*4.3. Limitations and Future Work*

Although this study has identified some clear differences in the application of cluster analysis, there are several limitations. Firstly, the majority of scenarios used time-series water depth datasets generated by model simulation. As these are smooth and noise-free, the results may not scale to field application. However, we found similarities between the results with the limited set of observed rainfall series used here, notably in the use of the different indices, but tend to result in a smaller number of clusters. Further work should apply these methods to a wider set of observed data to reduce the input (meteorological) uncertainties and meteorological variances if such data becomes available [36,37,78,79]. The possible integration of ensemble prediction system (EPS) and data assimilation techniques might be of interest for future work, which could provide help for estimating forecast uncertainty via a linear combination of suitable meteorological variances and uncertainties linked to the rainfall and hydraulics [80,81]. Secondly, as this paper only focuses on exploring usefulness of clustering model implementation and performance evaluation, analysis of errors and sensitivity analysis of water level datasets are recommended for to improve the reliability of results. Future work will concentrate on the application of these methods, including water-level sensor placement, combined sewer overflow detection, and urban flooding prediction. Since the dendrogram enables the AC algorithm to detect outliers in time-series water depth datasets, this can be used to help guide sensor deployment on vulnerable sites for observing overflow and flood events in the field [76]. It is planned to consider strengthening the connection between the theoretical results and field application by conducting a cluster analysis to optimize the sensor monitoring network for flooding detection at UDSs.
