Karakaya, NusretEvrendilek, FatihGüngör, KeremÖnal, Deniz2021-06-232021-06-2320131863-06501863-0669https://doi.org/10.1002/clen.201200683https://hdl.handle.net/20.500.12491/7449Human-induced and natural interruptions with continuous streams of observational data necessitate the development of gap-filling and prediction strategies towards better understanding, monitoring and management of aquatic systems. This study quantified the efficacy of multiple non-linear regression (MNLR) versus artificial neural network (ANN) models as well as the temporal partitioning of diurnal versus nocturnal data for the predictions of chlorophyll-a (chl-a) and dissolved oxygen (DO) dynamics. The temporal partitioning increased the predictive performances of the best MNLR models of diurnal DO by 45% and nocturnal DO by 4%, relative to the best diel MNLR model of diel DO (r(adj)(2) = 68.8%). The ANN-based predictions had a higher predictive power than the MNLR-based predictions for both chl-a and DO except for diurnal DO dynamics. The best ANNs based on independent validations were multilayer perceptron (MLP) for diel chl-a, generalized feedforward (GFF) for diurnal and nocturnal chl-a, MLP for diel DO, GFF for diurnal DO, and MLP for nocturnal DO.eninfo:eu-repo/semantics/closedAccessData-driven ModelingLake Water QualityNon-linear DynamicsTime Series DataArticle10.1002/clen.2012006834198728772-s2.0-84883445093Q3WOS:000327816200006Q2