首页|Recent Studies from North China Institute of Aerospace Engineering Add New Data to Machine Learning (Monitoring water quality parameters of freshwater aquacultu re ponds using UAV-based multispectral images)
Recent Studies from North China Institute of Aerospace Engineering Add New Data to Machine Learning (Monitoring water quality parameters of freshwater aquacultu re ponds using UAV-based multispectral images)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on artificial intelligence is now available. According to news originating from Hebei, People's Republic o f China, by NewsRx correspondents, research stated, "Monitoring water quality is crucial for water exchange, precise feeding, and quality control of water produ cts in freshwater aquaculture. In light of the issue of spatial heterogeneity in freshwater aquaculture pond waters and the constraints of conventional sensor d etection techniques and traditional machine learning models." The news editors obtained a quote from the research from North China Institute o f Aerospace Engineering: "In this study, UAV multispectral images were combined with four machine learning algorithms (Ridge, XGBoost, CatBoost, RF) and the Sta cking model to model the estimation of Chlorophyll a (Chla) and Turbidity and m ap their spatial distribution. The findings indicate that, in contrast to machin e learning models, the Stacking model of water quality parameter performs better with higher accuracy. Meanwhile,for Chl-a and Turbidity the optimal sub-model c ombination in the Stacking model varies, with the most effective estimation mode l for Chl-a concentration identified as RF-XGB-Ridge (R2 = 0.84, RMSE=1.882 g/L, MAE=3.433 g/L and Slope = 0.791). As to Turbidity, the RF-CAB-Ridge model demon strates superior performance, with macro-averaged precision (macro-p) of 93.3 %,macro-averaged recall (macro-R) of 88.8 %, macro-averaged F1-scor e (macro-F1) of 0.895, and Kappa coefficient of 0.813. Furthermore, the results of the joint analyses, which included measured samples and management measures a t the test site, demonstrated that the spatial distribution maps of Chl-a and Tu rbidity were in alignment with the current status of water quality at the test s ite. This consistency was observed across both temporal and spatial scales."
North China Institute of Aerospace Engin eeringHebeiPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
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