Department of Civil & Environmental Engineering, College of Engineering, Seoul National University, 1 Gwanak-ro, Gwanakgu, Seoul, 08826, Republic of Korea.
Department of Living Environment Research, Korea Environment Institute, 370 Sicheong-daero, Sejong, 30147, Republic of Korea.
J Environ Manage. 2020 Jul 1;265:110552. doi: 10.1016/j.jenvman.2020.110552. Epub 2020 Apr 8.
Accurate estimations of flood waste generation are a crucial issue in disaster waste management. Multilinear regression of related parameters has been recognized as a promising technique for flood waste estimation. There are two types of flood waste estimation methods: pre-event predictions using factors related to regional properties and rainfall hazards, and post-event predictions using damage variables due to floods, such as the number of damaged buildings. Previous attempts to establish these models used deterministic approaches; however, probabilistic methods have never been applied. Considering the large degrees of uncertainty in waste generation from floods, a probabilistic approach can provide a more accurate model compared to models developed by the conventional deterministic approach. This study applied Bayesian inference to develop a flood waste regression model in South Korea. The aims of the study are as follows: (1) to analyze the characteristics of coefficients estimated by the Bayesian approach; (2) evaluate the performance of the prediction model by Bayesian inference; and (3) assess the effectiveness of Bayesian updating in a flood waste estimation. According to the results, the coefficients obtained via Bayesian inference showed a more significant p-value compared to those developed through the deterministic approach. Bayesian inference with a null prior distribution was effective in error reduction, specifically for post-event prediction. Bayesian updating did not effectively increase the accuracy of the model, while iterative updating required a complex calculation process. These results reveal the potential of the Bayesian approach in flood waste estimations, which can be transferred to other countries.
准确估算洪水废物的产生量是灾害废物管理中的一个关键问题。多元线性回归相关参数已被认为是洪水废物估算的一种有前途的技术。有两种洪水废物估算方法：使用与区域属性和降雨危害相关的因素进行事件前预测，以及使用洪水造成的破坏变量（如受损建筑物的数量）进行事件后预测。以前建立这些模型的尝试使用了确定性方法；然而，概率方法从未被应用过。考虑到洪水废物产生的不确定性程度较大，与传统的确定性方法相比，概率方法可以提供更准确的模型。本研究应用贝叶斯推断在韩国开发了洪水废物回归模型。本研究的目的如下：（1）分析贝叶斯方法估计的系数的特征；（2）通过贝叶斯推断评估预测模型的性能；（3）评估洪水废物估算中贝叶斯更新的有效性。结果表明，与通过确定性方法开发的系数相比，通过贝叶斯推断获得的系数具有更显著的 p 值。具有零先验分布的贝叶斯推断在减少误差方面非常有效，特别是对于事后预测。贝叶斯更新并没有有效地提高模型的准确性，而迭代更新需要复杂的计算过程。这些结果表明，贝叶斯方法在洪水废物估算中具有潜力，可以应用于其他国家。
