Carreres, DanielFernández-Blanco, EnriqueRivero, DanielRabuñal, Juan R.Anta, JoseGarcía Bermejo, Juan Tomás2025-02-062025-02-062024Carreres-Prieto, D., Fernandez-Blanco, E., Rivero, D. et al. Optimization of indirect wastewater characterization using led spectrophotometry: a comparative analysis of regression, scaling, and dimensionality reduction methods. Environ Sci Pollut Res 31, 54481–54501 (2024). https://doi.org/10.1007/s11356-024-34714-8http://hdl.handle.net/2183/41096Financiado para publicación en acceso aberto: CRUE/CSIC[Abstract:] LED spectrophotometry is a robust technique for the indirect characterization of wastewater pollutant load through correlation modeling. To tackle this issue, a dataset with 1300 samples was collected, from both raw and treated wastewater from 45 wastewater treatment plants in Spain and Chile collected over 4 years. The type of regressor, scaling, and dimensionality reduction technique and nature of the data play crucial roles in the performance of the processing pipeline. Eighty-four pipelines were tested through exhaustive experimentation resulting from the combination of 7 regression techniques, 3 scaling methods, and 4 possible dimensional reductions. Those combinations were tested on the prediction of chemical oxygen demand (COD) and total suspended solids (TSS). Each pipeline underwent a tenfold cross-validation on 15 sub-datasets derived from the original dataset, accounting for variations in plants and wastewater types. The results point to the normalization of the data followed by a conversion through the PCA to finally apply a Random Forest Regressor as the combination which stood out These results highlight the importance of modeling strategies in wastewater management using techniques such as LED spectrophotometry.engAtribuciónhttp://creativecommons.org/licenses/by/3.0/es/Wastewater characterizationLED spectrophotometerWastewater qualityComparison between characterization techniquesChemical Oxygen of DemandTotal suspended solidsjournal articleopen access10.1007/s11356-024-34714-8