## 1 Introduction  
Local site conditions play a crucial role in determining the level of damage experienced during earthquakes, as demonstrated from past experiences in the Mexico City (1985), Loma Prieta (1989), Northridge (1994), Kobe (1995), and, more recently, Turkey (2023) earthquakes. In many cases, the geological and geotechnical characteristics of an area can amplify seismic waves, leading to greater ground shaking and structural damage. Soil type, liquefaction conditions, site topography, building design, and historical context are some of the key factors affecting the severity of shaking and damage [1, 2, 3, 4, 5, 6, 7, 8, 9]. Addressing these factors through better engineering practices and building codes can mitigate damage in future events. To this end, for many decades, using local elastic response spectra based on soil categorization and seismic hazard has been the common way to evaluate seismic input for the design and evaluation of structures. Soil categorization and characterization are also crucial when applying Ground Motion Prediction Equations (GMPEs) for accurate predictions of local ground motion and/or for developing site-specific spectra while taking into account local site conditions. Numerous researchers have put forward different site parameters to classify and characterize soils. [PERSON] (1994) was among the first to recommend using the average shear wave velocity over the upper 30 m (\(V_{s30}\)) as a fundamental criterion for soil characterization [10]. This parameter has since gained widespread acceptance in seismic assessments. However, there are concerns about \(V_{s30}\)'s effectiveness in adequately representing site effects on its own [11, 12, 13, 14, 15, 16, 17, 18, 19, 20]. More recent GMPEs that utilize various databases continue to rely on \(V_{s30}\) to characterize site conditions [21, 22, 23, 24]. However, \(V_{s30}\) is frequently complemented or replaced by other site parameters, including the fundamental frequency,\(f_{0}\), the average shear wave velocity at different depths within the soil profile, or the depth to hard bedrock [19, 22, 25, 26, 27, 28, 29]. Recent and current regulatory codes use peak ground acceleration (_PGA_) or spectral values for reference soil, along with \(V_{s30}\), to modify the design spectra's characteristics--specifically the plateau bandwidth, level, and long-period decay--to suit local site conditions. These regulations focus on the free surface response to ascending waves, which governs how seismic waves interact with structures. This methodology is fundamental to major international building codes, including the 1997 NEHRP Provisions, the 1997 Uniform Building Code, Eurocode 8 (ENV 1998), the International Building Code (IBC 2012), and the National Building Code of Canada (NBC) (2015a, b, 2020) [29, 30, 31, 32, 33, 34]. Recent studies have incorporated multiple soil parameters using Generalized Regression Neural Network models. These models are founded on the wave propagation theory applied to linear viscoelastic soil profiles sourced from the Japanese Kiban Kyoshin Network (KiK-net) and the US Geological Survey (USGS) [7, 35]. Site amplification is usually captured by relying on only few straightforward site proxies' values (like \(V_{s30}\) and contrast velocity). However, this simplification can lead to various challenges, including \(V_{s30}\)'s inability to reflect the shear wave velocity profile across the entire soil depth, which can result in a skewed seismic response for short, mid, and/or long periods. Given the complex nature of the problem, the selected limited set of site proxies used to predict site amplification may not necessarily be the best combination, especially because the literature lacks a systematic evaluation of the performance of a limited number of different site proxy combinations. On the other hand, artificial neural networks (ANNs) are particularly effective for prediction tasks due to their capacity to model complex nonlinear relationships withinhigh-dimensional datasets.