Landsat-8(OLI) imagery (Date: 19-September-2018) and ASTER DEM can downloaded from USGS official website ( ).įunding: This research is supported by the national key research and development program of china (2018YFC1508804) The Key Scientific and Technology Research and Development Program of Jilin Province (20180201033SF) The Key Scientific and Technology Research and Development Program of Jilin Province (20180201035SF) The Key Scientific and Technology Program of Jilin Province (20170204035SF). This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: All relevant data are within the manuscript and attached in Supporting Information files. Received: OctoAccepted: JanuPublished: March 25, 2020Ĭopyright: © 2020 Ullah, Zhang. PLoS ONE 15(3):Įditor: Mou Leong Tan, Universiti Sains Malaysia, MALAYSIA The findings of this study can play a key role in flood hazard management in the target region they can be used by the local disaster management authority, researchers, planners, local government, and line agencies dealing with flood risk management.Ĭitation: Ullah K, Zhang J (2020) GIS-based flood hazard mapping using relative frequency ratio method: A case study of Panjkora River Basin, eastern Hindu Kush, Pakistan. The results of the model were found reliable with area under curve values for success and prediction rate of 82.04% and 84.74%, respectively. Finally, a final hazard map was prepared and reclassified into five classes, i.e., very low, low, moderate, high, very high susceptibility. All of the factors were resampled into a pixel size of 30×30 m and were reclassified through the natural break method. The relative frequency ratio was used to determine the correlation between each class of flood parameter and flood occurrences. Eight flood parameters including slope, elevation, land use, Normalized Difference Vegetation Index (NDVI), topographic wetness index (TWI), drainage density, and rainfall were used to map the flood-prone areas in the study region. ![]() Of the total, 70% of flood locations were randomly used for building a model and 30% were used for validation of the model. An initial extensive field survey and interpretation of Landsat-7 and Google Earth images identified 154 flood locations that were inundated in 2010 floods. The main objective of this study is to delineate flood-prone areas in the Panjkora River Basin (PRB), eastern Hindu Kush, Pakistan. Consequently, the identification of flood-vulnerable areas is important for comprehensive flood risk management. Every year, flood claims hundreds of human lives and causes damage to the worldwide economy and environment. ![]() Flood is the most devastating and prevalent disaster among all-natural disasters.
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