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首页> 外文期刊>Environmental and ecological statistics >Modelling relationships between socioeconomy, landscape and water flows in Mediterranean agroecosystems: a case study in Adra catchment (Spain) using Bayesian networks
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Modelling relationships between socioeconomy, landscape and water flows in Mediterranean agroecosystems: a case study in Adra catchment (Spain) using Bayesian networks

机译:地中海农业系统中社会经济,景观和水流的建模关系 - 以贝叶斯网络为例

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摘要

In Mediterranean areas, the co-evolution between social and natural systems has given rise to heterogeneous and complex systems of interactions called agroecosystems, in which strong relationships between socioeconomy, landscape and water flows have been identified. In this context, water resources management is a prominent area of research, particularly in semi-arid conditions, where a special set of challenges requires novel tools to deal with uncertainty, multiple sources of information and expert knowledge. In this paper, Bayesian Networks are proposed as a means to model the relationships between socioeconomy, landscape and water flows in a Mediterranean agroecosystem, studying its behaviour under two scenarios of change in land use trends: maintenance of traditional Mediterranean agriculture, and agricultural intensification through the development of greenhouses. Results show that an increase in the area of traditional agriculture would lead to better control of runoff and increased primary productivity, measured as green water flows. By contrast, agricultural intensification of the territory would provoke an increase in evaporation and water losses. Due to the versatility of Bayesian networks, results can be expressed not only as probabilities, but also using other metrics that can be computed from them. Accordingly, Sensitivity Analysis to Evidence, Sensitivity Analysis to Parameters and the Kullback-Leibler divergence were carried out. Bayesian Networks have demonstrated their ability to deal with uncertainty inherent to natural systems, combining expert knowledge, data from regional datasets and Geographical Information Systems, and automatic training algorithms giving robust and proper results.
机译:在地中海地区,社会和自然系统之间的共同进化引起了叫做农业体系的异构和复杂的交互系统,其中已经确定了社会经济,景观和水流之间的强大关系。在这种情况下,水资源管理是一个突出的研究领域,特别是在半干旱条件下,特别挑战需要新颖的工具来处理不确定性,信息的多种信息和专家知识。在本文中,提出了贝叶斯网络作为模拟地中海农业体系中社会经济,景观和水流之间关系的手段,研究其在土地利用趋势的两种情况下的行为:传统地中海农业的维护,农业强化温室的发展。结果表明,随着绿色水流,传统农业面积的增加会更好地控制径流和增加的初级生产力。相比之下,该领土的农业强化会引起蒸发和水损的增加。由于贝叶斯网络的多功能性,结果不仅可以作为概率表示,而且可以使用可以从它们计算的其他度量来表示。因此,对证据,对参数的敏感性分析和克拉莱勒布勒发散进行敏感性分析。贝叶斯网络已经证明了他们对自然系统固有的不确定性的能力,将专家知识,区域数据集和地理信息系统的数据结合起来,以及自动培训算法,给出了稳健和适当的结果。

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