Monday, March 30, 2009

The Ocho

Neelakantan TR, Pundarikanthan NV (2000) “Neural network-based simulation-optimization model for reservoir operation,” J. of Water Resource Planning and Management, 126(2) pp. 57-64.

Chennai (formerly known as Madras) is a major city in the Tamil Nadu region of southern India. Although it receives 51 in/yr of rainfall, this mostly comes during the three-month monsoon, so the city is prone to droughts. The city's water is supplied by aa series of reservoirs, which have been typically regulated through standard operating policy. This paper attempts to study the reservoir management through the implementation of a neural network model.

The optimisation approach used was to minimize the overall deficit index, where the ODI is the sum of the squares of the deficits of all the reservoirs, after the deficits have been normalized based on the size of demand from the different reservoirs. Then, the two scenarios ( one with the current system and one with proposed reservoirs included) were optimised to test their performance.

Optimisation was done by a neural network based optimisation, where a neural network is an algorith based on how a brain works. First, the neural network must be trained, and then optimisation is done. The authors concluded that neural-networks can optimizee the workings of large water resource systems.


This paper was interesting in that it uses a new technique to try to solve an existing problem. Personally, I didn't have nearly enough knowledge of neural netwoks to understand most of what this paper is talking about.

My problems with (what I could understand of) this paper are:
I'm not really sure why the authors decided to force the system to maintain equity among the reservoirs. It seems like leaving this out might allow a more optimal solution. Also, I feel like the process of training sounded way too complicated to ever come into widespread use.

Monday, March 9, 2009

Reading #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) pp. 441-448.

Traditionally, stormwater has been controlled systems of detention basins. However, these structures are expensive to construct, so a growing trend is to use low impact development (LID) in the form of infiltration basins to curb runoff.

The Aberjona River watershed in Massachusetts was studied. The are was divided into 120 m X 120 m squares and elevations, land use, and flow paths were found for each cell using GIS. The NRCS curve number method was used to find the infiltration and runoff for each square during a specific event. The optimisation technique was setup so that each square would be represented by a binary variable representing whether the infiltration basin will be built there; if true, the CN for that cell would be decreased by five, representing an increase in infiltration. Then a genetic algorithm was used to find those cells which had the greatest impact on reducing the runoff. The algorithm was run several times with different numbers of infiltration ponds to develop a Pareto-optimal curve for infiltration ponds vs reduction in runoff.

I found that this article presented an interesting problem which was solved using genetic algorithms. The techniques used seemed logical, and the results seem to make sense. The methods presented in this article should be a valuable tool for community planners hoping to use infiltration ponds for flood control.

In the future, these methods should be used on a system consisting of detention and infiltration ponds, since almost no urban stormwater management systems are going to be completely infiltration. Also, a technique that can take into account how the pond affects runoff as well as water quality might be useful.

Monday, March 2, 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) pp. 107-114.

In this article, the authors discuss their use of optimization techniques to calculate the required geometry for the detention ponds in a watershed on a system-wide scale in order to ensure the discharge at the outlet met quality and flow requirements while minimizing the overall cost of building all the detention basins. For each basin, the authors used decision variables representing the storage volume, depth, and release rate for each pond. Constraints included the pollution reduction and the runoff control performance. The authors used isoquant curves (developed by Papa and Adams in 1997), which show the pollution control of a detention pond as a function of the ponds storage capacity and release rate.

In order to optimize the entire system, individual detention basins were allowed to discharge water that didn't meet flood attenuation or pollution requirements, as long as the requirements were met at the outlets. This allowed them to minimize the cost of all of the detention basins since the various detention basins each had different construction and real estate costs. Using their methods, the authors were able to reduce the cost of constructing detention basins for a system containing three basins by $100K.

I thought this was a very well written article explaining the authors' use of optimization for a practical problem which water resource engineers are having to solve all the time. I found the isoquants to be an interesting solution to the problem of modeling the water quality.

The methods used in the paper could be a valuable tool for city governments and developers developing huge tracts of land. I wonder whether having some basins releasing higher quality water and some releasing lower quality water would be permitted by the ordinances and standards regulating discharges.