Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Deciphering Eutrophication-Limiting Nutrients in Lakes: A Multiscale Analysis of Chinese Waters
https://orcid.org/0000-0001-6537-2145
The total nitrogen/total phosphorus (TN/TP) ratio is considered a valuable indicator for evaluating the abundance of phytoplankton and the eutrophic condition of a water body, but its effectiveness as an indicator of eutrophication at different watershed scales has not been fully explored. In this study, we collected data from 103 lakes within four major watersheds in China and utilized the machine learning models eXtreme Gradient Boosting (XGBoost) and k-nearest neighbors (KNN) to predict the TN/TP ratio at three different scales. We identified notable disparities in the TN/TP ratio, chlorophyll a concentration, and algal cell density across the three scales. By incorporating time as an input variable, we were able to capture temporal trends in TN/TP ratio, which enhanced the predictive accuracy and fit of the machine learning models. The optimization ratios of the model indicators’ coefficient of determination, root-mean-square error, and mean absolute percentage error at three scales are 35.71 ± 25.26%, 0.43 ± 0.17%, and 1.47 ± 1.19%, respectively. XGBoost demonstrated a higher accuracy and better fit than KNN. Our results reveal the substantial impact of the watershed scale on predicting eutrophication-limiting nutrients of water bodies.
The effectiveness of TN/TP as eutrophication indicators across watershed scales remains underexplored. This study reports that watershed scale significantly impacts nutrient prediction for eutrophication and with weak relationships between TN/TP, chlorophyll a, and algal cell density.
Deciphering Eutrophication-Limiting Nutrients in Lakes: A Multiscale Analysis of Chinese Waters
https://orcid.org/0000-0001-6537-2145
The total nitrogen/total phosphorus (TN/TP) ratio is considered a valuable indicator for evaluating the abundance of phytoplankton and the eutrophic condition of a water body, but its effectiveness as an indicator of eutrophication at different watershed scales has not been fully explored. In this study, we collected data from 103 lakes within four major watersheds in China and utilized the machine learning models eXtreme Gradient Boosting (XGBoost) and k-nearest neighbors (KNN) to predict the TN/TP ratio at three different scales. We identified notable disparities in the TN/TP ratio, chlorophyll a concentration, and algal cell density across the three scales. By incorporating time as an input variable, we were able to capture temporal trends in TN/TP ratio, which enhanced the predictive accuracy and fit of the machine learning models. The optimization ratios of the model indicators’ coefficient of determination, root-mean-square error, and mean absolute percentage error at three scales are 35.71 ± 25.26%, 0.43 ± 0.17%, and 1.47 ± 1.19%, respectively. XGBoost demonstrated a higher accuracy and better fit than KNN. Our results reveal the substantial impact of the watershed scale on predicting eutrophication-limiting nutrients of water bodies.
The effectiveness of TN/TP as eutrophication indicators across watershed scales remains underexplored. This study reports that watershed scale significantly impacts nutrient prediction for eutrophication and with weak relationships between TN/TP, chlorophyll a, and algal cell density.
Deciphering Eutrophication-Limiting Nutrients in Lakes: A Multiscale Analysis of Chinese Waters
https://orcid.org/0000-0001-6537-2145
The total nitrogen/total phosphorus (TN/TP) ratio is considered a valuable indicator for evaluating the abundance of phytoplankton and the eutrophic condition of a water body, but its effectiveness as an indicator of eutrophication at different watershed scales has not been fully explored. In this study, we collected data from 103 lakes within four major watersheds in China and utilized the machine learning models eXtreme Gradient Boosting (XGBoost) and k-nearest neighbors (KNN) to predict the TN/TP ratio at three different scales. We identified notable disparities in the TN/TP ratio, chlorophyll a concentration, and algal cell density across the three scales. By incorporating time as an input variable, we were able to capture temporal trends in TN/TP ratio, which enhanced the predictive accuracy and fit of the machine learning models. The optimization ratios of the model indicators’ coefficient of determination, root-mean-square error, and mean absolute percentage error at three scales are 35.71 ± 25.26%, 0.43 ± 0.17%, and 1.47 ± 1.19%, respectively. XGBoost demonstrated a higher accuracy and better fit than KNN. Our results reveal the substantial impact of the watershed scale on predicting eutrophication-limiting nutrients of water bodies.
The effectiveness of TN/TP as eutrophication indicators across watershed scales remains underexplored. This study reports that watershed scale significantly impacts nutrient prediction for eutrophication and with weak relationships between TN/TP, chlorophyll a, and algal cell density.
Deciphering Eutrophication-Limiting Nutrients in Lakes: A Multiscale Analysis of Chinese Waters
Fang, Yong (Autor:in) / Huang, Ruting (Autor:in) / Shi, Xian-yang (Autor:in)
ACS ES&T Water ; 4 ; 2995-3006
12.07.2024
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
scales , watersheds , TN/TP , prediction , machine learning
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