Flood Hazard Risk Mapping Using a Pseudo Supervised Random Forest
Flood Hazard Risk Mapping Using a Pseudo Supervised Random Forest
复制标题
使用伪监督随机森林绘制洪水灾害风险图
DOI：
10.3390/rs12193206
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发表时间：
2020
期刊：
影响因子：
5
通讯作者：
D. Coleman
中科院分区：
文献类型：
--
作者：
M. Esfandiari;Ghasem Abdi;S. Jabari;H. McGrath;D. Coleman
世界各地经常发生毁灭性的洪水。最近，机器学习模型已被用于洪水敏感性映射。然而，即使当这些算法被提供有足够的地面实况训练样本时，它们也可能无法可靠地预测洪水延伸。另一方面，最近流域上方高度（HAND）模型可以产生精度有限的洪水预报图。本研究的目的是通过结合HAND模型和机器学习，产生一种准确和动态的洪水建模技术，以产生洪水图作为水位的函数。本文首先利用HAND模型生成初步的洪水图，然后利用HAND模型的预测结果生成伪训练样本，用于一个R. F.模型为了改善射频训练阶段，五个最有效的洪水制图条件因素，即海拔，坡度，方面，距离河流和土地利用/覆盖图。在这种方法中，R.F.利用从HAND模型中获取的伪训练点对模型进行训练，动态估计洪水范围。然而，由于HAND模型的准确性有限，使用随机样本一致性（RANSAC）方法来检测离群值。在2014年，2016年，2018年，2019年，加拿大NB的弗雷德里克顿市的不同洪水事件中测试了所提出的洪水范围预测模型的准确性。此外，为了确保所提出的模型也可以在其他地区生成准确的洪水图，还在加拿大QC的加蒂诺的2019年洪水中进行了测试。精度评估指标，如整体精度，科恩的kappa系数，马修斯相关系数，真阳性率（TPR），真阴性率（TNR），假阳性率（FPR）和假阴性率（FNR），被用来比较预测的洪水范围的研究领域，由HAND模型估计的程度和范围的哨兵2号和陆地卫星成像。结果表明，该模型可以改善HAND模型的洪水范围预测，而无需使用任何地面真实训练数据。
Devastating floods occur regularly around the world. Recently, machine learning models have been used for flood susceptibility mapping. However, even when these algorithms are provided with adequate ground truth training samples, they can fail to predict flood extends reliably. On the other hand, the height above nearest drainage (HAND) model can produce flood prediction maps with limited accuracy. The objective of this research is to produce an accurate and dynamic flood modeling technique to produce flood maps as a function of water level by combining the HAND model and machine learning. In this paper, the HAND model was utilized to generate a preliminary flood map; then, the predictions of the HAND model were used to produce pseudo training samples for a R.F. model. To improve the R.F. training stage, five of the most effective flood mapping conditioning factors are used, namely, Altitude, Slope, Aspect, Distance from River and Land use/cover map. In this approach, the R.F. model is trained to dynamically estimate the flood extent with the pseudo training points acquired from the HAND model. However, due to the limited accuracy of the HAND model, a random sample consensus (RANSAC) method was used to detect outliers. The accuracy of the proposed model for flood extent prediction, was tested on different flood events in the city of Fredericton, NB, Canada in 2014, 2016, 2018, 2019. Furthermore, to ensure that the proposed model can produce accurate flood maps in other areas as well, it was also tested on the 2019 flood in Gatineau, QC, Canada. Accuracy assessment metrics, such as overall accuracy, Cohen’s kappa coefficient, Matthews correlation coefficient, true positive rate (TPR), true negative rate (TNR), false positive rate (FPR) and false negative rate (FNR), were used to compare the predicted flood extent of the study areas, to the extent estimated by the HAND model and the extent imaged by Sentinel-2 and Landsat satellites. The results confirm that the proposed model can improve the flood extent prediction of the HAND model without using any ground truth training data.