**5. Summary and Conclusions**

In the age of 'smart stormwater,' the increased deployment of sensors to monitor water level characteristics is resulting in rapidly accumulating data. It is becoming crucial to understand and promote methods to handle these big datasets to help in flood detection and control. This study aims to promote understanding of how cluster analysis facilitates the interpretation of the unlabeled time-series water depth data for flooding location detection at the stormwater urban drainage systems. In this work, three indexes, including silhouette coefficient index, Calinski–Harabasz index, and Davies–Bouldin index, were used to evaluate the performance of three popular unsupervised cluster analysis models namely K-means clustering, agglomerative clustering and spectral clustering. A real-world stormwater urban drainage systems SWMM model was applied to test the performance of clustering algorithms in capturing urban floods. Five conclusions were drawn below:


**Author Contributions:** Conceptualization, J.L.; Data curation, J.L.; Formal analysis, J.L. and D.H.; Funding acquisition, J.L. and R.S.; Investigation, J.L., D.H., and S.B.; Methodology, J.L. and S.B.; Resources, R.S.; Software, J.L.; Validation, J.L.; Visualization, J.L. and D.H.; Writing—original draft, J.L.; Writing—review & editing, D.H., S.B., and R.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research is funded by the Research Assistant Scholarship at the University of Utah. The contribution of the University Innsbruck is financially supported by the Austrian Climate and Energy Fund and by the programme "Smart Cities Demo - Living Urban Innovation 2018" (project 872123).

**Acknowledgments:** We would like to thank the Salt Lake City Department of Public Utilities (SLCDPU) for their efforts in developing the SWMM model. We also thank the Computational Hydraulics Int. (CHI) company for offering the research license of PCSWMM. Finally, we thank Steven Burian and the Student Engineering Association (SAE) of the University of Utah for sharing their relevant files and materials.

**Conflicts of Interest:** The authors declare no conflict of interest.
