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. 
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