ArticleScientific reports2025
Automating wastewater characteristic parameter quantitation using neural architecture search in AutoML systems on spectral reflectance data.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Wastewater (WW) analyses are required to establish optimal operation of treatment facilities, and rapid treatment assessment can greatly improve operational efficiency. WWTPs function through complex biochemical processes that exhibit high variability and are difficult to predict. This work presents the first in-depth study towards an original application of Auto Machine Learning (AutoML) models to accurately predict quality parameters of wastewater. This study aims to demonstrate the efficiency of neural network (NN) regression models built by applying the Neural Architectural Space (NAS) applying different search algorithms such as random search, grid search, Bayesian Optimization, and Hyperband search to predict the concentrations of various wastewater (WW) parameters such as biochemical oxygen demand (BOD), chemical oxygen demand (COD), ammonia (NH
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