Quantifying the Influences of Land Use and Rainfall Dynamics on Probable Flood Hazard Zoning

Abstract

Flooding is Pakistan's most common natural hazard, and it is exacerbated by increased rainfall and urbanization. Khyber Pakhtunkhwa (KPK), Pakistan flood-prone zones were determined by superimposing six flood parameters in an ArcGIS environment: elevation, slope, rainfall accumulation, land cover, soil geometry, and gap/buffer from water channel. Cellular automata based on artificial neural network (CA-ANN) along QGIS plugin module of Land Use Change Simulations (MOLUSCE) was used for predicting year 2050 land use, with a kappa value of 0.83. The results indicated that of the 75775 km2 land area covered by this research region, 3.37% (2553.62 km2) falls in extremely high risk, 18.44% (13972.91 km2) falls in high risk, 11.26% (8532.27 km2) falls in moderate risk, 0.51% (386.45 km2) falls in low risk, and just 66.42% (50329.76 km2) falls in very low risk areas. In KPK, like in any other place, a multi-criteria flood risk-vulnerability assessment is consequently necessary for preparation and post-hazard planning. Without a doubt, the outcomes reported here are crucial for flood risk assessments and hazard management decision-making. Key words:  natural disasters; floods; remote sensing; geographic information system, multi-criteria evaluation; weighted overlay.