Department of Watershed Management, Faculty of Natural Resources, Sari Agricultural Sciences and Natural Resources University (SANRU), Sari, 48441-74111, Iran.
Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, 71441- 65186, Iran.
Sci Rep. 2021 Mar 22;11(1):6496. doi: 10.1038/s41598-021-85862-7.
Natural hazards are diverse and uneven in time and space, therefore, understanding its complexity is key to save human lives and conserve natural ecosystems. Reducing the outputs obtained after each modelling analysis is key to present the results for stakeholders, land managers and policymakers. So, the main goal of this survey was to present a method to synthesize three natural hazards in one multi-hazard map and its evaluation for hazard management and land use planning. To test this methodology, we took as study area the Gorganrood Watershed, located in the Golestan Province (Iran). First, an inventory map of three different types of hazards including flood, landslides, and gullies was prepared using field surveys and different official reports. To generate the susceptibility maps, a total of 17 geo-environmental factors were selected as predictors using the MaxEnt (Maximum Entropy) machine learning technique. The accuracy of the predictive models was evaluated by drawing receiver operating characteristic-ROC curves and calculating the area under the ROC curve-AUCROC. The MaxEnt model not only implemented superbly in the degree of fitting, but also obtained significant results in predictive performance. Variables importance of the three studied types of hazards showed that river density, distance from streams, and elevation were the most important factors for flood, respectively. Lithological units, elevation, and annual mean rainfall were relevant for detecting landslides. On the other hand, annual mean rainfall, elevation, and lithological units were used for gully erosion mapping in this study area. Finally, by combining the flood, landslides, and gully erosion susceptibility maps, an integrated multi-hazard map was created. The results demonstrated that 60% of the area is subjected to hazards, reaching a proportion of landslides up to 21.2% in the whole territory. We conclude that using this type of multi-hazard map may be a useful tool for local administrators to identify areas susceptible to hazards at large scales as we demonstrated in this research.
自然灾害在时间和空间上具有多样性和不均匀性，因此，了解其复杂性是拯救人类生命和保护自然生态系统的关键。减少每次建模分析后获得的结果是为利益相关者、土地管理者和政策制定者呈现结果的关键。因此，本调查的主要目标是提出一种将三种自然灾害综合在一张多灾害图中的方法，并评估其在灾害管理和土地利用规划中的应用。为了测试这种方法，我们选择戈兰罗德流域（位于伊朗戈勒斯坦省）作为研究区域。首先，我们通过实地调查和不同的官方报告，制作了一张包含洪水、滑坡和沟壑三种不同类型灾害的清单图。为了生成易感性图，我们使用最大熵（Maximum Entropy）机器学习技术选择了总共 17 个地质环境因素作为预测因子。通过绘制接收者操作特征-ROC 曲线和计算 ROC 曲线下的面积-AUCROC，评估预测模型的准确性。MaxEnt 模型不仅在拟合程度上表现出色，而且在预测性能上也取得了显著的结果。三种研究类型的灾害的变量重要性表明，河流密度、距溪流的距离和海拔是洪水的最重要因素。岩性单元、海拔和年平均降雨量与滑坡检测有关。另一方面，在本研究区域，年平均降雨量、海拔和岩性单元用于沟壑侵蚀制图。最后，通过将洪水、滑坡和沟壑侵蚀易感性图相结合，创建了一张综合多灾害图。结果表明，60%的区域易受灾害影响，整个地区的滑坡比例高达 21.2%。我们得出结论，使用这种类型的多灾害图可能是地方管理员识别大面积易受灾害影响区域的有用工具，正如我们在这项研究中所展示的那样。
Zotero 插件
