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Open AccessArticle

Concrete Dam Displacement Prediction Based on an ISODATA-GMM Clustering and Random Coefficient Model

by 1,2,3, 1,2, 1,2,* and 3
1
College of Water Conservancy and Hydropower Engineering, Hohai University, 210098 Nanjing, China
2
National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety, 210098 Nanjing, China
3
Laboratory of Environmental Hydraulics, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
*
Author to whom correspondence should be addressed.
Water 2019, 11(4), 714; https://doi.org/10.3390/w11040714
Received: 15 February 2019 / Revised: 23 March 2019 / Accepted: 2 April 2019 / Published: 6 April 2019
(This article belongs to the Special Issue Machine Learning Applied to Hydraulic and Hydrological Modelling)
Displacement data modelling is of great importance for the safety control of concrete dams. The commonly used artificial intelligence method modelled the displacement data at each monitoring point individually, i.e., the data correlations between the monitoring points are overlooked, which leads to the over-fitting problem and the limitations in the generalization of model. A novel model combines Gaussian mixture model and Iterative self-organizing data analysing (ISODATA-GMM) clustering and the random coefficient method is proposed in this article, which takes the temporal-spatial correlation among the monitoring points into account. By taking the temporal-spatial correlation among the monitoring points into account and building models for all the points simultaneously, the random coefficient model improves the generalization ability of the model through reducing the number of free model variables. Since the random coefficient model supposed the data follows normal distributions, we use an ISODATA-GMM clustering algorithm to classify the measuring points into several groups according to its temporal and spatial characteristics, so that each group follows one distribution. Our model has the advantage of having a stronger generalization ability. View Full-Text
Keywords: dam safety; displacement; Gaussian mixture model; iterative self-organizing data analysing; random coefficient model dam safety; displacement; Gaussian mixture model; iterative self-organizing data analysing; random coefficient model
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Hu, Y.; Shao, C.; Gu, C.; Meng, Z. Concrete Dam Displacement Prediction Based on an ISODATA-GMM Clustering and Random Coefficient Model. Water 2019, 11, 714.

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