Staged cost optimization of urban storm drainage systems based on hydraulic performance in a changing environment

Maharjan, Manisha; Pathirana, Assela; Gersonius, Berry; Vairavamoorthy, Kalanithy

doi:10.5194/hess-13-481-2009

Cited by 32 publications

(15 citation statements)

References 3 publications

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“…The design objective is to minimize the cost including the residual flood damage and the cost of interventions. The total flood damage in the whole planning period for updating the drainage systems can be calculated as [28]:…”

Section: Staged Cost Optimization Modelmentioning

confidence: 99%

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Staged Optimization Design for Updating Urban Drainage Systems in a City of China

Bin

Lian

2018

Water

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Flooding has been reported more often than in the past in most cities of China in recent years. In response, China's State Council has urged the 36 largest cities to update the preparedness to handle the 50-year rainfall, which would be a massive project with large investments. We propose a staged optimization design for updating urban drainage that is not only a flexible option against environmental changes, but also an effective way to reduce the cost of the project. The staged cost optimization model involving the hydraulic model was developed in Fuzhou City, China. This model was established to minimize the total present costs, including intervention costs and flooding costs, with full consideration of the constraints of specific local conditions. The results show that considerable financial savings could be achieved by a staged design rather than the implement-once scheme. The model's sensitivities to four data parameters were analyzed, including rainfall increase rate, flood unit cost, storage unit cost, and discount rate. The results confirm the applicability and robustness of the model for updating drainage systems to meet the requirements. The findings of this study may have important implications on urban flood management in the cities of developing countries with limited construction investments.

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Section: Staged Cost Optimization Modelmentioning

confidence: 99%

“…To simplify the calculation of D(f), we assume that the flood damage has a linear correlation with the flooded volume [28]. In order to simplify the calculations, the continuous integral of the The storage and pumping capacity at each stage are optimized as the decision variables in this model.…”

Section: Staged Cost Optimization Modelmentioning

confidence: 99%

Staged Optimization Design for Updating Urban Drainage Systems in a City of China

Bin

Lian

2018

Water

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“…This method is one of the most popular and widely used methods in hydrologic modeling and hydrologic forecasting (e.g. van Dijk, 2010;Maharjan et al, 2009;Geetha et al, 2008). In the study of Lastra et al (2008), they chose the SCS method, for it is commonly used in different environments and delivers good results.…”

Section: Hydrologic Modeling: Hec-hmsmentioning

confidence: 99%

Reconstructing the Tropical Storm Ketsana flood event in Marikina River, Philippines

Abon

David

Pellejera

2011

Hydrol. Earth Syst. Sci.

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Abstract. In September 2009, Tropical Storm Ketsana (local name: TS Ondoy) hit the Manila metropolitan area (Metro Manila) and brought an anomalous volume of rain that exceeded the Philippines' forty-year meteorological record. The storm caused exceptionally high and extensive flooding. Part of this study was a survey conducted along the stretch of the Marikina River, one of the major rivers that flooded. Postevent resident interviews were used to reconstruct the flooding in the absence of stream gauge data. Hydraulic and hydrologic modeling were carried out to understand the mechanism that brought the flood. Peak floods occurred at different hours along the river resulting from the transmission of water from the main watershed to the downstream areas. Modeled peak flood and flood timing coincided well with actual observations except for downstream stations where actual peak floods were observed to have occurred at a later time. Compounding factors such as other flood sources and stream backflow could have caused this discrepancy. Nevertheless, prediction of flood heights and the use of the known time lag between the peak rainfall and the peak runoff could be utilized to issue timely flood forecasts to allow people to prepare for future flooding.

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“…The SWMM is a comprehensive hydrological simulation model that is commonly applied to designing the quality and quantity models of runoffs in urban areas (Ackerman and Schiff, 2003;Baffaut and Delleur, 1990;Borah and Bera, 2003;Denault et al, 2006;Maharjan et al, 2009;Tsihrintzis and Hamid, 1998). As understood, even though the RNN can easily build a water level prediction model at the gauged sites, the difficulty in predicting the water levels at an ungauged site is due to its unavailability of real observations for the training and evaluation of a RNN.…”

Section: Storm Water Management Model (Swmm)mentioning

confidence: 99%

Dynamic neural networks for real-time water level predictions of sewerage systems-covering gauged and ungauged sites

Chiang¹,

Chang²,

Tsai³

et al. 2010

Hydrol. Earth Syst. Sci.

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Abstract. In this research, we propose recurrent neural networks (RNNs) to build a relationship between rainfalls and water level patterns of an urban sewerage system based on historical torrential rain/storm events. The RNN allows signals to propagate in both forward and backward directions, which offers the network dynamic memories. Besides, the information at the current time-step with a feedback operation can yield a time-delay unit that provides internal input information at the next time-step to effectively deal with time-varying systems. The RNN is implemented at both gauged and ungauged sites for 5-, 10-, 15-, and 20-min-ahead water level predictions. The results show that the RNN is capable of learning the nonlinear sewerage system and producing satisfactory predictions at the gauged sites. Concerning the ungauged sites, there are no historical data of water level to support prediction. In order to overcome such problem, a set of synthetic data, generated from a storm water management model (SWMM) under cautious verification process of applicability based on the data from nearby gauging stations, are introduced as the learning target to the training procedure of the RNN and moreover evaluating the performance of the RNN at the ungauged sites. The results demonstrate that the potential role of the SWMM coupled with nearby rainfall and water level information can be of great use in enhancing the capability of the RNN at the ungauged sites. Hence we can conclude that the RNN is an effective and suitable model for successfully predicting the water levels at both gauged and ungauged sites in urban sewerage systems.

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Staged cost optimization of urban storm drainage systems based on hydraulic performance in a changing environment

Cited by 32 publications

References 3 publications

Staged Optimization Design for Updating Urban Drainage Systems in a City of China

Staged Optimization Design for Updating Urban Drainage Systems in a City of China

Reconstructing the Tropical Storm Ketsana flood event in Marikina River, Philippines

Dynamic neural networks for real-time water level predictions of sewerage systems-covering gauged and ungauged sites

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