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 1992-1998 and tested by the data of 1999-2000. The predictive accuracy of the models is found to be adequate for the potential use in the operational decision making.
Bhattacharya
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Hydroinformatics and Knowledge Management Department, UNESCO-IHE Institute for Water Education, P.O. Box 3015, 2601 DA Delft, The Netherlands. b.bhattacharya@unesco-ihe.org
Solomatine
D P
DP
D000465:Algorithms; D001185:Artificial Intelligence; D003627:Data Interpretation, Statistical; D017753:Ecosystem; D019015:Geologic Sediments; D016014:Linear Models; D009426:Netherlands; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D013997:Time Factors