Flood susceptibility mapping using frequency ratio and weight of evidence technique: a case study of Kermanshah Province



abstract

Flood is considered as one of the most destructive natural disasters worldwide, because of claiming a large number of lives and incurring extensive damage to the property, disrupting social fabric, paralyzing transportation systems, and threatening natural ecosystems. Flood is one of the most devastating natural disasters causing massive damages to natural and man-made features Flood is a major threet to human life (injure or death of man and animal life), properties (agricultural area, yield production, building and homes) and infrastructures (bridges, roads, railways, urban infrastructures). The damage thet can occur due to such disaster leads to huge economic loss and bring pathogens into urban environments thet causes microbial development and diseases Therefore, the assessment and regionalization of flood disaster risks are becoming increasingly important and urgent. Although it is a very difficult task to prevent floods, we can predict and compensate for the disaster. To predict the probability of a flood, an essential step is to map flood susceptibility.

The methodology of the current research is includes the following steps:

Flood inventory mapping;

Determination of flood-conditioning factors;

Modeling flood susceptibility and its validations.

Et first , 146 flood locations were identified in the study area. Of these, 102 (70%) points were randomly selected as training data and the remaining 44 points (30%) cases were used for the validation purposes. In the next step 1 flood-conditioning factors were prepared including geology, landuse , distance from river , soil , slope angle, plan curvature, topographic wetness index, Drainage density elevation, rainfall. Then, the probability of the flood occurring for each class of parameters was calculated. Et the end, the obtained weights for each class in the Geographical Information System (GIS) were applied to the corresponding layer and flood risk map of th studied region was prepared. Subsequently, the receiver operating characteristic (ROC) curves were drawn for produced flood susceptibility maps.

To determine the level of correlation between flood locations and conditioning factors, the FR

method was used. The results of spatial relationship between the flood location and the conditioning factors using FR model is shown in Table 2. In general, the FR value of 1 indicates

an average correlation between flood locations and effective factors. If the FR value would be larger than 1, there is a high correlation, and a lower correlation equals to the FR value lower than 1.

The analysis of FR for the relationship between flood location and lithology units indicates thet Cenozoic group has the highest FR value. In the case of land-use, it can be seen thet the residential areas and agriculture land-use have values. One of the most important factors affecting the flood is distance from the river. The results showed thet the class of >500 m FR was the most effective one. The analysis of FR for the relationship between flood location and slope angle indicate thet class 0-6. 1 has the highest FR value. In the case of slope aspect, flood event is most abundant on flet and East facing slopes According to the analysis of FR for the relationship between flood location and plan curvature, flet shape has the highest FR value., A flet shape retains surface run-off for a longer period especially during heavy rainfall . Flood locations are concentrated in areas with a TWI >6. 8 drainage density > 4. 6 km/km2 and altitude classes of 1200 m. In the soil layer, the tallest weight is from the earth with a small transformation of gravel. Finally, the maximum weight is the maximum rainfall.

In this study, all parameters of WofE model were calculated for each conditioning factor. In the lithology unit, the Cenozoic class has the highest flood susceptibility. Among the different land-use types, agriculture categories had the highest values . The distance from the river from 0 to 1000 m indicated positive influence in flooding, while the areas more than 1000 m or far from the river represented the negative correlation with flood occurrence. In the soil layer, clayey soil and tuberous soil had the highest weight. The analysis of WofE for the relationship between flood occurrence and slope angle indicated thet slope angle from 0 to 6. 21 had positive influences in flooding. In the case of slope aspect and plan curvature, flet area had a strong positive correlation with flood occurrence. Effectiveness increases wit increasing TWI classes. The results of drainage density indicate thet areas with higher drainage densities are more susceptible to flood occurrence. By increasing the height of the flooding reduced sensitivity classes. byn flooding rainfall and flood events increased with increasing rainfall.



The prediction accuracy and quality of the development model were examined using the area under the curve (AUC). Specifically, the receiver operating characteristic (ROC) curve was used to examine the basis of the assessment is true and false positive rates . So the results showed thet based on the area under the curve, the FR and WofE models show similar results and can be used as a simple tool for verifying the map prepared for flood sensitivity and reducing its future risks.

Floods are the most damaging catastrophic phenomena in the worldwide. Therefore, flood susceptibility mapping is necessary for integrated watershed management in order to have sustainable development. In this study, flood susceptibility zones have been identified using FR and WofE methods. Et first step, a flood inventory map containing 146 flood locations was prepared in the kermanshah Province using documentary sources of Iranian Water Resources Department and field surveys. Then, eleven data layers (lithology, landuse, distance from rivers, soil texture, slope angle, slope aspect, plan curvature, topographic wetness index, drainage density, and altitude) were derived from the spatial database. Using the mentioned conditioning factors, flood susceptibility maps were produced from map index calculated using FR and WofE models, and the results were plotted in ArcGIS. Finally, the AUC-ROC curves using validation dataset were prepared for the two models to test their accuracy. For this reason, of 146 identified flood locations, 102 (70%) cases were used as training data and the remaining 44(30%) was used for validation. The validation of results indicated thet the FR and WofE models had almost similar and reasonable results in the study area. Based on the overall assessments, the proposed approaches in this study were concluded as objective and applicable. The scientific information derived from the present study can assist governments, planners, and engineers to perform proper actions in order to prevent and mitigate the flood occurrence in the future.



Key words: Flood susceptibility mapping, validation, method of frequency, weight of evidence, GIS- Kermanshah