首页|New Machine Learning Research Has Been Reported by a Researcher at Zhejiang Ocea n University (Water Quality in the Ma’an Archipelago Marine Special Protected Ar ea: Remote Sensing Inversion Based on Machine Learning)
New Machine Learning Research Has Been Reported by a Researcher at Zhejiang Ocea n University (Water Quality in the Ma’an Archipelago Marine Special Protected Ar ea: Remote Sensing Inversion Based on Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on artificial intelligence are presented in a new report. According to news originating from Zhoushan, Peop le’s Republic of China, by NewsRx correspondents, research stated, “Due to the i ncreasing impact of climate change and human activities on marine ecosystems, th ere is an urgent need to study marine water quality. The use of remote sensing f or water quality inversion offers a precise, timely, and comprehensive way to ev aluate the present state and future trajectories of water quality.” Financial supporters for this research include Science And Technology Bureau of Zhejiang. Our news journalists obtained a quote from the research from Zhejiang Ocean Univ ersity: “In this paper, a remote sensing inversion model utilizing machine learn ing was developed to evaluate water quality variations in the Ma’an Archipelago Marine Special Protected Area (MMSPA) over a long-time series of Landsat images. The concentrations of chlorophyll-a (Chl-a), phosphate, and dissolved inorganic nitrogen (DIN) in the sea area from 2002 to 2022 were inverted and analyzed. Th e spatial and temporal characteristics of these variations were investigated. Th e results indicated that the random forest model could reliably predict Chl-a, p hosphate, and DIN concentrations in the MMSPA. Specifically, the inversion resul ts for Chl-a showed the coefficient of determination (R2) of 0.741, the root mea n square error (RMSE) of 3.376 mg/L, and the mean absolute percentage error (MAP E) of 16.219%. Regarding spatial distribution, the concentrations o f these parameters were notably elevated in the nearshore zones, especially in t he northwest, contrasted with lower concentrations in the offshore and southeast areas. Predominantly, the nearshore regions with higher concentrations were in proximity to the aquaculture zones. Additionally, nutrients originating from lan d sources, transported via rivers such as the Yangtze River, as well as influenc ed by human activities, have shaped this nutrient distribution. Over the long te rm, the water quality in the MMSPA has shown considerable interannual fluctuatio ns during the past two decades.”
Zhejiang Ocean UniversityZhoushanPeo ple’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningRemote Sensing
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