Abstract
Flood is one of the most predominant disasters around the globe and frequently occurring phenomena in the northern part of Pakistan. In this study, the effects of various divisions of flood inventory and combinations of conditioning factors were assessed for the preparation of final susceptibility map. The flood inventory map was prepared for Charsadda by visual interpretation of Landsat-7 image alongside the field survey and a total of 161 flood locations were mapped. The flood inventory was subsequently divided into training and validation datasets, 129 (80%) and 112 (70%) locations for training the model and 32 (20%) and 49 (30%) for validation of the model. In this study, nine conditioning factors were used (Elevation, Slope, Aspect, Curvature, Plan curvature, Profile curvature, Proximity to river, roads, and Land use/land cover) for the development of flood susceptibility map. All the conditioning factors were correlated with flood inventory map using the information value method. The final susceptibility maps were validated using prediction rate and success rate curve. The results from validation showed that the areas under curve in the prediction rate curve for the models are: Model A (99.47%), Model B (95.04%), and Model C (94.06%), respectively. The Area under curve (AUC) in the success rate curve obtained for the three models are: Model A (95.03%), Model B (86.91%), and Model C (89.67%), respectively. Eventually, the susceptibility maps were classified into five susceptibility zones. The success rate and prediction rate curve indicated that model A has more accuracy in comparison to model B and model C; though, the results obtained from prediction and success rate curve indicated that all the models are reliable and has no significant difference between the susceptibility maps. Consequently, results obtained from this study are useful for researchers, disaster managers, and decision-makers to manage the flood-prone areas in the study area to mitigate the flood damages.
Highlights
Natural hazards are increasing day by day and have gained attention at the global and national levels
The Area under curve (AUC) in the success rate curve obtained for the three models are: Model A (95.03%), Model B (86.91%), and Model C (89.67%), respectively
River is the most important conditioning factor in flood susceptibility assessment, and a range of 200 m distance from the river has a substantial effect on flooding in both models with information value method (IFV) of 0.2319 and 0.265 for Model A and Model B, respectively
Summary
Natural hazards are increasing day by day and have gained attention at the global and national levels. Flood is the most common and destructive hazard among all-natural disasters (Uddin et al, 2013). It is one of the most severe hazards in which the river cannot accommodate water more than its capacity and overspills on the banks of river and causes the economic, social, and human losses (Jonkman, 2005). Flood is the most destructive natural hazard in Pakistan, and since independence, the country has faced seventeen severe floods, which have caused an economic loss of 12 billion USD (WAPDA, 2013). In the 2010 flood, Mardan, Charsadda, Nowshera, and Peshawar were severely affected because of its exposure to three main rivers of the province, namely, river Swat, Kabul, and Indus (Ahmad et al, 2011; Khan & Iqbal, 2013). The 2010 flood is the deadliest one, which killed almost 1900 and affected 20 million people, followed by a flood in 1992 and 1995 in which 9.8 and 1.8 million people were affected with death toll of 1446 and 1063 respectively (EM-DAT, 2018)
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