首页|State University of New York (SUNY) College of Environmental Science and Forestry Researchers Reveal New Findings on Machine Learning (Mapping Water Clarity in Small Oligotrophic Lakes Using Sentinel-2 Imagery and Machine Learning Methods: A ...)
State University of New York (SUNY) College of Environmental Science and Forestry Researchers Reveal New Findings on Machine Learning (Mapping Water Clarity in Small Oligotrophic Lakes Using Sentinel-2 Imagery and Machine Learning Methods: A ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on artificial intelligence have been presented. According to news re- porting originating from Syracuse, New York, by NewsRx correspondents, research stated, “Optical remote sensing of water quality poses challenges in small oligotrophic lakes due to the diminishing contribution of constituents to the water-leaving radiance as water clarity increases. For monitoring water clarity over such lakes, this study utilizes machine learning models and data from citizen science to develop effective models for retrieving Secchi disk depth (SDD) in Canandaigua Lake, USA.” Financial supporters for this research include U.S. Department of State And Higher Education Com- mission. Our news correspondents obtained a quote from the research from State University of New York (SUNY) College of Environmental Science and Forestry: “Using Sentinel-2 band ratios as input, we trained random forest (RF), adaptive boosting, extreme gradient boosting, and support vector regression models using spatiotemporally distributed in situ data within 7 days of Senitnel-2 overpass. Each model was optimized using hyperparameter tuning, and cross-validation was used for accuracy assessment to compare the models’ effectiveness in retrieving SDD. The results indicate the superior performance of RF with an R2 of 0.74 and a root mean squared error of 0.72 m. A feature importance analysis for RF indicated the high relevance of the blue and green bands ratio in the estimation of SDD. The RF model was subsequently employed to generate temporal maps for Canandaigua Lake, indicating that water clarity tends to be higher during the early summer months (May and June) but declines during late summer and fall (September and October). This pattern can be closely associated with the increased algal presence in the lake.”
State University of New York (SUNY) College of Environmental Science and ForestrySyracuseNew YorkUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine Learning
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