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首页> 外文期刊>Water Resources Management >Planning of Ground water Supply Systems Subject to Uncertainty Using Stochastic Flow Reduced Models and Multi-Objective Evolutionary Optimization
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Planning of Ground water Supply Systems Subject to Uncertainty Using Stochastic Flow Reduced Models and Multi-Objective Evolutionary Optimization

机译：使用随机流量减少模型和多目标进化优化规划不确定性的地下水供应系统

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摘要

The typical modeling approach to groundwater management relies on the combination of optimization algorithms and subsurface simulation models. In the case of groundwater supply systems, the management problem may be structured into an optimization problem to identify the pumping scheme that minimizes the total cost of the system while complying with a series of technical, economical, and hydrological constraints. Since lack of data on the subsurface system most often reflects upon the development of groundwater flow models that are inherently uncertain, the solution to the groundwater management problem should explicitly consider the tradeoff between cost optimality and the risk of not meeting the management constraints. This work addresses parameter uncertainty following a stochastic simulation (or Monte Carlo) approach, in which a sufficiently large ensemble of parameter scenarios is used to determine representative values selected from the statistical distribution of the management objectives, that is, minimizing cost while minimizing risk. In particular, the cost of the system is estimated as the expected value of the cost distribution sampled through stochastic simulation, while the risk of not meeting the management constraints is quantified as the expected value of the intensity of constraint violation. The solution to the multi-objective optimization problem is addressed by combining a multi-objective evolutionary algorithm with a stochastic model simulating groundwater flow in confined aquifers. Evolutionary algorithms are particularly appropriate in optimization problems characterized by non-linear and discontinuous objective functions and constraints, although they are also computationally demanding and require intensive analyses to tune input parameters that guarantee optimality to the solutions. In order to drastically reduce the otherwise overwhelming computational cost, a novel stochastic flow reduced model is thus developed, which practically allows for averting the direct inclusion of the full simulation model in the optimization loop. The computational efficiency of the proposed framework is such that it can be applied to problems characterized by large numbers of decision variables.
机译：地下水管理的典型建模方法依赖于优化算法和地下模拟模型的结合。在地下水供应系统的情况下，管理问题可以构造为一个优化问题，以识别在满足一系列技术，经济和水文约束的同时，将系统总成本降至最低的泵送方案。由于地下系统缺乏数据通常反映了固有不确定性的地下水流模型的发展，解决地下水管理问题应明确考虑成本最优与不满足管理约束的风险之间的权衡。这项工作解决了随机模拟（或蒙特卡洛）方法之后的参数不确定性问题，在该方法中，使用足够大的参数场景集合来确定从管理目标的统计分布中选择的代表值，即在最小化成本的同时将风险最小化。特别是，将系统成本估算为通过随机模拟采样的成本分布的期望值，而将不满足管理约束的风险量化为约束违反强度的期望值。通过将多目标进化算法与模拟密闭含水层中地下水流的随机模型相结合，解决了多目标优化问题的解决方案。演化算法特别适用于以非线性和不连续的目标函数和约束为特征的优化问题，尽管它们在计算上也很苛刻，并且需要进行深入的分析以调整输入参数，以确保解的最优性。为了显着降低原本不堪重负的计算成本，因此开发了一种新颖的随机流量降低模型，该模型实际上避免了将整个仿真模型直接包含在优化循环中。所提出的框架的计算效率使得它可以应用于以大量决策变量为特征的问题。

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