首页|University of Agriculture and Forestry Researcher Details Research in Machine Le arning (Novel Learning of Bathymetry from Landsat 9 Imagery Using Machine Learni ng, Feature Extraction and Meta-Heuristic Optimization in a Shallow Turbid Lagoo n)
University of Agriculture and Forestry Researcher Details Research in Machine Le arning (Novel Learning of Bathymetry from Landsat 9 Imagery Using Machine Learni ng, Feature Extraction and Meta-Heuristic Optimization in a Shallow Turbid Lagoo n)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in artificial intelligence. According to news reporting originating from Hue City, Vietnam, by NewsRx correspondents, research stated, "Bathymetry data is indispen sable for a variety of aquatic field studies and benthic resource inventories." Funders for this research include Hue University. Our news editors obtained a quote from the research from University of Agricultu re and Forestry: "Determining water depth can be accomplished through an echo so unding system or remote estimation utilizing space-borne and air-borne data acro ss diverse environments, such as lakes, rivers, seas, or lagoons. Despite being a common option for bathymetry mapping, the use of satellite imagery faces chall enges due to the complex inherent optical properties of water bodies (e.g., turb id water), satellite spatial resolution limitations, and constraints in the perf ormance of retrieval models. This study focuses on advancing the remote sensing based method by harnessing the non-linear learning capabilities of the machine l earning (ML) model, employing advanced feature selection through a meta-heuristi c algorithm, and using image extraction techniques (i.e., band ratio, gray scale morphological operation, and morphological multi-scale decomposition). Herein, we validate the predictive capabilities of six ML models: Random Forest (RF), Su pport Vector Machine (SVM), CatBoost (CB), Extreme Gradient Boost (XGB), Light G radient Boosting Machine (LGBM), and KTBoost (KTB) models, both with and without the application of meta-heuristic optimization (i.e., Dragon Fly, Particle Swar m Optimization, and Grey Wolf Optimization), to accurately ascertain water depth ."
University of Agriculture and ForestryHue CityVietnamCyborgsEmerging TechnologiesMachine Learning
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