Remotely sensed estimates of long-term biochemical oxygen demand over Hong Kong marine waters using machine learning enhanced by imbalanced label optimisation
2024; Elsevier BV; Volume: 943; Linguagem: Inglês
10.1016/j.scitotenv.2024.173748
ISSN1879-1026
AutoresYadong Zhou, Boayin He, Xiaoyu Cao, Yu Xiao, Qi Feng, Fan Yang, Fei Xiao, Xueer Geng, Yun Du,
Tópico(s)Air Quality Monitoring and Forecasting
ResumoIn many coastal cities around the world, continuing water degradation threatens the living environment of humans and aquatic organisms. To assess and control the water pollution situation, this study estimated the Biochemical Oxygen Demand (BOD) concentration of Hong Kong's marine waters using remote sensing and an improved machine learning (ML) method. The scheme was derived from four ML algorithms (RBF, SVR, RF, XGB) and calibrated using a large amount (N > 1000) of in-situ BOD
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