Non-Uniform Flow Effect on Optimal Waste Load Allocation in Rivers

Murty, Y. S. R.; Bhallamudi, S. Murty; Srinivasan, K.

doi:10.1007/s11269-006-3084-3

Cited by 30 publications

(5 citation statements)

References 30 publications

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“…The NSGA-II algorithm has been widely applied in various disciplines. Some of the recent applications in water resources management are: reservoir system optimisation (Reddy and Kumar, 2007), optimal design of water distribution networks (Atiquzzaman et al, 2006), optimal waste load allocation in rivers (Murty et al, 2006), flood control structures (Rasekh et al, 2010), and groundwater monitoring design (Reed and Minsker, 2004).…”

Section: Optimisation Modelmentioning

confidence: 99%

Optimal prioritisation of watershed management measures for flood risk mitigation on a watershed scale

Yazdi

Neyshabouri

Niksokhan

et al. 2013

J Flood Risk Management

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Watershed management includes methods to create, enhance, and maintain vegetation to reduce run‐off and provide flood control in the watershed. The assignment of priority for watershed management measures requires the use of mathematical techniques to attain the most suitable strategies. In the present study, a framework was presented for assigning the optimal combinations of watershed management measures based on simulation‐based optimisation approach. For this purpose, a one‐dimensional hydrodynamic model was used to calculate the potential damages of different flood scenarios under various combinations of watershed management measures and was coupled with the NSGA‐II multi‐objective optimisation model to provide the optimal Pareto solutions between two conflicting objectives of minimising the investment costs of flood mitigation measures and the expected flood damages of the watershed. The proposed model was then applied to a small watershed in the centre of Iran as a case study, and the optimal trade‐off solutions were calculated for flood risk mitigation. Using these trade‐offs, for each level of funding, decision makers can select the optimal combination of watershed management measures considering decision criteria.

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Section: Optimisation Modelmentioning

confidence: 99%

Optimal prioritisation of watershed management measures for flood risk mitigation on a watershed scale

Yazdi

Neyshabouri

Niksokhan

et al. 2013

J Flood Risk Management

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“…It has been widely applied in various disciplines. Some of the recent applications are reservoir system optimization (Reddy and Kumar 2007), optimal design of water distribution networks (Atiquzzaman et al 2006), optimal waste load allocation in rivers (Murty et al 2006), and groundwater monitoring design (Reed and Minsker 2004).…”

Section: Multi-objective Optimization Algorithmmentioning

confidence: 99%

Risk-Cost Optimization of Hydraulic Structures: Methodology and Case Study

Rasekh

Afshar

2010

Water Resour Manage

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Simultaneous consideration of initial construction cost and failure risk in practical design of critical hydraulic structures is a long-standing problem in water resources planning and management. Despite extensive efforts to effectively tackle this problem during recent decades, the traditional rather inefficient technique of return period approach still enjoys being the dominant technique in real-world projects. Accordingly, this article aims at developing an innovative evolutionarycomputation-based multi-objective model as a remedy to shortcomings of existing common methods. This simulation-optimization framework generates a series of uniformly distributed trade-off solutions which represent the compromise between competing objectives of construction cost and failure risk. Hydrologic and hydraulic uncertainties along with flood routing process are considered in the optimization process to provide a more complete picture of the problem. This approach is demonstrated and discussed for flood diversion system of Bakhtiari Dam in Iran and the Pareto optimal fronts are found for different combinations of uncertainties. The presented multi-objective approach can effectively enlarge the decision maker's scope to more efficiently determine the optimum design of the system.

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“…It overcomes the drawbacks of the earlier non-dominated sorting-based multiobjective evolutionary algorithms such as high computational complexity of non-dominated sorting, lack of elitism and the need for specifying a sharing parameter (Niksokhan et al, 2009). Some of the recent applications using the fast elitist NSGA-II in water resources management are: groundwater monitoring design (Reed and Minsker, 2004), optimal design of water distribution networks (Atiquzzaman et al, 2006), optimal waste load allocation in rivers (Murty et al, 2006;Niksokhan et al, 2009), flood-control structures (Rasekh et al, 2010), detention-pond optimization (Yu et al, 2015), and reservoir system optimization (Reddy and Kumar, 2007;Shiau, 2009;Yazdi et al, 2012). It is adopted in the present paper to optimize the combination of input variables to construct the hourly typhoon rainfall forecasting models due to its successful applications in the literature.…”

Section: Introductionmentioning

confidence: 99%

Predictor selection method for the construction of support vector machine (SVM)‐based typhoon rainfall forecasting models using a non‐dominated sorting genetic algorithm

Yang

Lin

et al. 2018

Meteorological Applications

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This study proposes a predictor selection method for constructing a support vector machine (SVM)‐based typhoon rainfall forecasting models using a fast elitist Non‐dominated Sorting Genetic Algorithm II (NSGA‐II). Based on SVMs, four rainfall forecasting models with different combinations of the three types of input variables (i.e. antecedent rainfalls, typhoon characteristics and local weather factors) were constructed for 1–6 hr‐ahead forecasting. An application to three rain gauge stations in the Yilan River basin, northeastern Taiwan, was conducted to demonstrate the superiority of the proposed predictor selection method. The results showed that the optimal combination of predictors for each SVM‐based rainfall forecasting model can be automatically and effectively determined by the proposed predictor selection method. The rainfall forecasting model using all three types of input variables performed better than the other three models, especially for long lead‐time forecasting. The construction of rainfall forecasting models is helpful to extend the lead time of flood forecasting. The optimal rainfall forecasting model can be further integrated with river hydraulic models or flood inundation models for flood forecasting to assist floodplain managers to take suitable precautionary measures during typhoon landfall.

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Non-Uniform Flow Effect on Optimal Waste Load Allocation in Rivers

Cited by 30 publications

References 30 publications

Optimal prioritisation of watershed management measures for flood risk mitigation on a watershed scale

Optimal prioritisation of watershed management measures for flood risk mitigation on a watershed scale

Risk-Cost Optimization of Hydraulic Structures: Methodology and Case Study

Predictor selection method for the construction of support vector machine (SVM)‐based typhoon rainfall forecasting models using a non‐dominated sorting genetic algorithm

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