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Sustainable management of a coupled groundwater-agriculture hydrosystem using multi-criteria simulation based optimisation

机译:基于多准则模拟的优化对地下水-农业水力耦合系统的可持续管理

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In this paper we present a new simulation-based integrated water management tool for sustainable water resources management in arid coastal environments. This tool delivers optimised groundwater withdrawal scenarios considering saltwater intrusion as a result of agricultural and municipal water abstraction. It also yields a substantially improved water use efficiency of irrigated agriculture. To allow for a robust and fast operation we unified process modelling with artificial intelligence tools and evolutionary optimisation techniques. The aquifer behaviour is represented using an artificial neural network (ANN) which emulates a numerical density-dependent groundwater flow model. The impact of agriculture is represented by stochastic crop water production functions (SCWPF). Simulation-based optimisation techniques together with the SCWPF and ANN deliver optimal groundwater abstraction and cropping patterns. To address contradicting objectives, e.g. profit-oriented agriculture vs. sustainable abstraction scenarios, we performed multi-objective optimisations using a multi-criteria optimisation algorithm.
机译:在本文中,我们提出了一种新的基于模拟的综合水资源管理工具,用于干旱沿海环境中的可持续水资源管理。该工具提供了优化的地下水开采方案,该方案考虑了由于农业和市政取水而引起的盐水入侵。它还大大提高了灌溉农业的用水效率。为了实现强大而快速的操作,我们使用人工智能工具和进化优化技术统一了流程建模。含水层的行为用人工神经网络(ANN)表示,该网络模拟了一个数值密度相关的地下水流模型。农业的影响以农作物的随机生产水量(SCWPF)为代表。基于仿真的优化技术与SCWPF和ANN一起提供了最佳的地下水提取和种植模式。解决矛盾的目标,例如利润导向型农业与可持续性抽象方案之间,我们使用多准则优化算法进行了多目标优化。

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