Machine learning models

Data scientists at Neptune have been building and applying machine learning models for over two decades. We specialize in developing customized applications of boosting, bagging, support vector machines (SVM), neural nets, and more. This subset of AI modeling has been used in dozens of applications, including:

• Sensitivity analysis of complex fate and transport models to support radiological performance assessments

• Automating land use classification from remotely sensed data in the Brazilian Arc of Deforestation

• Quantifying the impact of changes in environmental stressors on sea grass productivity

• Forecasting Harmful Algal Bloom (HAB) extent and severity

• Projecting fire activity in Alaska using statistically downscaled climate data derived from General Circulation Models driven by Representative Concentration Pathways