School of Civil Engineering, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece.
Environ Sci Pollut Res Int. 2024 Jun;31(28):41267-41289. doi: 10.1007/s11356-024-33895-6. Epub 2024 Jun 7.
On a global scale, flooding is the most devastating natural hazard with an increasingly negative impact on humans. It is necessary to accurately detect flood-prone areas. This research introduces and evaluates the Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) integrated with GIS in the field of flood susceptibility in comparison with two conventional multi-criteria decision analysis (MCDA) methods: analytical hierarchy process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The Spercheios river basin in Greece, which is a highly susceptible area, was selected as a case study. The application of these approaches and the completion of the study requires the creation of a geospatial database consisting of eight flood conditioning factors (elevation, slope, NDVI, TWI, geology, LULC, distance to river network, rainfall) and a flood inventory of flood (564 sites) and non-flood locations for validation. The weighting of the factors is based on the AHP method. The output values were imported into GIS and interpolated to map the flood susceptibility zones. The models were evaluated by area under the curve (AUC) and the statistical metrics of accuracy, root mean squared error (RMSE), and frequency ratio (FR). The PROMETHEE model is proven to be the most efficient with AUC = 97.21%. Statistical metrics confirm the superiority of PROMETHEE with 87.54% accuracy and 0.12 RMSE. The output maps revealed that the regions most prone to flooding are arable land in lowland areas with low gradients and quaternary formations. Very high susceptible zone covers approximately 15.00-19.50% of the total area and have the greatest FR values. The susceptibility maps need to be considered in the preparation of a flood risk management plan and utilized as a tool to mitigate the adverse impacts of floods.
在全球范围内，洪水是最具破坏性的自然灾害，对人类的影响越来越负面。因此，有必要准确地检测易受洪水影响的地区。本研究在洪水易感性领域引入并评估了偏好排序组织法（PROMETHEE）与地理信息系统（GIS）的集成，并与两种传统的多准则决策分析（MCDA）方法：层次分析法（AHP）和逼近理想解排序技术（TOPSIS）进行了比较。选择希腊的 Spercheios 流域作为案例研究，该流域是一个高度易受洪水影响的地区。应用这些方法并完成研究需要创建一个包含八个洪水条件因素（海拔、坡度、NDVI、TWI、地质、土地利用/土地覆被、距河网的距离和降雨量）和洪水（564 个地点）和非洪水位置的地理空间数据库，以进行验证。这些因素的权重是基于层次分析法确定的。将输出值导入 GIS 并进行插值，以绘制洪水易感性图。通过曲线下面积（AUC）和准确性、均方根误差（RMSE）和频率比（FR）的统计指标对模型进行评估。结果表明，PROMETHEE 模型的 AUC 为 97.21%，被证明是最有效的。统计指标证实了 PROMETHEE 的优越性，准确性为 87.54%，RMSE 为 0.12。输出地图显示，最容易发生洪水的地区是低地地区、坡度较低的耕地和第四纪地层。非常高的易感性区域约占总面积的 15.00-19.50%，具有最大的 FR 值。这些易感性图需要在制定洪水风险管理计划时加以考虑，并作为减轻洪水不利影响的工具加以利用。
