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Machine learning in sedimentation modelling.

B Bhattacharya¹, D P Solomatine

¹Hydroinformatics and Knowledge Management Department, UNESCO-IHE Institute for Water Education, P.O. Box 3015, 2601 DA Delft, The Netherlands. b.bhattacharya@unesco-ihe.org

Neural Networks : the Official Journal of the International Neural Network Society

|March 15, 2006

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Machine learning (ML) models accurately predict harbor sedimentation using factors like waves and wind. These models, including MLP Artificial Neural Networks (ANN) and M5 model trees, offer operational decision-making potential.

Area of Science:

Environmental Engineering
Data Science
Hydrodynamics

Background:

Sedimentation in harbor basins, like the Port of Rotterdam, poses significant operational challenges.
Understanding and predicting sedimentation is crucial for efficient port management and maintenance.

Purpose of the Study:

To develop and evaluate machine learning models for predicting sedimentation in the Port of Rotterdam harbor basin.
To identify key environmental factors influencing sedimentation processes.

Main Methods:

Analysis of time series data for factors including waves, wind, tides, surge, and river discharge.
Application of two machine learning techniques: Multi-layer Perceptron Artificial Neural Network (MLP ANN) and M5 model tree.
Feature selection to identify the most influential variables for sedimentation prediction.

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Main Results:

The developed ML models demonstrated adequate predictive accuracy for sedimentation.
The M5 model tree, a set of piecewise linear regression models, proved effective.
Key variables influencing sedimentation were identified through data analysis.

Conclusions:

Machine learning models provide a viable tool for operational decision-making regarding harbor sedimentation.
The study highlights the effectiveness of MLP ANN and M5 model trees in this application.
Accurate prediction of sedimentation can lead to optimized port operations and reduced maintenance costs.