SPATIAL MODELLING OF FLOOD SUSCEPTIBILITY IN KELANTAN USING GIS AND RANDOM FOREST MACHINE LEARNING

Flood is a huge issue which influences the human activities. The outcomes will end in tremendous general death toll and destruction of property because of different factors, for example, disintegration of the biological system, environmental change, fast populace development and expanded and inappropriate land use. The use of the Geographic Information System (GIS) was subsequently used to make spatial forecasts for flood in danger inclined zones to give a future mitigation and relief plan. In any case, the act of utilizing the Geographic Information System (GIS) was not adequate to create an accurate predictive map. Considering its constraints, the positioning accommodated the conditioning factors that caused the event of the flood was not precise and incorrect to deliver an exact spatial forecast model. Accordingly, machine learning was being integrated with GIS to produce more reliable and accurate susceptibility maps for flood vulnerable locations.