Historical floods inventory map.

Historical floods inventory map.</p

Flood conditioning factors: (a) curvature, (b) NDVI, (c) TWI, (d) rainfall.

Flood conditioning factors: (a) curvature, (b) NDVI, (c) TWI, (d) rainfall.</p

Flood conditioning factors: (a) elevation, (b) slope, (c) drainage density, (d) LULC.

Flood conditioning factors: (a) elevation, (b) slope, (c) drainage density, (d) LULC.</p

Calculation results of weights for all conditioning factors.

Calculation results of weights for all conditioning factors.</p

Classification of different hazard classes.

Classification of different hazard classes.</p

Identification of flood triggering and causal factors.

Identification of flood triggering and causal factors.</p

The ROC curve values of success rate and prediction rate.

The ROC curve values of success rate and prediction rate.</p

Flow chart of the methodology adopted for flood hazard mapping in PRB.

Flow chart of the methodology adopted for flood hazard mapping in PRB.</p

Flood hazard map of the study area.

Flood hazard map of the study area.</p

GIS-based flood hazard mapping using relative frequency ratio method: A case study of Panjkora River Basin, eastern Hindu Kush, Pakistan

Flood is the most devastating and prevalent disaster among all-natural disasters. Every year, flood claims hundreds of human lives and causes damage to the worldwide economy and environment. Consequently, the identification of flood-vulnerable areas is important for comprehensive flood risk management. The main objective of this study is to delineate flood-prone areas in the Panjkora River Basin (PRB), eastern Hindu Kush, Pakistan. An initial extensive field survey and interpretation of Landsat-7 and Google Earth images identified 154 flood locations that were inundated in 2010 floods. Of the total, 70% of flood locations were randomly used for building a model and 30% were used for validation of the model. 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. The relative frequency ratio was used to determine the correlation between each class of flood parameter and flood occurrences. All of the factors were resampled into a pixel size of 30×30 m and were reclassified through the natural break method. Finally, a final hazard map was prepared and reclassified into five classes, i.e., very low, low, moderate, high, very high susceptibility. 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. 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.</div