Journal
WATER
Volume 11, Issue 4, Pages -Publisher
MDPI
DOI: 10.3390/w11040714
Keywords
dam safety; displacement; Gaussian mixture model; iterative self-organizing data analysing; random coefficient model
Categories
Funding
- National Key R&D Program of China [2016YFC0401601, 2017YFC0804607]
- National Natural Science Foundation of China [51739003, 51479054, 51779086, 51579086, 51379068, 51579083, 51579085, 51609074]
- Priority Academic Program Development of Jiangsu Higher Education Institutions [YS11001]
- Jiangsu Natural Science Foundation [BK20160872]
- Special Project - National Key Laboratory [20145027612, 20165042112]
- Key R&D Program of Guangxi [AB17195074]
- Central University Basic Research Project [2017B11114]
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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.
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