What are you doing Dave?

Neelakantan, T.R., Pundarikanthan, N.V. (2000) "Neural Network Based Simulation-Optimization Model for Reservoir Operation" J Water Res. Plan. Mgmt. 126(2):57-64

The authors of this paper used a combined neural network simulation optimization approach to study the optimal management policy for a series of water supply reservoirs in Chennai, India. The neural network based approach provided a large advantage over traditional simulation approaches in terms of analytical speed. The increased computation speed came at the expense of supporting data in that the neural network was designed to provide output in terms of the final value of the optimization function and did not provide any information about the status of other variable within the system. To overcome some of this the authors used the optimal and near optimal results from the neural network as fodder for more traditional Hooke and Jeeves optimization that also allowed them to gather information regarding delivery shortfalls, spillage events and evaporative losses in the system.

To be honest I didn't completely follow the deficiency index system that the authors put forward as an evaluative criteria the inclusion of an equity measure in the index didn't make sense to me on first or second readings. I'm not clear on why equity of shortfalls was included in the evaluation, is this an institutional constraint? Otherwise the paper was fairly impressive, the relative speed with which the neural network was able to handle the neccissary simulations for each scenario (36,000-54,000 runs) is worth noting. The idea of using multiple methods for analysis of water resource problems also seems to be a re-occuring theme in the papers so far. By using the neural network to do the "heavy lifting" the authors were able to find a collection of solutions that could then be further analysed by more traditional methods.

Stormwater Abatement

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 week’s paper represents a move away from the employment of large scale projects and land use for controlling storm water runoff in favor of multiple smaller scale infiltration based practices. Perez-Pedini et. al studied a watershed in Massachusetts in an attempt to apply a genetic algorithm approach to finding the optimal method for controlling storm water runoff. They began by dividing the watershed into ~4500 hydrologic response units that could be characterized in terms of their overall contribution to runoff during storm events. They calibrated and validated their distributed model using two events from 2002 and 2003. By comparing the predicted hydrograph at the output of the watershed to the hydrograph from the corresponding storm events they found that the distributed model based on hydrologic response units was able to accurately predict runoff volumes within the watershed.

Perez-Pedini et al then selected ~1900 hydrologic response units within the watershed that met the criteria of either:
1) Being highly impervious (CN > 89) or
2) Were likely to contribute to runoff due to close physical proximity to a discharge stream.
The selected hydrologic response units were used as the fodder for optimizing best management practices within the watershed using a genetic algorithm approach. The genetic algorithm produced a series of possible solutions to achieving a targeted reduction in storm water runoff using the 2003 storm simulation. From these possible solutions Perez-Pedini et. al were able to build a trade off curve that effectively related costs to runoff reduction.

The creation of the trade off curve ties in nicely with a point that was discussed in one of the opening papers of the semester regarding the nature of public (wicked) problems and the role of optimization models. While the GA approach was able to find numerous possible solutions for reducing storm water runoff within the watershed the end result was the creation of a “guide” rather than a absolute answer. Balancing the costs of construction against the needs for runoff reduction requires more direction and inputs than can be easily programmed into a GA or any other optimization method. I also enjoyed that this paper focused on smaller BMPs, e.g. swales and curb cuts, rather than large detention ponds. Detention pond based BMPs can work will in developing areas, where large amounts of land are available and land costs are lower but the BMPs tested in this paper are applicable to areas that are already highly developed. Using the methods of this paper makes it more feasible to redevelop storm water management in highly developed areas to reduce runoff and lessen the impacts of existing developments on stream systems.