Self-organizing map and clustering for wastewater treatment monitoring

The objective of this project is the development of plant supervision techniques based on self-organizing map (SOM) for the implementation in a wastewater treatment plant. SOM is an unsupervised learning algorithm to establish the relationships among process variables. Clustering techniques such as K-means algorithm have been used for the system state estimation, monitoring and visualization of process states. The best clustering structure is selected by means of the Davies–Bouldin index for evaluation of the several structures obtained from K-means.

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