Sun Hai, Wang Jin, Ye Wentao
College of Engineering, Ocean University of China, Qingdao 266100, China.
Institute of Marine Development of the Ocean University of China, Ocean University of China, Qingdao 266100, China.
Int J Environ Res Public Health. 2021 Mar 12;18(6):2918. doi: 10.3390/ijerph18062918.
The accurate prediction of storm surge disasters' direct economic losses plays a positive role in providing critical support for disaster prevention decision-making and management. Previous researches on storm surge disaster loss assessment did not pay much attention to the overfitting phenomenon caused by the data scarcity and the excessive model complexity. To solve these problems, this paper puts forward a new evaluation system for forecasting the regional direct economic loss of storm surge disasters, consisting of three parts. First of all, a comprehensive assessment index system was established by considering the storm surge disasters' formation mechanism and the corresponding risk management theory. Secondly, a novel data augmentation technique, k-nearest neighbor-Gaussian noise (KNN-GN), was presented to overcome data scarcity. Thirdly, an ensemble learning algorithm XGBoost as a regression model was utilized to optimize the results and produce the final forecasting results. To verify the best-combined model, KNN-GN-based XGBoost, we conducted cross-contrast experiments with several data augmentation techniques and some widely-used ensemble learning models. Meanwhile, the traditional prediction models are used as baselines to the optimized forecasting system. The experimental results show that the KNN-GN-based XGBoost model provides more precise predictions than the traditional models, with a 64.1% average improvement in the mean absolute percentage error (MAPE) measurement. It could be noted that the proposed evaluation system can be extended and applied to the geography-related field as well.
准确预测风暴潮灾害的直接经济损失，对于为防灾决策和管理提供关键支持具有积极作用。以往关于风暴潮灾害损失评估的研究，没有充分关注数据稀缺和模型过度复杂所导致的过拟合现象。为解决这些问题，本文提出了一种新的风暴潮灾害区域直接经济损失预测评估体系，该体系由三部分组成。首先，通过考虑风暴潮灾害的形成机制和相应的风险管理理论，建立了一个综合评估指标体系。其次，提出了一种新颖的数据增强技术——k近邻-高斯噪声（KNN-GN），以克服数据稀缺问题。第三，利用一种集成学习算法XGBoost作为回归模型来优化结果并产生最终的预测结果。为验证最佳组合模型——基于KNN-GN的XGBoost，我们与几种数据增强技术和一些广泛使用的集成学习模型进行了交叉对比实验。同时，将传统预测模型用作优化预测系统的基线。实验结果表明，基于KNN-GN的XGBoost模型比传统模型提供了更精确的预测，在平均绝对百分比误差（MAPE）测量中平均提高了64.1%。值得注意的是，所提出的评估体系也可以扩展并应用于地理相关领域。
