Evaluating different machine learning methods to simulate runoff from extensive green roofs

Green roofs are increasingly popular measures to permanently reduce or delay storm-water runoff. The main objective of the study was to examine the potential of using machine learning (ML) to simulate runoff from green roofs to estimate their hydrological performance. Four machine learning methods, artificial neural network (ANN), M5 model tree, long short-term memory (LSTM) and k nearest neighbour (kNN), were applied to simulate storm-water runoff from 16 extensive green roofs located in four Norwegian cities across different climatic zones. The potential of these ML methods for estimating green roof retention was assessed by comparing their simulations with a proven conceptual retention model. Furthermore, the transferability of ML models between the different green roofs in the study was tested to investigate the potential of using ML models as a tool for planning and design purposes. The ML models yielded low volumetric errors that were comparable with the conceptual retention models, which indicates good performance in estimating annual retention. The ML models yielded satisfactory modelling results (NSE >0.5) in most of the roofs, which indicates an ability to estimate green roof detention. The variations in ML models' performance between the cities was larger than between the different configurations, which was attributed to the different climatic characteristics between the four cities. Transferred ML models between cities with similar rainfall events characteristics (Bergen–Sandnes, Trondheim–Oslo) could yield satisfactory modelling performance (Nash–Sutcliffe efficiency NSE >0.5 and percentage bias |PBIAS| <25 %) in most cases. However, we recommend the use of the conceptual retention model over the transferred ML models, to estimate the retention of new green roofs, as it gives more accurate volume estimates. Follow-up studies are needed to explore the potential of ML models in estimating detention from higher temporal resolution datasets

Independent Validation of the SWMM Green Roof Module

Green roofs are a popular Sustainable Drainage Systems (SuDS) technology. They provide multiple benefits, amongst which the retention of rainfall and detention of runoff are of particular interest to stormwater engineers. The hydrological performance of green roofs has been represented in various models, including the Storm Water Management Model (SWMM). The latest version of SWMM includes a new LID green roof module, which makes it possible to model the hydrological performance of a green roof by directly defining the physical parameters of a green roof’s three layers. However, to date, no study has validated the capability of this module for representing the hydrological performance of an extensive green roof in response to actual rainfall events. In this study, data from a previously-monitored extensive green roof test bed has been utilised to validate the SWMM green roof module for both long-term (173 events over a year) and short-term (per-event) simulations. With only 0.357% difference between measured and modelled annual retention, the uncalibrated model provided good estimates of total annual retention, but the modelled runoff depths deviated significantly from the measured data at certain times (particularly during summer) in the year. Retention results improved (with the difference between modelled and measured annual retention decreasing to 0.169% and the Nash-Sutcliffe Model Efficiency (NSME) coefficient for per-event rainfall depth reaching 0.948) when reductions in actual evapotranspiration due to reduced substrate moisture availability during prolonged dry conditions were used to provide revised estimates of monthly ET. However, this aspect of the model’s performance is ultimately limited by the failure to account for the influence of substrate moisture on actual ET rates. With significant differences existing between measured and simulated runoff and NSME coefficients of below 0.5, the uncalibrated model failed to provide reasonable predictions of the green roof’s detention performance, although this was significantly improved through calibration. To precisely model the hydrological behaviour of an extensive green roof with a plastic board drainage layer, some of the modelling structures in SWMM green roof module require further refinement

Floating Vegetated Island Retrofit to Treat Stormwater Runoff

International audienc

Designing Dry Swales for Stormwater Quality Improvement Using the Aberdeen Equation

This case study presents a semiempirical method for designing water quality swales to treat stormwater runoff that is an alternative to current mostly anecdotal design approaches. Water quality swales are intended to reduce pollutant concentrations; they are not just flow conveyance systems. The design presented herein is a two-part process: (1) hydraulic design, and (2) treatment design. A hydraulic design feature unique to water quality swales includes maximum flow depths typically lower than grass height. Frequency analysis is used to estimate the water quality design storm intensity, and the design peak flow rate is estimated using the Rational method. Subsequently, Manning's equation is used to determine the swale cross-section and slope. A relatively high roughness coefficient (n=∼0.35) is applied because the water is not intended to overtop the vegetation. This case study used the Aberdeen equation to calculate pollutant removal efficiencies if particle-size information was available. The method was applied to field-monitored swales in Auckland, New Zealand and Knightdale, North Carolina, US, and was found to accurately predict sediment capture. The conceptual approach presented here can be used to estimate reductions in total suspended solids by swales. However, the method needs to be validated with appropriate monitoring data in estimating removal of metals and other particulate-bound pollutants, but it is not applicable to the dissolved fraction of pollutants.</p