A Comparison of Green Roof Systems with Conventional Roof for the Storm Water Runoff

2013 ◽  
pp. 287-303
Author(s):  
Sachiko Kikuchi ◽  
Hajime Koshimizu
Keyword(s):  
Green Roof ◽  
Storm Water ◽  
Water Runoff ◽  
Storm Water Runoff

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

2021 ◽  
Vol 25 (11) ◽  
pp. 5917-5935
Author(s):  
Elhadi Mohsen Hassan Abdalla ◽  
Vincent Pons ◽  
Virginia Stovin ◽  
Simon De-Ville ◽  
Elizabeth Fassman-Beck ◽  
...  
Keyword(s):  
Machine Learning ◽  
Green Roof ◽  
Green Roofs ◽  
Storm Water ◽  
Water Runoff ◽  
Learning Methods ◽  
Retention Model ◽  
Machine Learning Methods ◽  
Storm Water Runoff ◽  
Extensive Green Roofs

Abstract. 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.

scholarly journals AN ANALYSIS ON THE EFFICIENCY OF GREEN ROOF IN MANAGING URBAN STORMWATER RUNOFF

2021 ◽  
Vol 19 (17) ◽  
Author(s):  
Shazmin Shareena Ab. Azis ◽  
Muhammad Najib Mohamed Razali ◽  
Nurul Hana Adi Maimun ◽  
Nurul Syakima Mohd Yusoff ◽  
Mohd Shahril Abdul Rahman ◽  
...  
Keyword(s):  
Flash Flood ◽  
Meta Analysis ◽  
Green Roof ◽  
Drainage System ◽  
Green Roofs ◽  
Storm Water ◽  
Water Runoff ◽  
Extensive Green Roof ◽  
Storm Water Runoff ◽  
Urban Storm

Modernization has created new impervious urban landscape contributed to major catastrophe. Urban drainage system incapable to convey the excess rainwater resulting in flash flood due to heavy rainfall. The combination of green roof on building have tremendously proved to control stormwater efficiently. This study is conducted to review the efficiency of intensive and extensive green roof in reducing urban storm water runoff. This study identifies characteristic of green roof that contributes to lessening urban storm water runoff. Data was collected based on rigorous literature reviews and analyzed using meta-analysis. Overall, findings revealed intensive green roof performed better in reducing storm water runoff compared to extensive green roof. Green roof performance increases as the depth of substrate increased. Origanum and Sedum plants are both highly effective for intensive and extensive green roofs. The performance of green roof reduces as degree of roof slope increased.

Storm Water Runoff Mitigation Using a Green Roof

2009 ◽  
Vol 26 (2) ◽  
pp. 407-418 ◽  
Author(s):  
Daniel J. Bliss ◽  
Ronald D. Neufeld ◽  
Robert J. Ries
Keyword(s):  
Green Roof ◽  
Storm Water ◽  
Water Runoff ◽  
Storm Water Runoff

Green Roof Storm-Water Runoff Quantity and Quality

2013 ◽  
Vol 139 (4) ◽  
pp. 471-478 ◽  
Author(s):  
Susan Morgan ◽  
Serdar Celik ◽  
William Retzlaff
Keyword(s):  
Green Roof ◽  
Storm Water ◽  
Water Runoff ◽  
Storm Water Runoff

A green roof experimental site in the Mediterranean climate: the storm water quality issue

2013 ◽  
Vol 68 (6) ◽  
pp. 1419-1424 ◽  
Author(s):  
Ilaria Gnecco ◽  
Anna Palla ◽  
Luca G. Lanza ◽  
Paolo La Barbera
Keyword(s):  
Atmospheric Deposition ◽  
Water Chemistry ◽  
Mediterranean Climate ◽  
Green Roof ◽  
Green Roofs ◽  
Storm Water ◽  
Water Runoff ◽  
Experimental Site ◽  
Storm Water Runoff ◽  
Runoff Quality

Since 2007, the University of Genoa has been carrying out a monitoring programme to investigate the hydrologic response of green roofs in the Mediterranean climate by installing a green roof experimental site. In order to assess the influence of green roofs on the storm water runoff quality, water chemistry data have been included in the monitoring programme since 2010, providing rainfall and outflow data. For atmospheric source, the bulk deposition is collected to evaluate the role of the overall atmospheric deposition in storm water runoff quality. For subsurface outflow, a maximum of 24 composite samples are taken on an event basis, thus aiming at a full characterization of the outflow hydrograph. Water chemistry data reveal that the pollutant loads associated with green roof outflow is low; in particular, solids and metal concentrations are lower than values generally observed in storm water runoff from traditional rooftops. The concentration values of chemical oxygen demand, total dissolved solids, Fe, Ca and K measured in the subsurface outflow are significantly higher than those observed in the bulk deposition (p &lt; 0.05). With respect to the atmospheric deposition, the green roof behaviour as a sink/source of pollutants is investigated based on both concentration and mass.

Long-Term Simulation of Pollutant Loads in Treatment Plant Effluents and Combined Sewer Overflows

1990 ◽  
Vol 22 (10-11) ◽  
pp. 69-76 ◽  
Author(s):  
A. Durchschlag
Keyword(s):  
Waste Water Treatment ◽  
Treatment Plant ◽  
Storm Water ◽  
Water Runoff ◽  
Storage Tanks ◽  
Combined Sewer Overflows ◽  
Storm Water Runoff ◽  
Combined Sewer ◽  
Hydraulic Load ◽  
Water Tanks

As a result of urbanization, the pollutant discharges from sources such as treatment plant effluents and polluted stormwaters are responsible for an unacceptable water quality in the receiving waters.In particular, combined sewer system overflows may produce great damage due to a shock effect. To reduce these combined sewer overflow discharges, the most frequently used method is to build stormwater storage tanks. During storm water runoff, the hydraulic load of waste water treatment plants increases with additional retention storage. This might decrease the treatment efficiency and thereby decrease the benefit of stormwater storage tanks. The dynamic dependence between transport, storage and treatment is usually not taken into account. This dependence must be accounted for when planning treatment plants and calculating storage capacities in order to minimize the total pollution load to the receiving waters. A numerical model will be described that enables the BOD discharges to be continuously calculated. The pollutant transport process within the networks and the purification process within the treatment plants are simulated. The results of the simulation illustrate; a statistical balance of the efficiency of stormwater tanks with the treatment plant capacity and to optimize the volume of storm water tanks and the operation of combined sewer systems and treatment plants.

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