The compiled 8-year dataset (2012-2019) consisting of weekly river water quality indicators (CODMn, DO, NH3-N and PH ) in majors 10 sub-basin of Yangtze river based on imputation of machine learning

Water quality is significantly affected by global climate change and human activities, with diverse critical factors shaping its state in rivers and lakes. In the study, we utilized four indicators to characterize water quality: the physical water quality parameters included dissolved oxygen (DO, mg/L) and PH, while the chemical water quality parameters encompassed chemical oxygen demand (CODMn, mg/L) and ammonia nitrogen (NH3-N, mg/L). This study establishes weekly water quality models for typical 10 sub-basins along the Yangtze River using machine learning methods, which incorporate the impacts of hydro-meteorological and anthropogenic factors.These 10 sub-basins represent the principal tributaries of the Yangtze River basin and include Dongting Lake, the upper Han River, the lower Han River, the Jialing River, the Jinsha River, the Li River, the Min River, Poyang Lake, the Xiang River, and the Yuan River. This data collection was performed by National Environmental Monitoring Centre (http://www.cnemc.cn/sssj/szzdjczb/index_1.shtml). The water quality indicators discussed in this study are assessed in accordance with the national standard GB 3838-2002. Please refer to the paper for details.