ArticleEnvironmental science and pollution research international2017
Water quality trend and change-point analyses using integration of locally weighted polynomial regression and segmented regression.
Article in Environmental science and pollution research international, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A seasonally explicit counterfactual framework reveals incomplete turbidity recovery in a tropical river seven years after a major mine-tailings disaster.Environmental monitoring and assessment · 2026Article
- Fault detection for Li-ion batteries of electric vehicles with segmented regression method.Scientific reports · 2024Article
- Environmental impact of Karkheh Dam in the southern part of Iran on groundwater quality by intervention and trend analysis.Environmental monitoring and assessment · 2020Article
- Assessment of Long-Term Watershed Management on Reservoir Phosphorus Concentrations and Export Fluxes.International journal of environmental research and public health · 2018Article
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Authors and funding
8 authors.
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Abstract
Trend and change-point analyses of water quality time series data have important implications for pollution control and environmental decision-making. This paper developed a new approach to assess trends and change-points of water quality parameters by integrating locally weighted polynomial regression (LWPR) and segmented regression (SegReg). Firstly, LWPR was used to pretreat the original water quality data into a smoothed time series to represent the long-term trend of water quality. Then, SegReg was used to identify the long-term trends and change-points of the smoothed time series. Finally, statistical tests were applied to determine the significance of the long-term trends and change-points. The efficacy of this approach was validated using a 10-year record of total nitrogen (TN) and chemical oxygen demand (COD
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