Integrating Entropy-Based Naive Bayes and GIS for Spatial Evaluation of Flood Hazard
Integrating Entropy-Based Naive Bayes and GIS for Spatial Evaluation of Flood Hazard
复制标题
集成基于熵的朴素贝叶斯和 GIS 进行洪水灾害的空间评估
作者：
Liu;Rui;Chen;Yun;Wu;Jianping;Gao;Lei;Barrett;Damian;Xu;Tingbao;Li;Xiaojuan;Li;Linyi;Huang;Chang;Yu;Jia;Liu Rui;Li Xiaojuan;Chen Yun;Wu Jianping;Gao Lei;Barrett Damian;Xu Tingbao;Li Linyi;Huang Chang;Yu Jia;Liu R
极端气候条件下强降雨引发的区域性洪水风险日益引起全球关注。洪水危险性制图与评价是洪水风险评价的重要组成部分。本研究发展一个整合的架构，结合加权朴素贝氏（WNB）、地理资讯系统与遥感技术，以评估洪水灾害的空间可能性。选择澳大利亚昆士兰州菲茨罗伊河流域北部作为案例研究地点。环境指数，包括极端降雨量、蒸散量、净水指数、土壤持水率、海拔、坡度、排水邻近度和密度，是从代表气候、土壤、植被、水文和地形的空间数据中生成的。这些指数使用基于统计的熵方法进行加权。加权指数被输入到基于WNB的模型中，以描绘区域洪水风险图，表明洪水发生的可能性。利用中分辨率成像光谱仪（MODIS）图像提取的最大淹没范围验证了所得到的地图。评价结果包括洪水危险性分布图的绘制和评价，有助于指导该地区的洪水淹没灾害响应。提出的新方法包括加权网格数据，基于图像的采样和验证，逐单元概率推断和空间映射。该方法优于现有的空间朴素贝叶斯（NB）区域洪水危险性评估方法，具有上级优势。它也可以扩展到其他可能性相关的环境危害研究。
Regional flood risk caused by intensive rainfall under extreme climate conditions has increasingly attracted global attention. Mapping and evaluation of flood hazard are vital parts in flood risk assessment. This study develops an integrated framework for estimating spatial likelihood of flood hazard by coupling weighted naïve Bayes (WNB), geographic information system, and remote sensing. The north part of Fitzroy River Basin in Queensland, Australia, was selected as a case study site. The environmental indices, including extreme rainfall, evapotranspiration, net‐water index, soil water retention, elevation, slope, drainage proximity, and density, were generated from spatial data representing climate, soil, vegetation, hydrology, and topography. These indices were weighted using the statistics‐based entropy method. The weighted indices were input into the WNB‐based model to delineate a regional flood risk map that indicates the likelihood of flood occurrence. The resultant map was validated by the maximum inundation extent extracted from moderate resolution imaging spectroradiometer (MODIS) imagery. The evaluation results, including mapping and evaluation of the distribution of flood hazard, are helpful in guiding flood inundation disaster responses for the region. The novel approach presented consists of weighted grid data, image‐based sampling and validation, cell‐by‐cell probability inferring and spatial mapping. It is superior to an existing spatial naive Bayes (NB) method for regional flood hazard assessment. It can also be extended to other likelihood‐related environmental hazard studies.