Abstract
This study adopts rainwater harvesting systems (RWHS) into a stormwater runoff management model (SWMM) for the spatial design of capacities and quantities of rain barrel for urban flood mitigation. A simulation-optimization model is proposed for effectively identifying the optimal design. First of all, we particularly classified the characteristic zonal subregions for spatial design by using fuzzy C-means clustering with the investigated data of urban roof, land use and drainage system. In the simulation method, a series of regular spatial arrangements specification are designed by using statistical quartiles analysis for rooftop area and rainfall frequency analysis; accordingly, the corresponding reduced flooding circumstances can be simulated by SWMM. Moreover, the most effective solution for the simulation method is identified from the calculated net benefit, which is equivalent to the subtraction of the facility cost from the decreased inundation loss. It serves as the initially identified solution for the optimization model. In the optimization method, backpropagation neural network (BPNN) are first applied for developing a water level simulation model of urban drainage systems to substitute for SWMM to conform to newly considered interdisciplinary multi-objective optimization model, and a tabu search-based algorithm is used with the embedded BPNN-based SWMM to optimize the planning solution. The developed method is applied to the Zhong-He District, Taiwan. Results demonstrate that the application of tabu search and the BPNN-based simulation model into the optimization model can effectively, accurately and fast search optimal design considering economic net benefit. Furthermore, the optimized spatial rain barrel design could reduce 72% of inundation losses according to the simulated flood events.
Highlights
In recent years, on account of global climate change and the increasing occurrence of extreme hydrological events, coupled with the fact that Taiwan is densely populated and overdeveloped in catchment areas, the amount of flooding caused by heavy rain often exceeds the scale of the originally designed standard
All coefficient of correlation (CC) values reached 0.96; the CC values for control point 1 (CP1)−control point 3 (CP3) were 0.968, 0.970, and 0.963, respectively. These results indicate that the backpropagation neural network (BPNN)-based stormwater runoff management model (SWMM) developed by our research can accurately and quickly simulate the water level change of rainstorm events
The BPNN-based SWMM developed by our institute may have some errors in the calculation results; our institute returned the optimal solution to the United States Environmental Protection Agency (US EPA) SWMM to simulate the water level and volume of the burst pipes, and to calculate the actual flooding loss
Summary
On account of global climate change and the increasing occurrence of extreme hydrological events, coupled with the fact that Taiwan is densely populated and overdeveloped in catchment areas, the amount of flooding caused by heavy rain often exceeds the scale of the originally designed standard. The pumping station of the urban drainage system cannot handle such large amount of flood in recent years, this leads to flooding and the subsequent loss of life and property. In response to this challenging situation, new modes, measures and solutions should be developed to achieve the goal and evaluate the feasibility for flood mitigation. The hydrology component of SWMM operates on a collection of subcatchment areas with and without depression storage to predict runoff from precipitation, evaporation and infiltration losses from each of the subcatchment. The routing portion of SWMM transports this runoff through a system of pipes, channels, storage/treatment devices, pumps, and regulators
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