Hybrid Stacking Model for Turbidity Prediction of a Lake Using Neural Networks and Gradient Boosting

ABSTRACT

Industrialization is growing as a threat to the quality of water in urban lakes. In this paper, a hybrid machine learning system (combining remote sensing [RS] data with a stacked ensemble model) is used to predict the turbidity of Saki Lake within the Patancheruvu industrial belt in Hyderabad. As a result of the nonlinear relationship between RS spectral characteristics and in situ turbidity (NTU), conventional models are ineffective. XGBR + NN + GBR as base learners with Ridge Regression as meta‐learner minimally decreased MAE to 3.06 NTU (compared to 5.11, 4.17, and 3.81 NTU as un‐minimized MAE of individual models). An additional error‐correction step following stacking with the XGBR was used to further decrease MAE to 2.18 NTU ( R ²  = 0.86). The two‐step framework shows that ensemble learning with specific fault correction can provide scaled water quality monitoring applicable to several contaminated lentic water bodies.