The Application of KNN Algorithm in Optimizing Urban Land Use with an Emphasis on Earthquake Risk, Case study: Sanandaj City

As cities become more physically in structure and demographically wider, the rate of crises is also increasing consequently, and cities become more vulnerable to natural disasters for a variety of reasons, including economic and cultural poverty, fault alignment and non-compliance to regulations of earthquake-related issues. What matters is the level of community's preparedness and the degree of vulnerability of the city and its residents, which can be reduced to the lowest level if properly planned and principle policies are adopted. In this research, considering the high potential of Sanandaj in terms of seismicity and the existence of many faults in the vicinity and around of the city, it is tried to gain an authentic understanding of the subject with identifying the factors affecting the earthquake and combining indicators using the Classification Tree Analysis (CTA) model. The results indicate that a large area of the city is in the category of moderate to high vulnerability.52% is in the middle vulnerability category,16.5% is in the high vulnerability category and 1% of the city is considered to be in the very high vulnerability category, which exactly matches the same marginal, old and densely populated neighborhoods. This situation does not render a proper structure and needs more consideration in prospective development plans.