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Predicting BOD5 Removal Efficiency in a Constructed Wetland from Satellite and Meteorological Data Using Interpretable Machine Learning

Atila BezdanDepartment of Water Management, Faculty of Agriculture, University of Novi Sad, 21000 Novi Sad, SerbiaViola SomogyiInstitute of Environmental Engineering, University of Pannonia, 8200 Veszprém, HungaryJasna GrabićDepartment of Water Management, Faculty of Agriculture, University of Novi Sad, 21000 Novi Sad, SerbiaMiško MilanovićFaculty of Geography, University of Belgrade, 11000 Belgrade, SerbiaNikola StankovićFaculty of Geography, University of Belgrade, 11000 Belgrade, SerbiaÖner ÇETİNFaculty of Agriculture, Dicle University, Diyarbakır 21280, TürkiyeNodirbek SarmonovFaculty of Geography, Karshi State Technical University, Karshi 180100, UzbekistanJovana BezdanDepartment of Water Management, Faculty of Agriculture, University of Novi Sad, 21000 Novi Sad, Serbia

2026en

ABI

Annotatsiya

Constructed wetlands (CWs) offer a low-cost and sustainable option for wastewater treatment, but their performance is commonly assessed through infrequent, periodic field sampling that yields temporally sparse records. This study evaluated whether five-day biochemical oxygen demand (BOD5) removal efficiency at the Gložan horizontal subsurface-flow constructed wetland (Serbia) could be predicted from freely available satellite-derived and meteorological variables alone, using an interpretable machine learning workflow. Sixteen candidate predictors—the mean and spatial standard deviation of Landsat-derived indices (land surface temperature [LST], Normalized Difference Vegetation Index [NDVI], Normalized Difference Water Index [NDWI], Normalized Difference Suspended Sediment Index [NDSSI], Modified NDWI [MNDWI], and chlorophyll index) together with 7- and 14-day air temperature and precipitation—were screened against 38 BOD5 removal-efficiency observations (n = 38; 2005–2026) using Pearson correlation and three Random Forest importance measures, refined through variance-inflation-factor analysis and regularization-guided elimination, and evaluated across twelve regression algorithms under nested leave-one-out cross-validation (LOOCV). A one-component Partial Least Squares (PLS) regression using four predictors—14-day mean air temperature, spatial variability of land surface temperature (LST), and the mean and spatial variability of the Normalized Difference Water Index (NDWI)—achieved the best performance in the external (outer-loop LOOCV) evaluation (RMSE = 5.73 percentage points, MAE = 4.37 percentage points, R2 = 0.41). Linear-family models consistently outperformed tree-ensemble and kernel-based methods, and the explicit removal of multicollinearity during predictor selection was key to this advantage, allowing the linear models to surpass their nonlinear counterparts. These results indicate that a compact set of satellite-derived and meteorological variables can provide meaningful and interpretable information on CW treatment performance without in situ operational data, offering a complement to—rather than a replacement for—traditional physicochemical analyses in the monitoring of constructed wetlands.

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DOI: 10.3390/earth7050155

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