Article Reference:
T.R Neelakantan, N.V. Pundarikanthan, Neural Network-Based Simulation-Optimization Model for Reservoir Operation, Mar-Apr 2008, Journal of Water Resources Planning and Management
Summary:
The paper focuses on planning a model for reservoir operation which uses simulation-optimization approach. Author takes Chennai water supply as the study area. Reservoir planning was improved by demand management using the hedging rule which distributes the deficits for longer time by rationing water supply. For defining and optimizing the decision variables for hedging rule, the neural network based simulation model was used as a sub model to the Hookes and Jeeves programming model. Simulation analysis requires a operation policy, a standard policy is the optimal policy where the objective is to minimize the total deficit over the time. Hedging and rationing rule helps in distributing the deficit over a longer time. The total storage in the reservoir is divided into different zones and based on the falling level in each zone the release target can be fixed. For the study the reservoir is divided into four zones and the storage levels are S1, S2 and S3. This simulation model is sent as sub model to the Hookes and jeeves nonlinear programming model which passed management decision vectors to the simulation model to find the objective function as an output.
The whole study has been carried out in various stages. In the first stage the back propagation neural network is used to simulate the reservoir operation. Further in the second stage this neural network was fed as sub model to Hookes and Jeeves, this combined simulation-optimization model is used to screen the operation policies. In the third stage the best solution of the optimization is fine tuned using the conventional simulation-optimization model.
Authors have considered Chennai water supply as the study area. Chennai’s water supply is catered through three reservoirs- Poondi, Cholavaram and Red hills and ground water sources. The reservoirs are fed only by the north east monsoon which lasts only for three months. Due to increased extraction of ground water, the water table fell rapidly and there are traces of sea water intrusion. They have planned for the augmentation of the system by Krishna water. With the new excess water they have proposed to use Chembarambakkam reservoir as one of the terminal reservoirs. As Chennai is facing severe drought conditions the criteria would be to reduce the shortfalls as much as possible. During the drought condition the water supply managers prefer smaller shortfalls than dealing with larger ones and therefore they are following the method of hedging and rationing. Distribution of deficits should be done in such a way that they are spread for a longer period and with lesser magnitude. To do the above deficit index (sum of squared deficits) is followed which should be minimum among the other options. With the augmentation of Krishna water supply, equity of reservoir levels for two parallel reservoirs (Red Hills and Chembarambakkam) needs to be maintained. So the demand ratio needs to be 1:x and the deficit ratio to 1:x. As deficit index is the second order parameter the ratio between the two reservoirs needs to be 1:x^2.
The simulation model uses the mass balance principle and follows the constraints on canal capacity, minimum and maximum storage capacity, and evaporation loss and so on. The objective function is to minimize the deficit index by changing the decision management variables S1, S2, and S3 (Storage levels). The study was carried out using different scenarios considering different source and terminal reservoirs, to estimate which combination proves to improve the objective function. Inflow data into the reservoirs are studied to see the performance of the reservoir. Authors have considered catchment inflow, percolation loss, and transmission losses to be negligible. The study was carried out with different supply levels – full demand, 80% demand 75% demand, 67% demand and 50% demand.
Discussion:
This paper was interesting as the study deals with how we can optimize the operating policy based on the hedging rule. As this model is flexible to include various reservoirs and more complex system, I think it is a good model to carry out such a study. It has the flexibility of changing the nonlinear programming model instead of Hookes and Jeeves, which would be interesting to explore.
Monday, March 30, 2009
Monday, March 9, 2009
Assignment # 7
Article Reference:
Cristina Perez-Pedini, James F. Limbrunner, Richard M. Vogel, Optimization Location of Infiltration Based Best Management Practices for Storm Water Management, Nov-Dec 2008, Journal of Water Resources Planning and Management
Summary:
The present paper discusses about the optimization of infiltration based best management practices of Aberjona river watershed which is carried out by combination of hydrologic model and genetic algorithm. Main objective of the study was to optimize the number and location of infiltration based BMP thereby reducing the peak flow at the outlet of the watershed.
The hydrologic model was built using the SCS approach (CN method). This model was optimized using GA to find the areas within the watershed where the infiltration based BMP would be more effective to reduce the impacts of the increased peak flow. By considering different optimization results, the trade off curve between flood flow reduction and program budget. The paper considers the option of optimizing the BMP on the upslope so that it is more effective at the downstream flood protection. The whole catchment is modeled as the Hydrologic Response Units (HRU). It is considered that each HRU unit drains to nearest upslope HRU’s and a maximum HRU’s which can drain into are limited to 7. It is seen that only at most 2 HRU’s drain into adjacent upslope HRU. The stream network collects the runoff from the upstream HRU and routes to the watershed outlet. The hydrologic parameters were developed using ArcGIS and the cell to cell connectivity is done using D8 algorithm. BMP is modeled using a binary variable; introduction of BMP decreases the CN of the HRU. In the scope of this paper, they have assumed only the planning of the whole watershed system instead of designing of each BMP. We could further take the inputs from this methodology to study the watershed which have larger impact on the flood peak flow and apply the particular BMP to each HRU. The study considers only single type of BMP for the whole study as project cost would increase if different BMP types were considered. This study did not consider the cost implication of setting a BMP in HRU with different land use and surface conditions.
The hydrologic model is modified so that it calculated the cumulative water at the HRU by considering the sum of the precipitation, excess groundwater at the HRU and excess runoff from the adjacent down slope HRU and subtracting the cumulative initial abstraction. As defined by CN method, initial abstraction is defined as a fraction of the soil storage capacity. Combining above mentioned procedures, cumulative runoff, and cumulative infiltration are estimated. Ground water storage is calculated as sum of storage of previous time step, cumulative infiltration and sum of the previous time step base flow from the adjacent upslope. Stream channel is routed to carry the runoff and the base flow to the watershed outlet with a time lag. One of the model disadvantages is that time step is considered to be the major control of the system, so the time step needs to be calibrated such that it meets both the peak of flow and the basin lag time.
The hydrologic model was calibrated using the observed and modeled hydrograph. The model could not be used to model long term recession which impacts the groundwater storage and characteristics of the outflow. The objective of the study was to choose the HRU where if BMP applied would reduce the peak flow at the watershed outlet. As only one type of BMP is considered therefore project cost is directly proportional to the number of BMP selected. The study used GA approach to analyze different groups of BMP location so that the peak flow of a October storm is reduced. Initially every HRU was considered to be having BMP which was found out to be infeasible, so they restricted the location of BMP based on the CN which targets the HRU which is more impervious and which will eventually cause more runoff into the stream network. GA was used to maximize the peak flow reduction for a given budget constraint that total number of BMPs selected should not exceed than that of the preselected.
By continuous optimization with varied number of BMP’s i.e varied costs, a tradeoff (Pareto frontier) curve was obtained between the cost and the peak flow reduction. All the solutions below this curve are feasible and all the solutions above the curve as infeasible. The paper recommends for a distributed physical representation of the basin for carrying out BMP planning analysis. It was concluded that for the present area of study by applying BMP at not less than 200 HRU we can achieve 20% reduction in peak flow. It is illustrated by the trade off curve that with the increase in the number of BMP’s the returns are decreased. Author suggests using the incremental approach in designing which is BMP is introduced at places which are most affected by the storm and then moving on to the places which aren’t that critical, such an approach would be helpful in targeting resource management.
Discussion:
The paper was very helpful in giving an insight of how the GA optimization could be carried out. As the authors specify that physical representation of the basin is required for the planning, taking up a research to study that and comparing the results with this model which did not take into the consideration without a physical model would be worth knowing. The other aspect would be considering how the cost optimization results will be affected by considering different BMP’s based on the land use pattern of each HRU.
Cristina Perez-Pedini, James F. Limbrunner, Richard M. Vogel, Optimization Location of Infiltration Based Best Management Practices for Storm Water Management, Nov-Dec 2008, Journal of Water Resources Planning and Management
Summary:
The present paper discusses about the optimization of infiltration based best management practices of Aberjona river watershed which is carried out by combination of hydrologic model and genetic algorithm. Main objective of the study was to optimize the number and location of infiltration based BMP thereby reducing the peak flow at the outlet of the watershed.
The hydrologic model was built using the SCS approach (CN method). This model was optimized using GA to find the areas within the watershed where the infiltration based BMP would be more effective to reduce the impacts of the increased peak flow. By considering different optimization results, the trade off curve between flood flow reduction and program budget. The paper considers the option of optimizing the BMP on the upslope so that it is more effective at the downstream flood protection. The whole catchment is modeled as the Hydrologic Response Units (HRU). It is considered that each HRU unit drains to nearest upslope HRU’s and a maximum HRU’s which can drain into are limited to 7. It is seen that only at most 2 HRU’s drain into adjacent upslope HRU. The stream network collects the runoff from the upstream HRU and routes to the watershed outlet. The hydrologic parameters were developed using ArcGIS and the cell to cell connectivity is done using D8 algorithm. BMP is modeled using a binary variable; introduction of BMP decreases the CN of the HRU. In the scope of this paper, they have assumed only the planning of the whole watershed system instead of designing of each BMP. We could further take the inputs from this methodology to study the watershed which have larger impact on the flood peak flow and apply the particular BMP to each HRU. The study considers only single type of BMP for the whole study as project cost would increase if different BMP types were considered. This study did not consider the cost implication of setting a BMP in HRU with different land use and surface conditions.
The hydrologic model is modified so that it calculated the cumulative water at the HRU by considering the sum of the precipitation, excess groundwater at the HRU and excess runoff from the adjacent down slope HRU and subtracting the cumulative initial abstraction. As defined by CN method, initial abstraction is defined as a fraction of the soil storage capacity. Combining above mentioned procedures, cumulative runoff, and cumulative infiltration are estimated. Ground water storage is calculated as sum of storage of previous time step, cumulative infiltration and sum of the previous time step base flow from the adjacent upslope. Stream channel is routed to carry the runoff and the base flow to the watershed outlet with a time lag. One of the model disadvantages is that time step is considered to be the major control of the system, so the time step needs to be calibrated such that it meets both the peak of flow and the basin lag time.
The hydrologic model was calibrated using the observed and modeled hydrograph. The model could not be used to model long term recession which impacts the groundwater storage and characteristics of the outflow. The objective of the study was to choose the HRU where if BMP applied would reduce the peak flow at the watershed outlet. As only one type of BMP is considered therefore project cost is directly proportional to the number of BMP selected. The study used GA approach to analyze different groups of BMP location so that the peak flow of a October storm is reduced. Initially every HRU was considered to be having BMP which was found out to be infeasible, so they restricted the location of BMP based on the CN which targets the HRU which is more impervious and which will eventually cause more runoff into the stream network. GA was used to maximize the peak flow reduction for a given budget constraint that total number of BMPs selected should not exceed than that of the preselected.
By continuous optimization with varied number of BMP’s i.e varied costs, a tradeoff (Pareto frontier) curve was obtained between the cost and the peak flow reduction. All the solutions below this curve are feasible and all the solutions above the curve as infeasible. The paper recommends for a distributed physical representation of the basin for carrying out BMP planning analysis. It was concluded that for the present area of study by applying BMP at not less than 200 HRU we can achieve 20% reduction in peak flow. It is illustrated by the trade off curve that with the increase in the number of BMP’s the returns are decreased. Author suggests using the incremental approach in designing which is BMP is introduced at places which are most affected by the storm and then moving on to the places which aren’t that critical, such an approach would be helpful in targeting resource management.
Discussion:
The paper was very helpful in giving an insight of how the GA optimization could be carried out. As the authors specify that physical representation of the basin is required for the planning, taking up a research to study that and comparing the results with this model which did not take into the consideration without a physical model would be worth knowing. The other aspect would be considering how the cost optimization results will be affected by considering different BMP’s based on the land use pattern of each HRU.
Monday, March 2, 2009
Assignment # 66
Article Reference:
Pradeep Kumar Behera, Fabian Papa, Barry J. Adams, Optimization of Regional Storm Water Management Systems, 1999, Journal of Water Resources Planning and Management
Summary:
The paper focuses on the optimization methodology to determine the detention pond storage volume, release rate and pond depth for a single storm management pond, to meet the environmental regulations of runoff quality and quantity. The above mentioned methodology was extended to evaluate the storm management parameter for multiple catchments using dynamic programming.
With the increase in urbanization the importance of dealing with the uncontrolled discharge of runoff which results in adverse impacts like bank erosion, flooding and increasing the chances of pollution. Best management practices (BMP) are considered to attenuate the peak flow, control the runoff volume and control runoff quality, so as to maintain the predevelopment runoff conditions or meet the environmental regulations for runoff quality and quantity. The present study focuses on using detention pond as the storm management control measure. Volume and quality of runoff controlled is largely dependent on the storage volume of the detention pond. Though Storm water management pond (detention pond) is considered as the measure to mitigate the adverse effects, land developers and municipalities consider such ponds to be loss of developable land and added cost of construction and maintenance. The objective of such developers is to minimize the cost associated to the SWM ponds with satisfying the environmental regulations. Typical cost of concerns associated to these SWM ponds are cost of land occupied by the ponds, operation, maintenance and repair (OMR) costs. The present study is to optimize the geometry of the SWM pond to meet the runoff volume control and pollution control to a single catchment, further the problem was formulated for a multiple catchments to optimize design the parameters such that they meet the overall runoff control and pollution control.
The model was formulated considering three parallel catchments having similar metrological characteristics and with SWM ponds which are located upstream of the outlet into the common collection system. Different levels of quality and quantity control was considered at different catchments such that the final or overall control of quality and quantity at the outfall was met according to the environmental regulations. By considering the above mentioned methodology it will be easy to optimize the solution for minimizing the cost of implementing the SWM ponds for all the catchments by providing optimal blend of individual catchments controls than considered uniform control. Both runoff quantity and quality control constraint at the outfall are considered to be greater than the area-weighted average level of pollution/volume control at each catchment. In the study they have not considered the local conveyance cost as normally the SWM ponds are situated in close proximity to the collection system and they did not consider differential cost associated to different rates of release from the pond. In the present work, SWM ponds is considered to be extended detention dry pond type which stores catchment runoff until the entire volume is used. Decision variable in the optimization of the problem are the storage volume, release rate and the pond depth which estimates the extent of runoff quantity and quality.
For carrying out optimizing of SWM pond in a single catchment they have considered 90% runoff control isoquant and 50% pollution control isoquant with constant pond depth to study the strategy of how the release rates and storage volume of the pond vary. As pond depth is also a decision variable in optimizing the solution, a range of depths are analyzed to produce corresponding optimal storage volume and release rates. Therefore by considering the relationship between pond cost and pond depth, an optimal pond depth is estimated which meets the pollution and runoff control constraints.
For optimizing a SWM pond for multiple catchments, DP is considered as the optimization tool which divides the multiple catchment problem into series of individual problems, then combining the solution of each small(stages) problem to the overall (multiple catchment) problem. Overall pollution and runoff control is not violated even if we decompose the model into simpler stages. After the computation of each stage is carried out, track back procedure is followed to find the least cost of the SWM pond considering the design variables.
Discussion:
I found the paper very informative as it gives an idea on how a single catchment SWM optimization and multiple catchment optimizations are done. I could not find any particular problem of concern. But it would be interesting to carry out further research by considering all the assumptions they made in the present study.
Pradeep Kumar Behera, Fabian Papa, Barry J. Adams, Optimization of Regional Storm Water Management Systems, 1999, Journal of Water Resources Planning and Management
Summary:
The paper focuses on the optimization methodology to determine the detention pond storage volume, release rate and pond depth for a single storm management pond, to meet the environmental regulations of runoff quality and quantity. The above mentioned methodology was extended to evaluate the storm management parameter for multiple catchments using dynamic programming.
With the increase in urbanization the importance of dealing with the uncontrolled discharge of runoff which results in adverse impacts like bank erosion, flooding and increasing the chances of pollution. Best management practices (BMP) are considered to attenuate the peak flow, control the runoff volume and control runoff quality, so as to maintain the predevelopment runoff conditions or meet the environmental regulations for runoff quality and quantity. The present study focuses on using detention pond as the storm management control measure. Volume and quality of runoff controlled is largely dependent on the storage volume of the detention pond. Though Storm water management pond (detention pond) is considered as the measure to mitigate the adverse effects, land developers and municipalities consider such ponds to be loss of developable land and added cost of construction and maintenance. The objective of such developers is to minimize the cost associated to the SWM ponds with satisfying the environmental regulations. Typical cost of concerns associated to these SWM ponds are cost of land occupied by the ponds, operation, maintenance and repair (OMR) costs. The present study is to optimize the geometry of the SWM pond to meet the runoff volume control and pollution control to a single catchment, further the problem was formulated for a multiple catchments to optimize design the parameters such that they meet the overall runoff control and pollution control.
The model was formulated considering three parallel catchments having similar metrological characteristics and with SWM ponds which are located upstream of the outlet into the common collection system. Different levels of quality and quantity control was considered at different catchments such that the final or overall control of quality and quantity at the outfall was met according to the environmental regulations. By considering the above mentioned methodology it will be easy to optimize the solution for minimizing the cost of implementing the SWM ponds for all the catchments by providing optimal blend of individual catchments controls than considered uniform control. Both runoff quantity and quality control constraint at the outfall are considered to be greater than the area-weighted average level of pollution/volume control at each catchment. In the study they have not considered the local conveyance cost as normally the SWM ponds are situated in close proximity to the collection system and they did not consider differential cost associated to different rates of release from the pond. In the present work, SWM ponds is considered to be extended detention dry pond type which stores catchment runoff until the entire volume is used. Decision variable in the optimization of the problem are the storage volume, release rate and the pond depth which estimates the extent of runoff quantity and quality.
For carrying out optimizing of SWM pond in a single catchment they have considered 90% runoff control isoquant and 50% pollution control isoquant with constant pond depth to study the strategy of how the release rates and storage volume of the pond vary. As pond depth is also a decision variable in optimizing the solution, a range of depths are analyzed to produce corresponding optimal storage volume and release rates. Therefore by considering the relationship between pond cost and pond depth, an optimal pond depth is estimated which meets the pollution and runoff control constraints.
For optimizing a SWM pond for multiple catchments, DP is considered as the optimization tool which divides the multiple catchment problem into series of individual problems, then combining the solution of each small(stages) problem to the overall (multiple catchment) problem. Overall pollution and runoff control is not violated even if we decompose the model into simpler stages. After the computation of each stage is carried out, track back procedure is followed to find the least cost of the SWM pond considering the design variables.
Discussion:
I found the paper very informative as it gives an idea on how a single catchment SWM optimization and multiple catchment optimizations are done. I could not find any particular problem of concern. But it would be interesting to carry out further research by considering all the assumptions they made in the present study.
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