Data preparation for data mining
Data preparation for data mining
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
2006 Special issue: Modular learning models in forecasting natural phenomena
Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
IEEE Transactions on Information Theory
2006 Special issue: Modular learning models in forecasting natural phenomena
Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
A New Cluster Based Fuzzy Model Tree for Data Modeling
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Expert Systems with Applications: An International Journal
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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.