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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 OPEN ACCESSWater 2015, 7 5174 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.

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Abstract:Taiwan is located on a route where typhoons often strike. Each year, the strong winds accompanying typhoons are a substantial threat and cause significant damage. However, because the terrains of high mountains in Taiwan vary greatly, when a typhoon passes the Central Mountain Range (CMR), the wind speed of typhoons becomes difficult to predict. This research had two primary objectives: (1) to develop data-driven techniques and a powerful artificial neural network (ANN) model to predict the highly complex nonlinear wind systems in western Taiwan; and, (2) to investigate the accuracy of wind speed predictions at various locations and for various durations in western Taiwan when the track of westward typhoons is affected by the complex geographical shelters and disturbances of the CMR. This study developed a typhoon wind speed prediction model that evaluated various typhoon tracks (covering Type 2, Type 3, and Type 4 tracks, as defined by the Central Weather Bureau), and evaluated the prediction accuracy at Hsinchu, Wuqi, and Kaohsiung Stations in western Taiwan. Back propagation neural networks (BPNNs) were employed to establish wind speed prediction models, and a linear regression model was adopted as the benchmark to evaluate the strengths and weaknesses of the BPNNs. The results were as follows: (1) The BPNNs generally had favorable performance in predicting wind speeds and their performances were superior to linear regressions; (2) when absolute errors were adopted to evaluate the prediction performances, the predictions at Hsinchu Station were the most accurate, whereas those at Wuqi Station were the least accurate; however, when relative errors were adopted, the predictions at Hsinchu Station were again the most accurate, whereas those at Kaohsiung were the least accurate; and, (3) regarding the relative error rates for the maximum wind speed of Types 2, 3, and 4 typhoons, Wuqi, Kaohsiung, and Wuqi had the most accurate performance, respectively; as for maximum wind time error (ET M ) for Types 2, 3, and 4 typhoons, Kaohsiung, Wuqi, and Wuqi correspondingly performed the most favorably.

Abstract:This study applies an integrated methodology to assess short-term over-levee risk and long-term water shortage risk in the Da-Han Creek basin, which is the most important flood control and water storage system in northern Taiwan. An optimization model for reservoir flood control and water supply is adopted, to determine reservoir releases based on synthetic inflow hydrographs during typhoons, which are generated by Monte Carlo simulations. The release is then used to calculate the water level at a downstream control point using a novel developed back-propagation neural network-based model, to reduce computational complexity and achieve automatic-efficient risk evaluation. The calculated downstream water levels and final reservoir water levels after a typhoon event are used to evaluate the mapped over-levee risk and water shortage risk, respectively. The results showed that the different upper limit settings for the reservoir have a significant influence on the variation of 1.19 × 10 −5 % to 75.6% of the water shortage risk. This occurs because of the insufficient inflow and narrow storage capacity of the Shih-Men Reservoir during drought periods. However, the upper limit settings have a minor influence (with a variation of only 0.149% to 0.157%) on the over-levee risk in typhoon periods, because of the high protection standards for the downstream embankment.

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