Neelakantan TR, Pundarikanthan NV (2000) “Neural network-based simulation-optimization model for reservoir operation,” JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT, 126(2) pp. 57-64
The objective of this study was to optimize the operating policy of a reservoir system in Chennai, India. The study considered using a conventional simulation model and an artificial neural network, but determined that the ANN saved many days of computing time. The conventional simulation model was used to generate scenarios to train the ANN. The study utilized a Hooke and Jeeves optimization algorithm. When different inputs generated by Hooke and Jeeves were input to the ANN, the ANN gave the objective function value as the only output, which is reentered into the Hooke and Jeeves model.
The discussion of the trial scenarios and their results made very little sense to me. I thought the standard operation policy was what the reservoir managers were already doing. But the SOP generates a better objective function than the suggested operating policies. I don't see how this is an improvement or a success in the study.
I had a difficult time reading the article because I seemed to miss a lot of critical information at the beginning that had me confused the rest of the article. Really, the authors only wrote a sentence mentioning that a conventional simulation model was created in order to train the ANN, and the ANN was utilized for its quicker solve times. I missed that sentence and then went through the whole article wondering why the authors were neglecting local catchment inflows, transmission losses and percolation losses when those are irrelevant (I think) in an ANN. The full discussion of why a conventional model was necessary and why an ANN was chosen was saved until the very end and then I could go back through and understand a lot more of what was going on earlier in the article. In conclusion I'd say the only fault of the article that stuck out to me was poor order and clarity of the presentation of the material.
Tuesday, March 31, 2009
Wednesday, March 18, 2009
Perez-Pedini et al 2005
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) pp. 441-448
This article examined a methodology for locating the optimal placement of infiltration-based stormwater BMPs. The goal was to reduce the peak runoff flow based on implementing a budget of 25 to 400 BMPs (the results would be a pareto frontier with the number of BMPs implemented on one axis and the percent reduction of peak flow on the other axis).
The researchers modeled a developed watershed as a grid made up of 4,533 hydrologic response units (HRUs) which could be modeled as having a BMP implemented or not. If it was decided to implement a BMP in an HRU, then the CN of the HRU would be decreased by 5 units. The researchers planned to run a genetic algorithm to decide where to place the BMPs. However, testing 4,533 HRUs for possible BMP implementation was too large of a decision space for a GA to handle, and the researchers had to make some limitations to reduce this decision space. They made the decision to limit possible BMPs to HRUs with low permeability (high CN) and HRUs which were in close proximity to a river. The first limitation makes sense that the most effective BMP would be removing an impervious surface, and the second limitation of being close to a river makes sense because it will likely have a larger volume of water running over it than an HRU at a high point in the watershed.
The results of the study find that the first few BMPs implemented have a high reduction in peak flow per BMP, and as the number of BMPs implemented increases, the reduction in peak flow per BMP decreases. This makes sense as the GA should automatically target the very best locations first and then the less effective HRU locations for BMPs will get selected.
I was surprised that the article mentioned that the GA didn't find the optimal solution, as discovered when they played around with the results a little. If they say their results are acceptable because they are near-optimal, I'll believe them, but I would've liked to see this proven a little more in the paper. Perhaps that is for another paper. Another component I would've liked to see expanded on was their study of possible commonalities between selected HRUs. They concluded that there were no dominating characteristics which could identify good HRUs without use of the GA, and I would be interested to read an article which examined this in detail. I suppose since they found no dominating characteristics, the article results might not be interesting enough to warrant an article.
This article examined a methodology for locating the optimal placement of infiltration-based stormwater BMPs. The goal was to reduce the peak runoff flow based on implementing a budget of 25 to 400 BMPs (the results would be a pareto frontier with the number of BMPs implemented on one axis and the percent reduction of peak flow on the other axis).
The researchers modeled a developed watershed as a grid made up of 4,533 hydrologic response units (HRUs) which could be modeled as having a BMP implemented or not. If it was decided to implement a BMP in an HRU, then the CN of the HRU would be decreased by 5 units. The researchers planned to run a genetic algorithm to decide where to place the BMPs. However, testing 4,533 HRUs for possible BMP implementation was too large of a decision space for a GA to handle, and the researchers had to make some limitations to reduce this decision space. They made the decision to limit possible BMPs to HRUs with low permeability (high CN) and HRUs which were in close proximity to a river. The first limitation makes sense that the most effective BMP would be removing an impervious surface, and the second limitation of being close to a river makes sense because it will likely have a larger volume of water running over it than an HRU at a high point in the watershed.
The results of the study find that the first few BMPs implemented have a high reduction in peak flow per BMP, and as the number of BMPs implemented increases, the reduction in peak flow per BMP decreases. This makes sense as the GA should automatically target the very best locations first and then the less effective HRU locations for BMPs will get selected.
I was surprised that the article mentioned that the GA didn't find the optimal solution, as discovered when they played around with the results a little. If they say their results are acceptable because they are near-optimal, I'll believe them, but I would've liked to see this proven a little more in the paper. Perhaps that is for another paper. Another component I would've liked to see expanded on was their study of possible commonalities between selected HRUs. They concluded that there were no dominating characteristics which could identify good HRUs without use of the GA, and I would be interested to read an article which examined this in detail. I suppose since they found no dominating characteristics, the article results might not be interesting enough to warrant an article.
Sunday, March 1, 2009
Behera, P, Papa, F., Adams, B (1999) “Optimization of Regional Storm-Water Management Systems” Journal of Water Resources Planning and Management, 125
Detention ponds are useful engineering tools to help mitigate the impacts of urban development. Land developers see ponds as loss of developable land and extra added costs. So when designing a pond, both economic and environmental goals need to be considered.
The paper demonstrates a methodology to design a detention pond with the decision variables being the storage volume, pond release rate, and pond depth. The constraints of the model include quality constraints for pollution control and runoff control. The objective function is to minimize costs which includes the value of the land used and the construction cost involved.
There was a discussion on isoquants in the paper which I didn't understand at all.
In this paper, the authors have designed a methodology of optimizing for one pond, and they apply this methodology to a larger scale optimization problem of designing a multiple catchment system. For multiple catchments, they use dynamic programming to solve.
It is interesting to see that by not constraining each catchment to certain parameters but simply restricting the end result of the system, they were able to achieve a 13% reduction in cost.
The paper demonstrates a methodology to design a detention pond with the decision variables being the storage volume, pond release rate, and pond depth. The constraints of the model include quality constraints for pollution control and runoff control. The objective function is to minimize costs which includes the value of the land used and the construction cost involved.
There was a discussion on isoquants in the paper which I didn't understand at all.
In this paper, the authors have designed a methodology of optimizing for one pond, and they apply this methodology to a larger scale optimization problem of designing a multiple catchment system. For multiple catchments, they use dynamic programming to solve.
It is interesting to see that by not constraining each catchment to certain parameters but simply restricting the end result of the system, they were able to achieve a 13% reduction in cost.
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