A NEW SIMULATION-OPTIMIZATION APPROACH FOR SIMULTANEOUSLY IDENTIFYING

This study proposes a new simulation-optimization approach for simultaneously identifying the spatial distribution and source fluxes of the areal pollution sources in groundwater systems. In the proposed approach, groundwater flow and pollution transport processes are simulated via MODFLOW and MT3DMS models in the simulation part. These models are then integrated to an optimization model where a binary genetic algorithm (GA) is used. In the proposed GA based optimization model, finite difference grid blocks of the given aquifer domain are considered to be the potential areal pollution source locations. The main objective of the GA is to evolve the source fluxes and spatial distributions of source locations through genetic operators by minimizing the error value calculated between the measured and simulated pollution concentrations at given monitoring locations and times. The performance of the proposed approach is evaluated on a hypothetical aquifer model for 4 different pollution source distributions. Identified results indicated that the proposed simulation-optimization approach may be used as an effective way to solve the areal pollution source identification problems

Algorithm

Groundwater is not only an important component of water resources, but also a reliable source of fresh water for domestic and industrial uses. However, due to climate change and the fast population growth, the quality and quantity of groundwater resources have been continuously decreasing. Therefore, sustainable management strategies should be developed for groundwater systems by decision makers. This chapter provides a brief review dealing with the use of Harmony Search (HS) optimization algorithm for solving the groundwater management problems. Review results indicate that HS can successively solve the groundwater management problems and provides identical or better results than the other non-heuristic and heuristic optimization algorithms

groundwater pollution source identification problems

In this study, a new simulation-optimization approach is proposed for solving the areal groundwater pollution source identification problems which is an ill-posed inverse problem. In the simulation part of the proposed approach, groundwater flow and pollution transport processes are simulated by modeling the given aquifer system on MODFLOW and MT3DMS models. The developed simulation model is then integrated to a newly proposed hybrid optimization model where a binary genetic algorithm and a generalized reduced gradient method are mutually used. This is a novel approach and it is employed for the first time in the areal pollution source identification problems. The objective of the proposed hybrid optimization approach is to simultaneously identify the spatial distributions and input concentrations of the unknown areal groundwater pollution sources by using the limited number of pollution concentration time series at the monitoring well locations. The applicability of the proposed simulation-optimization approach is evaluated on a hypothetical aquifer model for different pollution source distributions. Furthermore, model performance is evaluated for measurement error conditions, different genetic algorithm parameter combinations, different numbers and locations of the monitoring wells, and different heterogeneous hydraulic conductivity fields. Identified results indicated that the proposed simulation optimization approach may be an effective way to solve the areal groundwater pollution source identification problems. (C) 2016 Elsevier B.V. All rights reserved

groundwater pollution source identification problems

This study proposes a linked simulation-optimization model for solving the unknown groundwater pollution source identification problems. In the proposed model, MODFLOW and MT3DMS packages are used to simulate the flow and transport processes in the groundwater system. These models are then integrated with an optimization model which is based on the heuristic harmony search (HS) algorithm. In the proposed simulation-optimization model, the locations and release histories of the pollution sources are treated as the explicit decision variables and determined through the optimization model. Also, an implicit solution procedure is proposed to determine the optimum number of pollution sources which is an advantage of this model. The performance of the proposed model is evaluated on two hypothetical examples for simple and complex aquifer geometries, measurement error conditions, and different HS solution parameter sets. Identified results indicated that the proposed simulation-optimization model is an effective way and may be used to solve the inverse pollution source identification problems. (c) 2010 Elsevier B.V. All rights reserved

shallow water flows

A linked simulation-optimization model is proposed for simultaneously estimating the Manning's surface roughness parameters and their associated parameter structures for one-dimensional shallow water flows. In the simulation part of the model, hydrodynamic flow process is simulated by modeling the given flow reach on HEC-RAS. The association of unknown parameter structure with the roughness values is accomplished by partitioning the given flow reach into sub-regions using the one dimensional Voronoi Diagram (VD). The developed simulation model is then linked to an optimization model where heuristic Particle Swarm Optimization algorithm is used. The main objective of the PSO based optimization model is to determine the roughness parameters and their associated parameter structures along the flow reach by minimizing the discrepancies between simulated and measured water elevations at several observation locations. The applicability of the model is evaluated on a flow reach of East Fork River, WY, USA under three scenarios by considering the known/unknown roughness parameter structures and unsteady flow conditions. Furthermore, a sensitivity analysis is conducted to determine the influence of different PSO parameters on solution accuracy. Identified results indicated that the model provides better results than those obtained by different optimization methods in literature and significantly improves the calibration statistics by considering the variability of roughness parameters along the flow reach. (C) 2013 Elsevier B.V. All rights reserved

Algorithm

Mathematical simulation models are widely used to predict the future response of groundwater systems for different flow and mass transport conditions. These models are based on the solution of governing partial differential equations which require some spatially distributed hydro-geological model parameters. However, these parameters are usually unknown due to the complexity of groundwater systems. Therefore. identification of them is an important task since they are the primary input of management models used in groundwater modeling. This chapter provides a brief review dealing with the solution of parameter structure identification problems based on the harmony search optimization algorithm. The results of this review indicate that the harmony search algorithm yields nearly same or better solutions than those of a genetic algorithm, which is another popular meta-heuristic optimization algorithm

the Tahtali watershed

The aim of this study is to present a linked simulation optimisation model to estimate the groundwater recharge rates, their associated zone structures, and hydraulic conductivity values for regional, steady-state groundwater flow models. For the zone structure estimation problem the fuzzy c-means clustering (FCM) method was used. The association of zone structures with the spatial distribution of groundwater recharge rates was then accomplished using an optimisation approach where the heuristic harmony search (HS) algorithm was used. Since the solution was obtained by a heuristic algorithm, the optimisation process was able to use a non-specific initial solution, i.e. an initial solution that does not have to be close to the final solution. The HS-based optimisation model determines the shape of zone structures, their corresponding recharge rates and hydraulic conductivity values by minimizing the root mean square error (91) between simulated and observed head values at observation wells and springs, respectively. To determine the best recharge zone structure, the identification procedure starts with computation of one zone and systematically increased the zone number until the optimum zone structure is identified. Subsequently, the performance of the proposed simulation-optimisation model was evaluated on the Tahtali watershed (Izmir, Turkey), an urban watershed for which a seasonal steady-state groundwater flow model was developed for a previous study. The results of our study demonstrated that the proposed simulation optimisation model is an effective way to calibrate the groundwater flow models for the cases where tangible information about the groundwater recharge distribution does not exist

Optimum design of the booster chlorination systems by using hybrid

A hybrid simulation-optimization approach is developed in this study for optimally designing the booster chlorination systems in water distribution networks. In the developed approach, chlorine residuals in the demand locations are determined by using the response matrix (RM) approach. The generated RM is then integrated to an optimization model where a hybrid HS-Solver optimization approach is used. HS-Solver is a recently proposed hybrid approach which integrates the harmony search (HS) algorithm and a spreadsheet Solver as the global and local optimizers, respectively. The objective of the HS-Solver in the developed approach is for optimally designing the booster stations by maintaining the chlorine residuals within desired limits. The applicability of the developed approach is tested on a real water distribution network. Identified results indicate that the developed HS-Solver based solution approach determined similar or better results than those obtained by using the different solution approaches in literature

the Tahtali watershed

The aim of this study is to present a linked simulation optimisation model to estimate the groundwater recharge rates, their associated zone structures, and hydraulic conductivity values for regional, steady-state groundwater flow models. For the zone structure estimation problem the fuzzy c-means clustering (FCM) method was used. The association of zone structures with the spatial distribution of groundwater recharge rates was then accomplished using an optimisation approach where the heuristic harmony search (HS) algorithm was used. Since the solution was obtained by a heuristic algorithm, the optimisation process was able to use a non-specific initial solution, i.e. an initial solution that does not have to be close to the final solution. The HS-based optimisation model determines the shape of zone structures, their corresponding recharge rates and hydraulic conductivity values by minimizing the root mean square error (91) between simulated and observed head values at observation wells and springs, respectively. To determine the best recharge zone structure, the identification procedure starts with computation of one zone and systematically increased the zone number until the optimum zone structure is identified. Subsequently, the performance of the proposed simulation-optimisation model was evaluated on the Tahtali watershed (Izmir, Turkey), an urban watershed for which a seasonal steady-state groundwater flow model was developed for a previous study. The results of our study demonstrated that the proposed simulation optimisation model is an effective way to calibrate the groundwater flow models for the cases where tangible information about the groundwater recharge distribution does not exist

groundwater well locations and pumping rates

In this study, a simulation/optimization (S/O) model is proposed for the identification of unknown groundwater welt locations and pumping rates for two-dimensional aquifer systems. The proposed S/O model uses a finite-difference solution of governing groundwater flow equation as simulation model This model is then combined with a genetic algorithm (GA) based optimization model which is used to determine the pumping rates for each well. To determine the well locations, an iterative moving sub-domain approach is proposed. The main advantage of this approach is that the optimization model only determines the pumping rates and it does not require the wet[ locations as decision variables. The welt locations are implicitly determined based on the results of pumping rate optimizations for different pre-defined well locations within subdomains. The performance of the proposed S/O model is tested on two hypothetical. aquifer models for both steady-state and transient flow conditions. In both cases, the identification procedure starts with one pumping welt and systematically increases the number of the pumping wells until the best welt configuration is identified. Determination of the best number of pumping wells is performed based on the residual errors (RE) between simulated and observed piezometric head values for given observation sites. Results indicated that when the number of pumping wells is greater than the true number of wells, the identified welt configuration approaches to the true well configuration. Moreover, under steady-state flow conditions, the robustness of the proposed moving sub-domain approach is tested for different initial locations of sub-domains. Results showed that the true welt locations are identified wherever the search process starts. Finally, the performance of the proposed S/O mode( is compared with a pure GA solution in which the well locations and pumping rates are treated as decision variables. Results indicate that the proposed S/O model finds smaller RE than the pure GA solution by performing 14% less simulations. (C) 2008 Elsevier B.V. All rights reserved