Estimating depths in lakes using machine learning
Recreational-grade sonar equipment can collect vast amounts of data. Unfortunately, the data is often hidden in some kind of proprietary binary format. However, efforts in reverse engineering such formats have made it possible to extract of the information. I have spent time tracking down some this information which has resulted in a R-package as well which can read ‘.sl2’ and ‘.sl3’ file formats collected using Lowrance sonar equipment. See also the sllib Python library which fills a similar gap.
Delineating lake catchments for all (180 k) Danish lakes
Predicting lake and stream chemistry from catchment characteristics using machine learning
Developing the 'sonaR' R-package for reading and processing sonar data
Using machine learning and GIS to predict carbon dioxide in streams