## 1 Introduction  
Urban development serves as an integral part in minimizing natural hazards and strengthening emergency management in increasingly urbanizing regions. Integrating risk management frameworks into urban design promotes resilience and adaptation in the face of environmental challenges. However, governance difficulties, including institutional dispersion and unclear legal frameworks, can limit adequate hazard management strategies. Collaborative governance approaches that incorporate local communities, NGOs, and business sectors help overcome gaps in disaster preparation. By considering these challenges, cities can establish inclusive policies that preserve vulnerable populations and increase sustainability over the long term. efficient disaster risk management requires the combination of hazard assessment with urban design, ensuring facilities and resident populations stay safeguarded against unforeseen environmental hazards [1]. Among these hazards is soil liquefaction, a geotechnical phenomenon whereby saturated soils lose strength and take on a more liquid-like state when subjected to dynamic forces, as in the case of earthquakes. The increased pore water pressure within the shaking soil reduces the friction of the soil particles on each other, essentially making the soil behave as a liquid. This can leads to foundation failure or even complete structural collapse. A sudden loss of strength so great can lead to catastrophic results: liquefaction can collapse buildings, roads, and bridges [2; 3; 4; 5]. Serious risk exists when loose, saturated soils are the base; that is why realistic predictions of locations likely to undergo liquefaction are critical components in disaster mitigation and urban planning with the aim of developing resilient infrastructure. At places where this phenomenon may occur, earthquake-related destruction risks rise, mostly in areas exposed to danger, including the frontier territories of tectonic plates [6; 7; 8]. That is why realistic predictions of locations likely to undergo liquefaction are critical components in disaster mitigation and urban planning with the aim of developing resilient infrastructure. The difficulty in identifying zones prone to liquefaction is due to the complexity of the physical properties of soils in different regions, including composition, grain size, density, and moisture content, which determine the overall behavior of the soil under seismic forces [9; 10]. The factors that influence susceptibility to liquefaction include soil saturation, grain size distribution, and the seismic history of the area. These patterns are often subtle, complex, and nonlinear and require sophisticated models and techniques in data analysis to identify. One of the most significant problems in developing reliable liquefaction prediction models is that liquefaction events are rare [11; 12]. Seismic activity may be relatively low, or previous earthquakes may have not triggered liquefaction in a region. For this reason, datasets used in model training are usually hugely imbalanced, with an overwhelming proportion of data reflecting \"normal\" soil conditions that to not lead to liquefaction, even when subjected to seismic activity. This makes it difficult to use machine learning models, which tend to be biased toward the majority class and often fail to appropriately recognize the rare but critical instances of liquefaction. So, to cope with this, advanced data-driven techniques that can distinguish areas prone to liquefaction from more stable soils due to complex patterns are required [13; 14; 15; 16]. Traditional geophysical methods like geotechnical investigations and borehole tests are valuable but seriously limited in time, cost, and scalability. Most geotechnical investigationsrequire the collection of field data at discrete locations, which is expensive and often not logistically feasible in large, remote, or inaccessible areas.