Natural Hazards Review

The concept of resilience is surging in popularity, but relevant discussions are often disconnected from one field to another. To prompt integration of disparate conversations on resilience, we examine the concept’s origins etymologically, genealogically, and by analyzing the interdependencies of drinking water and public health systems in six academic disciplines and practice-oriented fields. These disciplines are engineering, social work, urban studies, political science, communication, and public health. While the disciplinary resilience literatures are relatively stove-piped from one another’s contexts, they all theorize resilience at multiple levels of analysis. They also engage a range of understandings of how to build resilience in complex systems. This paper brings several conversations together, addressing gaps and resonances in disciplinary conceptualizations of resilience with nine propositions to cultivate interdisciplinary and transdisciplinary discussions and debates. We ground this creative inquiry in real-world examples of water system crises to highlight subthemes among the propositions and stimulate more diverse discussions moving forward. We examine dynamics of interfaces and interactions within and between systems through the Elk River Water chemical contamination in Charleston, West Virginia in 2014. We investigate tensions that arise in knowledge and practice through lead poisoning of public water systems in Washington, DC and Flint, MI. Finally, we consider how change and persistence shape learning through water infrastructure in Southern California. All together, these propositions offer a starting point and a provocation to strengthen theorizing around resilience for critical infrastructure systems.

To reduce life threats and financial losses under hurricane weather and traffic conditions, stakeholders need to make a sequence of decisions (e.g., enforcing traffic control and/or broadcasting travel advisory) with the presence of various uncertainties, which could be formulated as a Markov decision process (MDP). To effectively solve the MDP for optimal operation of hurricane-impacted transportation networks, a risk-informed decision-support framework is proposed consisting of both a decision-making environment and tool. The decision-making environment involves essentially three coupled modules of hurricane hazard, transportation infrastructure, and traffic flow, where the uncertainties from natural environment, built environment, and human behavior are examined. Specifically, multiple correlated hazards for each specific storm are capsulated in the hurricane hazard module, representative hurricane-vulnerable infrastructure components and associated moving vehicles are evaluated in the transportation infrastructure module, and network-level traffic relations are addressed in the traffic flow module. In view of the complexity and intractability of a hurricane-transportation infrastructure-traffic system through full model representation, the efficient low-dimensional modeling approaches including both physics-based analytical and data-driven surrogate models are utilized in all these three simulation modules. Considering an explicit probabilistic model for the decision-making environment (and hence state transition function) is not available, the operation optimization (involving competing objectives of traffic safety and mobility) is accomplished through a deep reinforcement learning-based decision-making tool that learns to act optimally (involving both traffic control and travel advisory actions) by directly interacting with the environment. A hypothetical case study is conducted to verify the applicability and effectiveness of the proposed framework as a testbed for optimal operation of hurricane-impacted transportation networks.

Urban development is promoting aboveground-underground city integration. Underground engineering and surrounding buildings and infrastructure form an underground engineering environment (UEE). In critical disasters, damage to the UEE not only causes chain damage to the surroundings but also aggravates urban resilience. Few studies have focused on UEE resilience and its disaster reduction. This study investigated the emergency restorability of the UEE to fill this research gap. First, we classified the restoration levels based on UEE damage patterns. Second, we performed subregion optimization based on cast-in-situ (CIS) restoration, and technical optimization by utilizing prefabrication and assembly technology (PAT). Third, the CIS/PAT UEE emergency restorability functions were constructed based on these optimizations and the local assembly capacity. Finally, the performance of the CIS/PAT restoration, optimization effect, and local assembly capacity were analyzed using a case study. The results indicated that (1) meticulous subregion optimization significantly reduced environmental work with a 13.5% speed up; (2) technical optimization incorporating PAT had the highest efficiency for improving UEE emergency restorability; (3) PAT restoration enhanced the structural resilience and functional resilience of the UEE by at least 28.34% and 62.27% over CIS restoration, respectively; (4) upper and lower thresholds exist for assembly speed in PAT restoration; (5) there exist three types of assembly capacity in PAT restoration: ring-quantity-sensitive, ring-weight-sensitive, and insensitive; and (6) the adaptive schemes maximized the performance of different local assembly capacities in PAT restoration. The UEE emergency restorability function provides a quantitative assessment tool for the resilience of UEE and a resilience enhancement scheme for disaster reduction.

This article explores the factors that influence citizens’ confidence in public and nonprofit sectors in the context of natural hazards. Utilizing a survey of residents in Austin, Texas, USA, we model the influence of residents’ hazard risk awareness on the perceived confidence in formal institutions that are responsible for hazard preparedness and response. The empirical results of structural equation modeling show that perceived confidence in public and nonprofit sectors is differentially affected by residents’ hazard risk awareness. Importantly, we also find the role of civic participation to positively mediate the effect of hazard risk awareness on the levels of confidence. Our findings contribute to the growing literature on coproduction of services for preparing urban residents for the impacts of climate change and emphasize the importance of community-driven development of community resilience.

Abstract

Dr. Dennis Mileti devoted his career to the study of public warnings disseminated through mass media, word of mouth, and, later, mobile technologies. To date, research on mobile public warnings has focused on standardized elements of messages (e.g., the common alerting protocol). During this same period, however, a variety of private, public, and governmental organizations have adopted mobile alert technologies to disseminate a diverse variety of free-form (nonstandardized) messages. We investigated how the evidence-based guidance developed for mobile public warnings applies to this broader class of free-form mobile alerts and warnings (FMAWs). This study reports an experimental comparison of US residents’ ($N=299$) reactions to notional free-form mobile messages about a safety risk. Experimental conditions compared messages that included safety instructions, ways to seek additional information, expressions of empathy, and emphasizing choice (“choice forward”). Results indicated that message efficacy was greater for FMAWs that gave safety instructions rather than just notifications. Choice-forward FMAWs produced both message efficacy and safety efficacy, or confidence that the person could make a good safety decision for the situation. Application: When the location, timing, and severity of an emergent risk are ambiguous, or when for public safety organizations, they have not yet reached a threshold appropriate for wireless emergency alert messages, a free-form message disseminated through mobile technologies or social media could use choice-forward language and links to more detailed information about possible self-protective action to facilitate just-in-time pre-event education and preparedness.

Practical Applications

Thanks to the scholarship of Dr. Dennis Mileti, guidance for mobile public warnings, such as wireless emergency alert (WEA) messages, works. This guidance helps make people feel safer, and it is becoming standardized through technologies and practices, such as the common alerting protocol. Existing guidance applies to a specific set of circumstances: Experts have identified an imminent threat and can accurately predict its severity, location, and the single best way that people at risk can protect themselves. For many other situations, mobile alert technologies and social media messages may still be useful. These messages would be “free-form,” meaning not following a template or common alerting protocol, so understanding how existing wisdom about warnings applies is key. This study further suggests that free-form messages should empower recipients to seek additional information about a range of self-protective actions and to make the best choice for their individual situation.

While simulation environments for the study of community resilience are rapidly advancing, they remain constrained by the completeness of inventory data. This paper presents an augmented parcel approach leveraging various sources of open data, machine learning modules, and time-evolving rulesets to support Hazus-compatible risk assessments on a wide class of buildings under hurricane wind and flood hazards. These techniques are implemented within the open-source regional hurricane loss assessment workflow of the Natural Hazards Engineering Research Infrastructure (NHERI) SimCenter. Illustrative examples demonstrate building inventory generation in both data-rich and data-scarce environments. The study’s validations of computer vision–based modules underscore the importance of training on “in the wild” images labeled with explicit knowledge of the region and representative of architectural nuances such as carports. Validations further reveal the challenges of simplifying complex contemporary roof geometries to the simplified shapes adopted in Hazus and the criticality of accurate year built data, given the augmented parcel approach’s reliance on time-evolving code-based rulesets. Published field observations collected in Lake Charles, Louisiana, following the landfall of Hurricane Laura, demonstrate that the use of an augmented parcel inventory within the SimCenter’s workflow for Hazus-compatible loss assessments yields damage states consistent with ground-truth observations for minor to moderate damage states. Simulations of extreme damage states (characterized by fewer ground-truth observations) bias toward minor damage for undamaged structures and plateau at moderate damage even for severely damaged and collapsed buildings. This trend persists when considering uncertainty in hazard intensity, as well as the low rates of shutter compliance. Root causes of inconsistencies revealed in this validation exercise will require further processing of street-level panoramic images to generate more samples of severely damaged and collapsed buildings as well as post-2007 construction.

Two backpropagation neural network (BPNN) models were constructed to predict two historic geomagnetic storms that occurred in September 1999 and October 2003. The disturbance storm time (Dst) indices from January 1, 1999, to December 31, 2014 (coordinated universal time, UTC), were used as the training and test data sets for cross-validation in order to verify and validate the reliability and robustness of the two BPNN models, and yielded reasonable, predicted results. A large correlation coefficient (R) and low root mean square error (RMSE) were obtained, verifying the reliability of the two BPNN models. The predicted Dst indices can be provided for giving inputs in advance (i.e., any future time). Therefore, this analyzed method can serve as an excellent real-time prediction (RTP). To test the ability and possibility of the long-term prediction (LTP) obtained using the two BPNN models, the Dst indices were examined, which corresponded to two significant historic large geomagnetic storms that occurred in August 1972 and March 1989. For the both BPNN models, after evaluating their learning procedure, the time-dependence of LTP, the dependence of the predicted errors on the time period length of training data sets, and the variance by learning process, we found that they were stable models for the RTP and LTP.

Wood-frame structures are used in almost 90% of residential buildings in the United States. It is thus imperative to rapidly and accurately assess the damage of wood-frame structures in the wake of an earthquake event. This study aims to develop a machine-learning-based seismic classifier for a portfolio of 6,113 wood-frame structures near the New Madrid Seismic Zone (NMSZ) in which synthesized ground motions are adopted to characterize potential earthquakes. This seismic classifier, based on a multilayer perceptron (MLP), is compared with existing fragility curves developed for the same wood-frame buildings near the NMSZ. This comparative study indicates that the MLP seismic classifier and fragility curves perform equally well when predicting minor damage. However, the MLP classifier is more accurate than the fragility curves in prediction of moderate and severe damage. Compared with the existing fragility curves with earthquake intensity measures as inputs, machine-learning-based seismic classifiers can incorporate multiple parameters of earthquakes and structures as input features, thus providing a promising tool for accurate seismic damage assessment in a portfolio scale. Once trained, the MLP classifier can predict damage classes of the 6,113 structures within 0.07 s on a general-purpose computer.

The failure of stream-crossing bridges during flood events can endanger human life, impede evacuation planning and emergency response efforts, and cause long-term disruptions to transportation systems and local and regional economies. Expanding urban development and changing precipitation patterns due to climate change can increase the frequency and intensity of high-flow conditions at bridge locations, potentially exacerbating flood-related damages. Thus, there is a critical need to evaluate how shifts in extreme precipitation and continued urban growth will affect peak flows and hydraulic conditions experienced by bridges. This study applies hydrologic and hydraulic modeling to investigate the individual and combined effects of climate change and urbanization on the magnitude of peak discharge, bridge freeboard encroachment, and flow velocity at stream-crossing bridge locations in 19 watersheds of Harris County, Texas. The developed framework provides a screening tool to assess the vulnerability of regional bridge infrastructure to high-flow events and to prioritize maintenance, retrofitting, and replacement efforts. The model results demonstrate that peak flows during a 1% annual exceedance probability event can increase by up to 40% by 2100 when considering the combined impacts of climate change and urbanization, with changes in precipitation generally contributing more than changes in land use. In 16 of the 19 studied watersheds, the 1% annual exceedance probability event is projected to occur at least twice as frequently by 2100. Across the region, an additional 36 bridges experience loss of freeboard due to the effects of urbanization and climate change, while notable increases in flow velocity ($\ge 10\%$) are observed at 93 bridge locations, five of which are considered scour critical. Overall, the results of this study show that climate change and urbanization will influence the hydraulic conditions at bridge locations and therefore should be carefully accounted for in the design, operation, and maintenance of bridge infrastructure in the future.

Amid rising global disaster risks, governmental and nongovernmental organizations have called for increased technical assistance to build capacity within communities to build safer housing. However, limited research has evaluated these approaches to building technical construction capacity. This study discusses the process of collaboratively designing an approach to build construction capacity for safer housing, evaluating this process through measures of builders’ self-efficacy and knowledge of mitigation measure efficacy. The capacity-building focuses particularly on the appropriate use of hurricane straps in wood roof construction in Puerto Rico, which was identified as an area where engineering recommendations did not align with many builders’ perceptions of safety. Three phases were used to design, implement, and assess this capacity-building approach. First, we interviewed staff in local nongovernmental organizations (NGOs) to identify propositions for building technical construction capacity in Puerto Rico. We then used these propositions as a framework to design the capacity-building approach. Finally, we piloted and evaluated this approach with existing trainers and community members who are building and repairing housing in Puerto Rico, interviewing them after implementation to analyze shifts in self-efficacy and knowledge of mitigation measure efficacy. The results reveal interviewees’ increased perceived self-efficacy and knowledge of mitigation measure efficacy following capacity-building, leading them to express increased willingness to mitigate hurricane risks to housing with appropriately installed hurricane straps. Overall, this study provides evidence that centering people’s perspectives is needed in technical construction capacity-building to influence builders’ self-efficacy and knowledge of mitigation measure efficacy and promote safer housing construction and suggests an approach that can be used to center these perspectives.

Investigating the relationship between water contents and the dynamic response of soil cut slopes under the action of train loading is beneficial to slope safety assessment. The effect of water contents on dynamic response characteristics and the potential failure mechanism of the slope under cyclic loading was investigated using geotechnical model tests and numerical calculations. It was found that a slope with higher water content showed a larger vibration intensity. The main effect of heavy-haul train loading on the slope was always concentrated in the slope foot or the shallow depths. As the propagation distance increased, the far-field vibration was mainly low frequency. Dynamic stress from the train loading was much larger close to the slope foot, and it was prone to forming a localized low-reliability zone with higher water content at an early time. As the water content increased, the development scope of the potential sliding surface tended to extend into the slope, which can lead to slope failure ahead. Also, the shear strain increment caused by two passing trains was more than that by a single passing train, which showed no obvious periodicity with random fluctuations significantly. In addition, a dynamic strength reduction method to calculate the safety factor of the soil slope under train cyclic loading is proposed to evaluate the slope stability better.

Abstract

The upward tendency of global disasters and associated losses urgently calls for enhancing disaster education (DE) and professional training. Although postsecondary education institutions (PSEIs) play an essential role in this educational mission, a comprehensive understanding of DE development in PSEIs remains unclear. In response to this knowledge deficit, this article employed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach to explore the current development of DE in PSEIs through reviewing 76 publications from 2011 to 2022 through a mixed-method analysis process (univariate analysis and thematic inquiry). The quantitative analysis outcomes illustrate that the studies were unevenly distributed globally. The qualitative findings demonstrate the following fourfold characteristics of DE in PSEIs: (1) in terms of pedagogical characteristics of DE, there is a call for a radical shift in pedagogical strategies because the conventional teaching approaches are inadequate in tackling the potential risks and emerging hazards; (2) disciplinary and interdisciplinary perspectives have been integrated into DE, addressing diverse disasters and their differential influences; (3) through community-based partnerships, PSEIs can provide informal and nonformal education to affected individuals and communities; and (4) interconnections between DE and other educational approaches that broadly address global climate change have been recognized, contributing to resilience and sustainability. Based on these outcomes, this study recommends future DE-specific research could focus on (1) exploring potential radical pedagogical approaches to respond to emerging risks and hazards, (2) stimulating cross-disciplinary collaboration to address the full spectrum of disasters’ societal impacts, and (3) increasing collaboration between PSEIs and nonacademic organizations to raise grassroots awareness through informal DE.

Practical Applications

The increasing number of disasters worldwide encourages the development of DE, especially at PSEIs. A nuanced understanding of the DE development in PSEIs will identify the strengths and areas for improvement, better preparing future researchers, educators, practitioners, and policymakers. Having focused on DE at PSEIs, this literature review examined 76 journal articles published from 2011 to 2022. Disasters are a global issue, and the literature review outcomes show the uneven distribution of DE on a global scale. Although interdisciplinary approaches have been applied to DE in PSEIs to examine disaster’s diverse impacts, innovative pedagogies are needed to address the emerging issues associated with extreme events. DE provides community outreach activities to educate and support the individuals and communities hit by disasters. DE also joins other educational missions to contribute to the global issues of climate change, resilience, and sustainability. These findings suggest that future improvement could focus on developing novel pedagogies, enhancing cross-disciplinary collaboration, and promoting community outreach and public education.

Abstract

This paper examines communicating about complex challenges facing populations experiencing homelessness and examining alternative ways to communicate with these populations in times of crisis. Utilizing a case study approach, the researchers focused on cities in New York and California due to the high numbers of unhoused populations and individuals experiencing homelessness within these states. Experts in crisis and emergency management have provided invaluable recommendations on the general protection of life and property; however, individuals identifying as homeless or unhoused share characteristics that heighten their risk and vulnerability, such as lower socioeconomic status, unstable resilience levels prior to a crisis, and geographic disparities. The intersection of these attributes presents unique challenges for emergency managers and relevant public administrators when implementing crisis response and communication strategies, such as one-way written and oral messaging over media and social networks, along with enacting any new protocols needed in relation to the crisis itself, like targeted outreach. This paper discusses the evolution of crisis communication for homeless and unhoused populations, national statistics, along with analysis of case studies for Los Angeles, California, and New York City, New York. The paper concludes with eight major themes related to communicating with homeless and unhoused populations during times of crises and practical recommendations for emergency managers and relevant public administrators.

Practical Applications

It is critical that representatives and advocates for populations experiencing homelessness, or are unhoused, are involved in the dialogue and planning efforts. Stable investment in communication infrastructure and crisis communication programs and services is vital to assist homeless and unhoused populations in gaining access to the Internet and cell phone providers, outreach campaigns for printed messaging, alternative locations for media reporting, and receiving critical information during crisis and noncrisis periods. In terms of messaging, crisis-related alerts (oral and written) must be reviewed to ensure clarity in tone to avoid ambiguity and identify and remove any stigmatization language to open a two-way dialogue. Emergency managers and related public administrators must use a diverse array of communication avenues and strategies, such as social media and electronic messages (i.e., cell phones, media statements, and business signage) and traditional methods (i.e., leaflets at social service providers and shelters) with a focus on creating dialogue and empowering individuals to engage and seek out services. In addition, a crisis team dedicated to communicating with homeless and unhoused populations in times of crisis is needed along with dedicated resources for staffing, communication infrastructure, programs, and services.

The US utilizes a market-driven resourcing approach for post-disaster residential housing reconstruction, where homeowners must quickly secure financial capital to compete for limited construction resources. However, homeowners’ financial capital resource availability is driven by household socioeconomic status. Construction resource availability is constrained by the available supply of labor and material within the local construction industry market. Since resource availability varies geographically under a market-driven model, this study investigates the spatially varying influence of pre-disaster construction and socioeconomic resource availability on post-disaster residential housing recovery. County-level data was collected and analyzed for US states with federal major disaster declarations following Hurricane Sandy, namely Connecticut, New Jersey, New York, and Rhode Island. Geographically weighted regression (GWR) model results show how the uneven geographic distribution of pre-disaster construction and socioeconomic resources creates regional disparities in post-disaster housing recovery outcomes.

Abstract

This research was conducted with the aim of producing flood susceptible area maps of the Zahedan catchment in Iran during lack of data using remote sensing. To determine the factors to be included in the model, factors identified in previous studies were prepared using remote sensing and were evaluated by the information gain ratio (IGR) method and the multicollinearity diagnostic test, and ultimately 10 factors with the highest IGR values were chosen as the most effective factors in the region. The flood inventory map was produced by processing the Sentinel-1 satellite data. The gathered data were used to map flood susceptibility maps with the SVM model optimized with IWO and ACO. The prediction accuracy of the models was evaluated in terms of root mean square error (RMSE), mean absolute error (MAE), and the area under the receiver operating characteristic (ROC) curve (AUC). While both optimization algorithms are effective in improving the performance of SVM, the hybrid of SVM with IWO shows the best performance in terms of statistical measures and Friedman test results. The results of the study confirm the good performance of the proposed models in spatial prediction of flood susceptible areas. Since over half of the urban lands of the city of Zahedan are at moderate to very high risk of flooding, this area needs more attention in terms of flood prevention and control measures.

Practical Applications

Prevention of flood events will not always be practical, so using flood mitigation methods is considered the best strategy for flood risk management. The purpose of using these methods is to significantly reduce flood damage by evaluating flood-prone areas and taking protective measures in areas. In this study, data mining methods were used to select parameters affecting floods in the region. One of the problems of the study area is the lack or absence of information. Considering that there was no complete information about the parameters required in this research in the studied area, information was obtained using remote sensing and then optimization was done using the data obtained by the support vector machine model. The flood sensitivity map of the area was drawn. The flood sensitivity map will help to identify high-risk and low-risk areas for flood prevention planning. The results showed that 9% of the studied area is in very high susceptibility, 3% in high susceptibility, 3% in moderate susceptibility, 7% in low susceptibility and 78% in very low susceptibility. Also, in the meantime, more than half of Zahedan’s urban area is located in the medium to very high sensitivity area, which should be given special attention.

Abstract

A reliability-based procedure is proposed to assess and compare the economic effectiveness of three retrofit alternatives for actual schools located in three zones with different seismic hazards. The procedure calculates the school failure probability under the damaged conditions, by considering the damages produced by the 2017 earthquake in Mexico, and the failure probability under each retrofit alternative, from the seismic spectra corresponding to the seismic hazard of each site. The best retrofit alternative is the one with the minimum expected life-cycle cost for each school. The global failure probability is calculated according to the Ditlevsen’s bounds, and the limit states consist of the event in which the maximum moment exceeds the bending capacity for beams, and the combined axial and bending forces exceed the capacity of the columns; the maximum responses are obtained through nonlinear analyses. The proposed retrofit alternatives are column and beam jacketing with steel angles and plates, increment of the concrete cross sections of columns and beams, and the addition of bracing. The effectiveness of each alternative, in terms of failure probability reduction and expected life-cycle cost, was assessed for each school. The procedure may be used to update the current retrofit specifications for schools.

Practical Applications

This paper presents a procedure to select the best retrofit strategy among several proposed alternatives to retrofit a school damaged by earthquakes. The proposed alternatives are the jacketing or reinforcement of the concrete sections, the addition of steel cover plates, and the addition of steel bracings, all in the critical sections or zones. For the selection of the best alternative, a cost–benefit analysis is proposed to balance the cost, in terms of the expected life-cycle cost (which includes the cost of failure consequences), with the benefit, which consists of the improvement of structural reliability. These analyses provide information to the owner or designer to make decisions about the desired level of reliability for the corresponding investment to protect schools against future earthquakes at the school location. The methodology was illustrated for three actual schools. The procedure considers the level of risk, according to the number of students exposed to the earthquakes and the level of seismic hazard for each school location. With the proper adaptations to other specific cases, the procedure may complement the engineering judgment, experience, and best practices of designers to guide the search for the optimal retrofit of schools under seismic hazard.

This study presents landslide susceptibility mapping by using coupled GIS statistical analysis and interferometry synthetic aperture radar (InSAR) data applied to a road corridor in West Sulawesi, Indonesia. Landslide-contributing factors in the road corridor including slope angle, distance to drainage, lithology, distance to road, distance to tectonic fault, and rainfall, were selected based on knowledge of landslide triggering mechanisms, numerically analyzed with the multicriteria decision of the analytical hierarchy process (AHP), and controlled with the variance inflation factor (VIF) in the multicollinearity analysis. Then, the data associated with slope angle, the distance of the road to the slope, and the distance of the road to natural drainage were obtained from the digital elevation model (DEM) of the road corridor. In addition, the data of the lithological type of the area in the road corridor and the distance of the road to the tectonic faults were derived from a geology map, whereas the data of 10 years of daily rainfall were collected from three rainfall stations in the proximity of the road corridor and employed as rainfall data. Causal relations between those contributing factors and landslide occurrences were identified, statistically analyzed with the AHP, and numerically converted as landslide ratings into a GIS statistics–based landslide susceptibility map (LSM). In a parallel way, the interferogram of Sentinel-1’s synthetic aperture radar images of the road corridor was also generated to observe the ground movement rate. The observed ground movement rates were then numerically converted into landslide ratings in the InSAR-based LSM. By overlaying these maps, a coupled GIS statistics and InSAR-based LSM of the road corridor was generated. To validate its accuracy, the landslide density index ($R$-index) was calculated, in which the highly and very highly susceptible zones in the LSM were compared with the actual sliding areas in the landslide inventory data. In comparison with the GIS-AHP-based LSM from a prior study, which had an $R$-index of 91.03%, the GIS statistical analysis and InSAR-based LSM’s $R$-index was determined to be 97.09%, suggesting high accuracy and improved prediction. The results indicated that creating a landslide susceptibility map using GIS statistical analysis and InSAR would be beneficial in reducing the risk of landslides for road infrastructure.