R2 = 0.96 for DO (testing RMSE = 0.12 mg/L, MAE = 0.08 mg/L, MAPE = 1.25%), with small Train–Test gaps indicating good generalization. Spatiotemporal mapping and distributional analyses consistently identified persistent eutrophication pressure in Puno Bay and the Minor Lake, expressed as recurrent elevated Chl-a and localized low-DO events. Overall, the proposed workflow provides a transparent and scalable decision-support tool for monitoring design and targeted management in high-altitude Andean lakes under increasing anthropogenic pressure.