Automatic classification of municipal call data to support quantitative risk analysis of urban drainage systems

Abstract

Quantitative analyses of urban flood risks are often limited by lack of data on flood incidents. Call data are a valuable source of information about urban flood incidents, yet the unstructured nature of call information results in large time investments to prepare the data for application in quantitative analyses. Consequently, the existing call databases are not used for this purpose. If automatic classification routines can be applied to transfer unstructured call data into a quantitative data source, large stores of currently unused data can be made available for quantitative risk analysis of urban infrastructure systems. This article aims to assess whether automatic classification of calls from municipal call centres can reach sufficient accuracy to allow for use of the results in quantitative risk analysis. This is illustrated by the application of automatic classification results in quantitative fault tree analysis for urban flooding, for two cases with datasets of approximately 6000 calls. The results show that the obtained classification accuracy is sufficient to correctly rank failure mechanisms according to their contributions to the overall failure probability. This is a promising first result that shows the potential of automatic call classification to obtain data about failure incidents that are otherwise hard to find.

Spatio-Temporal Analysis of Areas Vulnerable to Urban Flooding: A Case Study of Lahore, Pakistan

“…In this way, urban flooded areas are analysed spatially and temporally. Also, incident intensity can be quantified which are creating problems and disrupting the daily activities of citizens (Veldhuis et al, 2013;Veldhuis et al, 2011). However, this method is surely not complete coverage of flood occurrence.…”

Decision support method to systematically evaluate first-level inspections of the functional status of check dams

“…Other important criteria that should be further investigated to set priorities for maintenance planning are (Table 1 in Section 2): for example, changes in the functional status at different periods; dominant water-sediment processes in the system; functional type of the check dam within the system; and type and relevance of exposed elements that are being protected by the check dam. Moreover, the outputs of this method could be further evaluated with alternative approaches such as Bayesian inference (Schweckendiek, Vrouwenvelder, & Calle, 2014) and fault tree analysis (Ten Veldhuis, Harder, & Loog, 2013) to analyse changes of the functional status over time.…”

Critical rainfall thresholds for urban pluvial flooding inferred from citizen observations

“…Both rain events stay well below the critical thresholds and half-year return period across all time-scales. This suggests that flooding was associated with other types of failure mechanisms, such as blockages of sewer pipes, pumps or sewer inlets (ten Veldhuis, 2010).…”

“…On the other hand, there were also many FNs in the model, which corresponded to days with no or little rain (e.g., Rd 1440 < 5 mm). The most likely explanation for the flooding in these cases is a blockage in the stormwater system rather than excess rainfall (ten Veldhuis et al, 2011(ten Veldhuis et al, , 2010. Alternative explanations are (i) an underestimation of rainfall by radar, and (ii) large amounts of antecedent rainfall on the days prior to the flooding.…”

“…Urban pluvial flooding occurs when stormwater produced by intense or long-lasting rainfall exceeds the capacity of urban https://doi.org/10.1016/j.scitotenv.2019.06.355 0048-9697/© 2019 Elsevier B.V. All rights reserved. drainage or sewerage systems (Kolsky, 1999;Butler and Davies, 2004;ten Veldhuis, 2010). It is one of the most common and costly natural hazards worldwide, posing a great threat to human safety and economic growth (Spekkers et al, 2015).…”