Neural Network-Based Simulation-Optimization Model for Reservoir Operation
By T.R. Neelakantan and N.V. Pundarikanthan
Summary
In many reservoir operations studies - such as that of Chennai, India, which is the case used for this article – complex simulation models combined with optimization models are computationally infeasible when trying to model the problem. In an effort to circumvent this problem, the authors propose a way to develop a “planning model for reservoir operation that uses a simulation-optimization approach.” To do this they use a neural network-based simulation model, which was developed for reservoir system operation, as a submodel in a Hooke and Jeeves unconstrained nonlinear programming model. The optimization model minimized the operation policies.
That’s it in a nutshell… if you want more you’ll have to wait until Wednesday when Michelle and I present. The suspense is killing you I’m sure.
Discussion
I thought this article was a little more difficult to follow than the previous few articles. Or it could be that I’m already creating excuses for why I might be clueless come Wednesday. Either way, I still don’t feel I have a great grasp on all the terms and concepts explained in the paper. In particular, I don’t really understand how the exemplars are selected or how a neural network simulation model is formed.
Monday, March 30, 2009
Monday, March 9, 2009
Blog 7
Optimal Location of Infiltration-Based Best Management Practices for Storm Water Management
By Cristina Perez-Pedini, James F. Limbrunner, and Richard M. Vogel
Summary
The purpose of this study was to introduce a methodology to determine the optimal number and location of infiltration-based BMPs on a watershed to reduce peak flow flood flows at the watershed outlet (Perez-Pedini 441). Although the research can be applied to numerous watersheds, the Aberjona River watershed northwest of Boston, Massachusetts was the focus of this study. This small highly urban catchment was modeled in a spreadsheet and optimized using a genetic algorithm (GA) to determine areas within the watershed where the application of infiltration-based BMPs would be most effective in decreasing flood flows at the catchment outlet (Perez-Pedini 442). A distributed, event-based hydrologic model, along with the SCS curve number method, was employed to determine the runoff and infiltration for each of the 4,533 hydrologic response units (HRUs)
During the optimization process, the overall goal was to locate the HRUs which, if BMPs were applied, would lead to a maximum reduction in peak stream flow at the watershed outlet (Perez-Pedini 444). The end result revealed that the optimal location of the BMPs was a complex function of HRU characteristics and locations. The authors summarize the results by creating a Pareto frontier depicting the number of BMPs, which is synonymous to the project cost, and peak flow reduction.
Discussion
The research conducted by Perez-Pedini has great applicability because (1) it can be applied to nearly any watershed; (2) the analysis could be used to inform policy decisions regarding future storm water management investments; (3) the optimal number of BMPs can be constructed in stages as funds become available, while still achieving optimal reduction during each stage (i.e. When only a few BMPs are located, their optimal locations are subsets of the optimal locations of a much larger set of optimal BMPs.).
By Cristina Perez-Pedini, James F. Limbrunner, and Richard M. Vogel
Summary
The purpose of this study was to introduce a methodology to determine the optimal number and location of infiltration-based BMPs on a watershed to reduce peak flow flood flows at the watershed outlet (Perez-Pedini 441). Although the research can be applied to numerous watersheds, the Aberjona River watershed northwest of Boston, Massachusetts was the focus of this study. This small highly urban catchment was modeled in a spreadsheet and optimized using a genetic algorithm (GA) to determine areas within the watershed where the application of infiltration-based BMPs would be most effective in decreasing flood flows at the catchment outlet (Perez-Pedini 442). A distributed, event-based hydrologic model, along with the SCS curve number method, was employed to determine the runoff and infiltration for each of the 4,533 hydrologic response units (HRUs)
During the optimization process, the overall goal was to locate the HRUs which, if BMPs were applied, would lead to a maximum reduction in peak stream flow at the watershed outlet (Perez-Pedini 444). The end result revealed that the optimal location of the BMPs was a complex function of HRU characteristics and locations. The authors summarize the results by creating a Pareto frontier depicting the number of BMPs, which is synonymous to the project cost, and peak flow reduction.
Discussion
The research conducted by Perez-Pedini has great applicability because (1) it can be applied to nearly any watershed; (2) the analysis could be used to inform policy decisions regarding future storm water management investments; (3) the optimal number of BMPs can be constructed in stages as funds become available, while still achieving optimal reduction during each stage (i.e. When only a few BMPs are located, their optimal locations are subsets of the optimal locations of a much larger set of optimal BMPs.).
Sunday, March 1, 2009
Blog 6
OPTIMIZATION OF REGIONAL STORM-WATER MANAGEMENT SYSTEMS
By Pradeep Kumar Behera, Fabian Papa, and Barry J. Adams
Summary
This paper presents dynamic programming optimization methodologies which seek to minimize the cost associated with detention storage. The objective function that is minimized is constrained by two environmental constraints that the regional outlet must satisfy: 1) environmental regulations for runoff quantity and 2) environmental regulations for runoff quality. The primary cost associated with storm-water detention ponds – and the cost which are used in the objective function - is the land which the detention pond occupies and the initial construction, operation, and maintenance costs. It should be noted that an optimizing methodology is presented for determining the design parameters of a single storm-water management pond and is then expanded (using dynamic programming as mentioned earlier) to a multiple parallel catchment system (Behera 107).
Numerous inputs are entered into the model (you’ll have to read the paper if you want to know what they all are because I’m sure not going to list them all) but there is only one thing minimized and that is the cost of all detention ponds for the desired levels of runoff and pollution control.
Discussion
I really enjoyed this paper because the basic methodologies and techniques used to solve the optimization problem can be easily applied to any other real-world system with any number of catchments (although the paper only used three). In addition, the decision variables and constraints of this model can be easily adjusted to meet different requirements of either the developer, the engineer or various government regulations – something that adds even more flexibility and applicability to the model. My understanding of DP is still incomplete but I hope by the end of the semester we will all have enough expertise in the subject to be able to implement research such as this.
By Pradeep Kumar Behera, Fabian Papa, and Barry J. Adams
Summary
This paper presents dynamic programming optimization methodologies which seek to minimize the cost associated with detention storage. The objective function that is minimized is constrained by two environmental constraints that the regional outlet must satisfy: 1) environmental regulations for runoff quantity and 2) environmental regulations for runoff quality. The primary cost associated with storm-water detention ponds – and the cost which are used in the objective function - is the land which the detention pond occupies and the initial construction, operation, and maintenance costs. It should be noted that an optimizing methodology is presented for determining the design parameters of a single storm-water management pond and is then expanded (using dynamic programming as mentioned earlier) to a multiple parallel catchment system (Behera 107).
Numerous inputs are entered into the model (you’ll have to read the paper if you want to know what they all are because I’m sure not going to list them all) but there is only one thing minimized and that is the cost of all detention ponds for the desired levels of runoff and pollution control.
Discussion
I really enjoyed this paper because the basic methodologies and techniques used to solve the optimization problem can be easily applied to any other real-world system with any number of catchments (although the paper only used three). In addition, the decision variables and constraints of this model can be easily adjusted to meet different requirements of either the developer, the engineer or various government regulations – something that adds even more flexibility and applicability to the model. My understanding of DP is still incomplete but I hope by the end of the semester we will all have enough expertise in the subject to be able to implement research such as this.
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