Machine learning in sedimentation modelling

The paper presents machine learning (ML) models that predict sedimentation in the harbour basin of the Port of Rotterdam. The important factors affecting the sedimentation process such as waves, wind, tides, surge, river discharge, etc. are studied, the corresponding time series data is analysed, missing values are estimated and the most important variables behind the process are chosen as the inputs. Two ML methods are used: MLP ANN and M5 model tree. The latter is a collection of piece-wise linear regression models, each being an expert for a particular region of the input space. The models are trained on the data collected during 1992

• Type: Journal
• Year: 2006
• Where: NN
• Authors: Biswanath Bhattacharya, Dimitri P. Solomatine
• Keywords: Corresponding Time Series, Linear Regression Models, Neural Networks, NN 2006, Sedimentation Process