The most dangerous natural instability processes, including landslides, cause significant socioeconomic losses and property destruction each year throughout the world (Heersink, 2005; Petley, 2012; Froude and Petley, 2018; Haque et al., 2019). One of the main sources of damage to buildings and infrastructure, as well as injuries and fatalities in mountainous and hilly areas, are shallow landslides and debris flows, which are typically brought on by brief but intense rainstorms. Unlike debris flows, shallow landslides often include tiny quantities. However, due to their extensive spatial distribution over territories, quick development, and high velocity of dissemination, both can cause a great deal of harm (Hungr et al., 2014; Roccati et al., 2021). Accordingly, landslide susceptibility assessment and mapping are crucial tools in landslide risk management, assisting authorities, practitioners, and decision-makers in developing risk mitigation strategies that are more appropriate and sustainable, including the implementation of monitoring and warning systems (Dai et al., 2002; Cascini et al., 2005; Corominas et al., 2014).

Landslides have been studied using a variety of techniques to define their geometry and gather data on their stability conditions and state of activity (Petley, 2012; Al-Otaibi, 2019; Kahal et al., 2021). These techniques can typically be divided into two groups: intrusive techniques, such as boreholes, soil samples, and laboratory testing, and non-intrusive techniques, such as geophysical techniques. The latter’s use for subsurface characterization, localizing sliding surfaces, assessing the formation and evolution of cracks, comprehending water dynamics, and potential reactivation by rains has expanded rapidly (Pazzi et al., 2019). In order to define landslide ground models and afterward perform slope stability evaluation, the data from geophysical surveys are used as input (Whiteley et al., 2019).

Slope instabilities (landslides or rock falls) can be caused by a wide range of occurrences, including heavy rain, quick snowmelt, human-caused activities, and seismic events (Cruden and Varnes, 1996). Due to their enormous potential for destruction, these phenomena-especially those brought on by earthquakes-affect many parts of the world and are quite noteworthy. The losses resulting from earthquake damage are currently difficult to estimate. Traditional techniques rely on their estimates of the cost of such damage on the repair and restoration of buildings, without accounting for economic losses resulting from the loss of economic activity and human lives (Cardone et al., 2019). Therefore, seismic hazards in inhabited areas need to minimize the consequences and phenomena linked to strong ground vibrations. This is done so that the inherent seismic hazard may be calculated by looking at historical events as well as the geological and geotechnical conditions of regions that are likely to suffer a seismic event (Abdel-Rahman et al., 2010; Fat-Helbary et al., 2012; Harutoonian, 2015; Abdelrahman et al., 2017; Abdelrahman et al., 2021a; Abdelrahman et al., 2021b; Alamri et al., 2020; Almadani et al., 2020). Co-seismic landslides, which are aftershocks of earthquakes, are essential for pinpointing prior seismic occurrences and enhancing seismic hazard forecasts (Jibson, 1996). These landslides offer significant real-time geological evidence that enables researchers to recreate a region’s seismic catalog and better comprehend previous seismic activity (Rodrguez-Peces et al., 2011). The accuracy of seismic hazard predictions can be improved by increasing the dataset available for seismic analysis, which in turn helps to increase community resilience against seismic occurrences in the future.

Landslides are extremely damaging natural disasters that have a negative impact on social and economic development, as well as the safety of human life and property (Hungr et al., 2014; Wang et al., 2020). According to Gokceoglu et al. (2005) and Fang et al. (2023), landslides make up about 9% of all-natural disasters that occur worldwide, and China is one of the nations that is most seriously and extensively affected by landslide catastrophes. Landslides typically occur in mountainous and hilly areas. According to Hong et al. (2016) and Lacroix et al. (2020), landslide susceptibility mapping (LSM) is a technique for quantitatively predicting the spatial distribution of landslide susceptibility in a region by combining regional topography, geological structures, hydro-meteorology, and other characteristics. Statistical models, such as entropy, have been mostly used in earlier studies on LSM (Lee et al., 2014; Guo et al., 2019; Sun et al., 2021; Sun et al., 2023).

It is crucial to assess and identify landslide-prone locations using various landslide susceptibility mapping techniques for proper and strategic land use planning. As it demonstrates the level of susceptibility of a region to the occurrence of landslides, creating a map of a specific area’s landslide susceptibility is a useful tool in managing landslide hazards. The assumption that future landslides would occur under the same circumstances as in the past allows for the generation of landslide susceptibility maps (Pham et al., 2015). Understanding the circumstances and mechanisms that govern landslides in the research area is necessary for interpreting their likely future occurrence. By integrating these conditioning elements and past landslides in a GIS context, the essential characteristics to measure and evaluate landslide susceptibility include past landslides and other conditioning factors, such as slope morphology, hydrogeology, and geology of the area. Several researchers have employed GIS-based landslip susceptibility mapping techniques, which may be divided into qualitative and quantitative ones (Yalcin et al., 2011; Felicisimo et al., 2012; Peng et al., 2014; Wang and Li, 2017). Geomorphological analysis and inventory techniques are examples of qualitative methodologies. These rely more on expert opinion and are more individualized than quantitative approaches. In order to build and execute mathematical models, expertise is still required (Aleotti and Chowdhury, 1999; Kanungo et al., 2009). Quantitative methods such as deterministic analysis, probabilistic approaches, and statistical procedures heavily rely on these models because they have considerably less human bias.

Landslides and their dynamics can be mapped using HVSR, which is both economical and logistically effective (D'Amico et al., 2019). It offers details on the geomorphological, engineering, and geological aspects' resonance behaviors. It has been used widely in the assessment of landslide hazards and vulnerability to various triggering variables, such as earthquakes and rainfalls (Hussain et al., 2019). But as is the situation with a clayey landslide, rainfall-induced saturation lowers impedance contrast by causing changes in the rheology of the overlying unconsolidated material. The investigation of the seasonal dynamics of rainfall-triggered landslides using HVSR is based on this (Imposa et al., 2017; Bertello et al., 2018). Other environmental studies that have used HVSR include monitoring of fluvial systems (Anthony et al., 2018), estimation of changes in ice thickness (Martino, 2016; D'Amico et al., 2019; Picotti et al., 2017) and its dynamics (Köhler et al., 2015). As shown in numerous research (Burjánek et al., 2010; Del Gaudio and Wasowski, 2011; Panzera et al., 2012; Galea et al., 2014; Iannucci et al., 2017; Imposa et al., 2017; Iannucci et al., 2018), HVSR can indicate the directional influence for landslide-affected areas. Additionally, HVSR has been used for a number of purposes, such as site effect response and microzonation, seismic vulnerability assessment, and soil-structure response (Fnais et al., 2010; Fnais et al., 2015a; Fnais et al., 2015b; Alyousef et al., 2015a; Alyousef et al., 2015b; Almadani et al., 2015; Alharbi et al., 2015; Al-Malki et al., 2015; Aldahri et al., 2018.

Al Taif area lies in the southwest of Saudi Arabia and is surrounded by arid terrain and high mountains with steep slopes (Figure 1). In Saudi Arabia, Makkah City is located around 80 km to southeast of Al Taif City. The study area is bounded by longitudes 40° 00′ and 40° 30′E and latitudes 21° 00′ to 21° 30′ N. The city’s recent growth has been determined by this mountainous area. Low-land zones are where urban infrastructure and communities are extended. The native rock is used to construct traditional dwellings. Taif receives rain from the higher edges along the terrain’s slopes. These slopes will be more exposed if they are close to the main roadways. Permanent people who reside along the City’s natural slopes live in areas with a high population density. The Al-Sharai’a earthquake on 8 October 1992, the non-tectonic seismic shock on 12 September 2005, and most recently the earthquake on 28 November 2019, have had a huge impact on Makkah. These earthquakes had a dangerous impact and were felt throughout the majority of the region (Abdelrahman et al., 2019a; Abdelrahman et al., 2019b). Additionally, the city’s proximity to possibly active tectonic structures will make it more susceptible to the landslide phenomenon because it will operate as a more vulnerable place. The disastrous impacts of landslides are readily acknowledged and intensively investigated by several authors worldwide (Mora and Vahrson, 1994; Aleotti and Chowdhury, 1999; Mora-Castro et al., 2012; Somos-Valenzuela et al., 2018; Al-Saud, 2015; Youssef et al., 2015a; Youssef et al., 2015b; Shanmugam and Wang, 2015; Berov et al., 2016; Saputra et al., 2016; Keskinsezer and Ersin, 2019). Gaudio and Wasowski (2007), Rezaei et al. (2018), and Zul Bahrum and Sugianto (2018) have all given their approval for microtremor measurements at some locations around the world.

For the city of Al Taif, where soft soil, even with little thickness, will expedite landslide occurrences and cause significant harm to the populace, soil response effects, such as resonance frequency and amplification characteristics, are crucial. Al Taif can effectively transmit the earthquake’s ground shaking because of its proximity to the Red Sea earthquake source zone. The susceptibility of landslides will be increased by soft sediments and weathered, broken blocks. Therefore, assessing Al Taif’s landslide susceptibility is essential given the area’s growing population, buildings, and impressive economic activity. The study area has never been investigated before, especially in terms of the environmental risks associated with landslides, which makes this study novel. Additionally, integration between two of the most recent techniques for mitigation of landslide hazards, namely, the two remote sensing techniques with the ambient seismic noise measurements, microtremors, in the area. With the help of this innovative technique, we hope to pinpoint Al Taif region’s landslide-prone areas so that they can be avoided in future plans of developmental projects in the area and its surrounding with the best land-use and urban planning.

The data used in this study will be integrated through a GIS-based approach and the methodology carried out through this study as in Figure 2.

Forty-two locations were used to measure the microtremors throughout the study area (Figure 1). The SESAME team’s recommendations were followed when setting up the data collection experimental parameters (SESAME, 2004). Using the STA/LTA anti-trigger algorithm, microtremors were measured for at least 1 hour at each site to ensure long records free from transient conflicts (such as moving vehicles and wind gusts). Data were monitored using a sample rate of 100 sps and filtered using a 0.2–20 Hz bandpass filter. The seismometers were calibrated before recording, installed in good coupling with the surficial soil, orientated horizontally (N–S and E–W), and leveled vertically. The quality and precision of the findings attained with this method depend on the processing sequence. Records in this study were processed using Geopsy software (Wathelet et al., 2006). The accuracy of the microtremor measurements has been confirmed using SESAME team reliability standards. Additionally, the azimuthal rotation of the horizontal-to-vertical spectral ratio with intervals of 10° azimuth was used to determine the direction of the site response.

Land use planning, hazard management, and decision-making about regions vulnerable to landslides are all aided by the spatial prediction of landslides. These maps were created using a variety of techniques in different parts of the world. Areas that are likely to experience a landslide can be predicted based on physical criteria such as bedrock, previous landslide history, slope steepness, and hydrology. Clarifying the significance of early consideration of landslides in planning studies and introducing a method that could be used at all planning phases were the two main goals. All of the aforementioned information aids decision-makers and planners in gaining a practical understanding of ideas and terminology in addition to the crucial factors relating to landslides and landslide hazard mapping.

In this study, 42 sites had microtremor tests done to determine Taif’s susceptibility to landslides. The Nakamura technique was used to process the data in order to determine the dominant frequency and H/V amplitude. Following that, these factors were mapped through Al Taif research area. The rotation of the H/V spectral ratios at azimuth intervals of 10° was used to verify the phenomena of directional site response. The accuracy of the microtremor data was then assessed using a field survey to confirm the directivity results. These findings showed that the greatest and least prominent frequencies in Al Taif were, respectively, 0.3 and 12.75 Hz. The H/V amplitudes were 9.28 at the maximum and 1.17 at the lowest. The high fundamental frequency readings point to a shallow contact with a seismic impedance contrast. At stations near the landslide, there was a clear sense of directionality. The site reaction directions were parallel to the primary direction of the landslide, according to a thorough examination of microtremor stations. These findings show that the microtremor measurements offer a thorough method for assessing landslides. It expedites landslide analyses and lowers the initial expenses of numerical computations.