Automated model construction for combined sewer overflow prediction based on efficient LASSO algorithm

The prediction of combined sewer overflow (CSO) operation in urban environments presents a challenging task for water utilities. The operation of CSOs (most often in heavy rainfall conditions) prevents houses and businesses from flooding. However, sometimes, CSOs do not operate as they should, potentially bringing environmental pollution risks. Therefore, CSOs should be appropriately managed by water utilities, highlighting the need for adapted decision support systems. This paper proposes an automated CSO predictive model construction methodology using field monitoring data, as a substitute for the commonly established hydrological-hydraulic modeling approach for time-series prediction of CSO statuses. It is a systematic methodology factoring in all monitored field variables to construct time-series dependencies for CSO statuses. The model construction process is largely automated with little human intervention, and the pertinent variables together with their associated time lags for every CSO are holistically and automatically generated. A fast least absolute shrinkage and selection operator solution generating scheme is proposed to expedite the model construction process, where matrix inversions are effectively eliminated. The whole algorithm works in a stepwise manner, invoking either an incremental or decremental movement for including or excluding one model regressor into, or from, the predictive model at every step. The computational complexity is thereby analyzed with the pseudo code provided. Actual experimental results from both single-step ahead (i.e., 15 min) and multistep ahead predictions are finally produced and analyzed on a U.K. pilot area with various types of monitoring data made available, demonstrating the efficiency and effectiveness of the proposed approach

Reducing Flood Risk in Changing Environments: Optimal Location and Sizing of Stormwater Tanks Considering Climate Change

[EN] In recent years, there has been an increase in the frequency of urban floods as a result of three determinant factors: the reduction in systems' capacity due to aging, a changing environment that has resulted in alterations in the hydrological cycle, and the reduction of the permeability of watersheds due to urban growth. Due to this, a question that every urban area must answer is: Are we ready to face these new challenges? The renovation of all the pipes that compose the drainage system is not a feasible solution, and, therefore, the use of new solutions is an increasing trend, leading to a new operational paradigm where water is stored in the system and released at a controlled rate. Hence, technologies, such as stormwater tanks, are being implemented in different cities. This research sought to understand how Climate Change would affect future precipitation, and based on the results, applied two different approaches to determine the optimal location and sizing of storage units, through the application of the Simulated Annealing and Pseudo-Genetic Algorithms. In this process, a strong component of computational modeling was applied in order to allow the optimization algorithms to efficiently reach near-optimal solutions. These approaches were tested in two stormwater networks at Bogota, Colombia, considering three different rainfall scenarios.This research was funded by MEXICHEM-PAVCO and COLCIENCIAS, grant number 565263339028Saldarriaga, J.; Salcedo, C.; Solarte, L.; Pulgarín, L.; Rivera, ML.; Camacho, M.; Iglesias Rey, PL.... (2020). Reducing Flood Risk in Changing Environments: Optimal Location and Sizing of Stormwater Tanks Considering Climate Change. 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