Assessment of Catastrophic Risk Using Bayesian Network Constructed from Domain Knowledge and Spatial Data
Assessment of Catastrophic Risk Using Bayesian Network Constructed from Domain Knowledge and Spatial Data
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DOI：
10.1111/j.1539-6924.2010.01429.x
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发表时间：
2010-07
期刊：
影响因子：
3.8
通讯作者：
Lianfa Li;Jinfeng Wang;Hareton K. N. Leung;Cheng-Sheng Jiang
中科院分区：
文献类型：
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作者：
Lianfa Li;Jinfeng Wang;Hareton K. N. Leung;Cheng-Sheng Jiang
由于多种相关因素的不确定性和复杂性，自然灾害及其后果的预测是困难的。本文探讨了使用领域知识和空间数据来构建贝叶斯网络(BN)，该网络便于在一个一致的系统内集成多个因素并量化不确定性以评估巨灾风险。BN具有融合多源数据和领域知识于一致系统、从数据集学习、对缺失数据进行推理、支持决策等优点而被选择。我们方法的一个关键优势是将领域知识和从数据中学习相结合来构建一个健壮的网络。为了改进评估，我们使用空间数据分析和数据挖掘来扩展训练数据集，选择风险因素，并对网络进行微调。我们方法的另一个主要优势是集成了最优离散化、信息特征选择器、学习器、局部拓扑的搜索策略和贝叶斯模型平均。这些技术都有助于对自然灾害风险概率的稳健预测。在洪涝灾害的研究中，与其他方法相比，我们的方法获得了更好的高风险检测概率、更高的精度和更好的ROC区域，使用基于历史数据的交叉验证和灾难风险预测。我们的结果表明，BN是一种很好的风险评估选择，并可作为巨灾风险管理的决策工具。
Prediction of natural disasters and their consequences is difficult due to the uncertainties and complexity of multiple related factors. This article explores the use of domain knowledge and spatial data to construct a Bayesian network (BN) that facilitates the integration of multiple factors and quantification of uncertainties within a consistent system for assessment of catastrophic risk. A BN is chosen due to its advantages such as merging multiple source data and domain knowledge in a consistent system, learning from the data set, inference with missing data, and support of decision making. A key advantage of our methodology is the combination of domain knowledge and learning from the data to construct a robust network. To improve the assessment, we employ spatial data analysis and data mining to extend the training data set, select risk factors, and fine‐tune the network. Another major advantage of our methodology is the integration of an optimal discretizer, informative feature selector, learners, search strategies for local topologies, and Bayesian model averaging. These techniques all contribute to a robust prediction of risk probability of natural disasters. In the flood disaster's study, our methodology achieved a better probability of detection of high risk, a better precision, and a better ROC area compared with other methods, using both cross‐validation and prediction of catastrophic risk based on historic data. Our results suggest that BN is a good alternative for risk assessment and as a decision tool in the management of catastrophic risk.