Lynker has well established processes for Artificial Intelligence and Machine Learning, using them across applications including data processing and analytics.
We use AI in our designs for geospatial analytical tools, pulling in massive environmental datasets and automating / augmenting quality control to train the system to spot anomalies. Our experience using languages such as R and Golang with deep learning also means we can apply artificial neural networks to location data to handle massive data sets. We also use Golang for automated image analysis, classifying fish populations from underwater cameras and algal blooms from satellite images.
In one project, Lynker scientists at NOAA utilize machine learning techniques based on historical records of release decisions to improve the National Water Model’s operational forecasting skill. Deep neural networks (i.e., feed-forward backpropagation with three hidden layers and a considerable number of neurons) have been utilized to train the National Water Model to think like a dam and reservoir operator.
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