Neelakantan TR, Pundarikanthan NV (2000) “Neural network-based simulation-optimization model for reservoir operation,” Journal of Water Resources Planning and Management, 126(2) pg. 57-64.
Summary
This study was to act as an effort to develop the planning model for a reservoir operation. The reservoir operation model was based on a simulation-optimization approach, which was chosen for time consumption reasons. Simulation modeling was practical for an operation schedule because of the way that the modeling could accurately signify the reservoir’s qualities and characteristics that may be too complex or difficult to model. The model was also used to portion water use in the reservoir for reasons of relieving future drought conditions. The location that the model was tested on was in the Chennai water supply system in India.
The nonlinear programming model that the authors chose to use was the Hooke and Jeeves unconstrained linear programming model. This model included a “neural-network-based simulation sub-model.” This was introduced as a model that will closely mimic brain neurology.
The research took multiple steps to identify the optimal reservoir schedule. Firstly, the network was adjusted to simulate the accurate operation of the reservoir system. Secondly, the neural network model was built and linked as the sub-model, which was used together to “screen the operation policies.” Lastly, the optimization stage of the model was conducted. The operation policy that will yield the better objective function value is chosen from the dual simulation-optimization results. These results will further be filtered through using the “conventional simulation model”, versus the neural network simulation model.
Discussion
This article was interesting to me, because I think reservoir operation policy techniques are intriguing. The results of the model were found to be satisfactory, compared to the conventional simulation-optimization model. The authors said that a certain amount of exemplars is necessary for the network to be trained accurately.
This method was also found to be quite flexible and can easily adjust to complex operations. The authors seemed to have no superiority towards a Hooke and Jeeves nonlinear optimization model. They said that other optimization models can be used in place of Hooke and Jeeves. So what I would like to know is how much the results would be modified by using a different nonlinear optimization model. We all saw in our homework that results can vary a significant amount when comparing 5 distinctive nonlinear optimization models.
Sunday, March 29, 2009
Sunday, March 8, 2009
Assignment 7
Perez-Pedini C, Limbrunner JF, Vogel RM (2005) “Optimal location of infiltration-based best management practices for storm water management,” JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT, 131(6) pg. 441-448.
Summary
This essay was to look at best management practices (BMPs) based on infiltrations versus storage-based to mitigate flood and water quality damages. The objective of their model was to minimize the peak flows at the watershed outlets by determining optimal locations of the BMPs. The event-based hydrologic model used for the optimization was expanded upon the Natural Resource Conservation Services Curve Number method. This model was incorporated in Microsoft Excel, using a genetic algorithm methodology. The genetic algorithm established areas in which the BMP would be most efficiently used in order to decrease the flood flow. The topographical constraints incorporated in the model was developed by ArcGis, and then was brought into Excel. There was also curve number and slope constraints. The algorithm used for the flow direction simulation was the D8 algoritm due to simplicity.
The model was used on a watershed called Averjona River, near Boston. This was a highly urban watershed. The model was calibrated using two distinct storm events. However, the authors concluded that the output of the model was not interrelated to the observed, long term data. Therefore the authors decided that there was a problem with the groundwater storage characteristics in the formulation of the model. Another disadvantage of the model is the time step has the most control over the entire model in terms of locations for the infiltration-based BMPS. It was also found that optimal locations of BMPS were subsets of larger sets of BMPs.
Discussion
This article was interesting to me, because I think ArcGIS is a powerful tool that should be implemented when using the Natural Resources Conservation Services Curve Number method. It would make the Curve Number calculations easy, and has accurate topographical information readily available. Furthermore, this optimization model would be quite useful in practice.
Even though I’m still somewhat new to the concept of genetric algorithms, I noticed the authors noted that the D8 algorithm was used for simplicity. This could definitely be a fault of the research, and definitely something I would expand on if I were to continue this research. Also, I would like to see how the output of this infiltration-based BMP model compares to a storage-based technique. Lastly, I would conclude that this model wouldn’t be very replicable.
Summary
This essay was to look at best management practices (BMPs) based on infiltrations versus storage-based to mitigate flood and water quality damages. The objective of their model was to minimize the peak flows at the watershed outlets by determining optimal locations of the BMPs. The event-based hydrologic model used for the optimization was expanded upon the Natural Resource Conservation Services Curve Number method. This model was incorporated in Microsoft Excel, using a genetic algorithm methodology. The genetic algorithm established areas in which the BMP would be most efficiently used in order to decrease the flood flow. The topographical constraints incorporated in the model was developed by ArcGis, and then was brought into Excel. There was also curve number and slope constraints. The algorithm used for the flow direction simulation was the D8 algoritm due to simplicity.
The model was used on a watershed called Averjona River, near Boston. This was a highly urban watershed. The model was calibrated using two distinct storm events. However, the authors concluded that the output of the model was not interrelated to the observed, long term data. Therefore the authors decided that there was a problem with the groundwater storage characteristics in the formulation of the model. Another disadvantage of the model is the time step has the most control over the entire model in terms of locations for the infiltration-based BMPS. It was also found that optimal locations of BMPS were subsets of larger sets of BMPs.
Discussion
This article was interesting to me, because I think ArcGIS is a powerful tool that should be implemented when using the Natural Resources Conservation Services Curve Number method. It would make the Curve Number calculations easy, and has accurate topographical information readily available. Furthermore, this optimization model would be quite useful in practice.
Even though I’m still somewhat new to the concept of genetric algorithms, I noticed the authors noted that the D8 algorithm was used for simplicity. This could definitely be a fault of the research, and definitely something I would expand on if I were to continue this research. Also, I would like to see how the output of this infiltration-based BMP model compares to a storage-based technique. Lastly, I would conclude that this model wouldn’t be very replicable.
Sunday, March 1, 2009
Assignment 6
Behera, P, Papa, F., Adams, B (1999) “Optimization of Regional Storm-Water Management Systems” Journal of Water Resources Planning and Management, 125(2) pg. 107-114.
Summary
This essay is an expansion of a previous study, authored by the same individuals. It introduced design of ponds that will follow constraints of runoff control along with quality control. There is always a problem of designing multiple storm-water management ponds in a site that will outfall to the same point. The writers extended their previous study’s methodology by introducing a dynamic programming model to three parallel catchments, each of which had their own detention pond.
Obviously, the objective function of this model was to minimize cost of the ponds in each catchment, which included initial construction cost, operation cost, and maintenance costs. The variables that were optimized were the storage volume, location, pond depth, and release rate. The major constraints of the model were the runoff control constraint and the pollution control constraint.
The cost of the individual SWM pond was the sum of pond surface area multiplied by the land value and the product of the volume of the pond and value of construction and OMR costs. Furthermore, this cost was a function of the active storage volume of the pond, the pond depth, and the surface area of the catchment.
The performance (runoff and pollution) constraints were that the trunk sewer (discharge point) was required to meet or exceed a certain level of both quantity and quality control. The authors chose to optimize the blend of catchment controls in the multiple catchment system, compared to providing uniform control. The runoff control constraint was a function of storage and release rate, and the pollution control constraint was dependent on the settling velocity and the mass of suspended solids. This constraint was a function of the three decision variables: pond depth, release rate, and storage volume. Since these two major constraint equations were quite complex in the sense of isolating the decision variables, “isoquants” were generated to find the optimal solution for the three ponds.
Discussion
This paper was significant due to the large interest in designing storm-water management ponds in optimized fashions. Land developers wish to maximize the developable land, leaving little to be used for detention ponds. Therefore there is a high interest in minimizing the cost of the pond, and furthermore minimizing the storage volume (volume is proportional to cost). There is often pollution constraints set on an outlet that will be released back to the rivers, and many designers just choose to design the individual pond to follow that constraint. These authors decided to implement a model that would allow each pond to have a lower runoff and pollution control percentage; however the entire system (at the outfall point) will reach the minimal value. The authors proved that this methodology in fact decreased the cost, as shown in their example.
The assumptions that were made for the model were meteorological conditions, and that the ponds all outfall to eventually the same location. It is important that someone building research on this model understand that a multiple catchment pond design cannot be designed with this methodology if these assumptions are not satisfied. The authors did a great job on this research, and I cannot find any faults. If I were to build on this research, I would look into incorporating land use patterns with the model, creating the most developable land.
Summary
This essay is an expansion of a previous study, authored by the same individuals. It introduced design of ponds that will follow constraints of runoff control along with quality control. There is always a problem of designing multiple storm-water management ponds in a site that will outfall to the same point. The writers extended their previous study’s methodology by introducing a dynamic programming model to three parallel catchments, each of which had their own detention pond.
Obviously, the objective function of this model was to minimize cost of the ponds in each catchment, which included initial construction cost, operation cost, and maintenance costs. The variables that were optimized were the storage volume, location, pond depth, and release rate. The major constraints of the model were the runoff control constraint and the pollution control constraint.
The cost of the individual SWM pond was the sum of pond surface area multiplied by the land value and the product of the volume of the pond and value of construction and OMR costs. Furthermore, this cost was a function of the active storage volume of the pond, the pond depth, and the surface area of the catchment.
The performance (runoff and pollution) constraints were that the trunk sewer (discharge point) was required to meet or exceed a certain level of both quantity and quality control. The authors chose to optimize the blend of catchment controls in the multiple catchment system, compared to providing uniform control. The runoff control constraint was a function of storage and release rate, and the pollution control constraint was dependent on the settling velocity and the mass of suspended solids. This constraint was a function of the three decision variables: pond depth, release rate, and storage volume. Since these two major constraint equations were quite complex in the sense of isolating the decision variables, “isoquants” were generated to find the optimal solution for the three ponds.
Discussion
This paper was significant due to the large interest in designing storm-water management ponds in optimized fashions. Land developers wish to maximize the developable land, leaving little to be used for detention ponds. Therefore there is a high interest in minimizing the cost of the pond, and furthermore minimizing the storage volume (volume is proportional to cost). There is often pollution constraints set on an outlet that will be released back to the rivers, and many designers just choose to design the individual pond to follow that constraint. These authors decided to implement a model that would allow each pond to have a lower runoff and pollution control percentage; however the entire system (at the outfall point) will reach the minimal value. The authors proved that this methodology in fact decreased the cost, as shown in their example.
The assumptions that were made for the model were meteorological conditions, and that the ponds all outfall to eventually the same location. It is important that someone building research on this model understand that a multiple catchment pond design cannot be designed with this methodology if these assumptions are not satisfied. The authors did a great job on this research, and I cannot find any faults. If I were to build on this research, I would look into incorporating land use patterns with the model, creating the most developable land.
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