Prediction of Discharges from Polders to ‘Boezem’ Canals with a Random Forest and an LSTM Model: Improving Inputs of the Decision Support System of the Hoogheemraadschap van Delfland

In this research the possibilities of the application of machine learning models at ‘Hoogheemraadschap van Delfland’ are studied. A random forest (RF) and an LSTM model are used for the prediction of the sum of the discharge in the next 2, 8 and 12 hours from the polders to the boezem canals. This research has showed the potential of machine...

Digital Soil Mapping based on PDFs of Cone Penetration Tests and Vibro Cores using Image Processing and Machine Learning

Digital Soil Mapping (DSM) of soil types in geotechnical project areas is a top priority. These maps are often used in decision making and can have significant consequences related to costs and risks. Usually, these maps are generated by digital soil models that interpolate soil types at known locations. In practice, conventional spatial...