An innovative method of predicting the maximum flow in stormwater sewage systems using soft-sensors

Developing universal hydrological models for modeling urban catchments remains one of the major challenges in contemporary hydrology. This study aimed to create a model that integrates catchment characteristics, sewer network topology, sewer storage capacity, and rainfall data, along with a sensitivity analysis of input parameters. The goal was to evaluate the potential of advanced analytical methods, specifically, Multivariate Adaptive Regression Splines (MARS) and soft-sensor technology, to improve peak flow (Q) forecasting in stormwater systems. The results showed that combining MARS models with soft sensors yields high forecasting accuracy (R² = 0.96, RMSE = 0.038), even under variable rainfall conditions. However, the development of universally applicable model relationships proved challenging due to difficulties in parameterizing the model under changing rainfall scenarios. Additionally, the inclusion of a risk analysis method also enabled consideration of sewer network capacity and introduced a safety margin coefficient to assess system flexibility under future climate conditions. While the proposed approach does not lead to the creation of universal tools, it offers valuable insights for further research on adapting sewer systems to evolving hydrological conditions. The findings suggest promising directions for the development of cost-free, zero-emission soft sensors and models adaptable across diverse urban catchments.