A novel framework to predict water turbidity using Bayesian modeling. (1st September 2021) - Huang, Jiacong; Qian, Rui; Gao, Junfeng; Bing, Haijian; Huang, Qi; Qi, Lingyan; Song, Song; Huang, Jiafang
Highlights: A new framework was developed to predict water turbidity via smartphone images. The framework had the novel ability of uncertainty estimation and model update . Bayesian model in the framework is robust ( p <0.001) in predicting water turbidity. More measured data into the Bayesian model can reduce uncertainty in estimating water turbidity. Abstract: High water turbidity in aquatic ecosystems is a global challenge due to its harmful impacts. A cost-effective manner to rapidly and accurately measure water turbidity is thus of particular useful in water management with limited resources. This study developed a novel framework aiming to predict water turbidity in various aquatic ecosystems. The framework predicted water turbidity and quantified the uncertainty of the prediction through Bayesian modeling. To improve model performance, a model-update method was implemented in the framework to update the model structure and parameters once more measured data were available. 120 paired records (an image from smartphone and a measured water turbidity value by standard turbidimeters for each record) were collected from rivers, lakes and ponds across China to evaluate the performance of the developed framework. Our cross-validation results revealed a well prediction of water turbidity with Nash-Sutcliffe efficiency ( NS ) >0.87 ( p <0.001) during the training period and NS >0.73 ( p <0.001) during the validation period. The model-update method (in case of more measured data) for the developed Bayesian models in the framework resulted in a decreasing trend of model uncertainty and a stable mode fit. This study demonstrated a high value of the Bayesian-based framework in predicting water turbidity in a robust and easy manner.