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Development of Multi-Hazard Risk Assessment Model for Agricultural Water Supply and Distribution Systems Using Bayesian Network

机译:贝叶斯网络农业供水和分配系统多灾害风险评估模型的发展

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摘要

To identify and assess the impact of various hazards threaten agriculture water supply systems, the development of a risk analysis framework is inevitable in promoting sustainable agricultural development. This study aims to develop a novel multi-hazard risk assessment model by applying a Hybrid Bayesian Network for agricultural water supply and distribution systems. This model consists of discrete and continuous nodes and their probable interactions. The structure of this model is designed to assess the risk associated with the agricultural water system's for supply and distribution sections separately considering various factors such as river discharge, the inflow of water distribution system and its fluctuation, and the demand of water. The developed model is applied to the Roodasht Irrigation district located in the center of Iran to demonestrate its capability. This Irrigation district is under the threat of drought, improper performance of the ditch-riders, and operational losses. The results showed that the model in both training and test datasets has proper accuracy and performance with the root mean square error of 0.069 and 0.076, coefficient of determinations equal to 0.717 and 0.690, and the overall index of model performance equal to 0.787 and 0.671, respectively. The results of this research and the proposed model will help stakeholders and decision-makers to be aware of the probable causes and the extent of system failure and its components due to the threatening hazards. Also, it will assist in planning the allocation of irrigation water based on predictable risk associated with various hazards.
机译:为识别和评估各种危害的影响威胁农业供水系统,风险分析框架的发展在促进可持续农业发展方面是不可避免的。本研究旨在通过对农用供水和分配系统应用混合贝叶斯网络开发新的多危险风险评估模型。该模型包括离散和连续节点及其可能的相互作用。该模型的结构旨在评估与农业水系统的供应和分销部分相关的风险,分别考虑河流排放等各种因素,水分配系统的流入及其波动以及水需求。开发的模型适用于位于伊朗市中心的ROODASHT灌溉区,以令欺骗其能力。这个灌区是在干旱,沟渠的不当表现的威胁下,以及运营损失。结果表明,训练和测试数据集中的模型具有适当的精度和性能,具有0.069和0.076的根均方误差,确定系数等于0.717和0.690,以及模型性能的总指标等于0.787和0.671,分别。本研究的结果和拟议的模式将帮助利益相关者和决策者了解可能的原因以及由于威胁危害而导致的系统故障及其组件的程度。此外,它将有助于根据与各种危害相关的可预测风险规划灌溉水分配。

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