Abstract:
keep_len="250">Housing in informal settlements often lacks construction techniques that adopt criteria of resilience to natural hazards. Smartphones are rapidly diffusing in economically developing countries. The aim of this study was to assess the current use of smartphones by the masons of the informal settlements of Iringa, Tanzania, and to identify pathways for improving their construction practices. Data were collected through a mixed method approach that includes in-depth interviews, surveys, and a focus group with masons. The results reveal that only a few masons received formal training, most of them have never interacted with a qualified engineer nor consulted trained professionals when needed. Most masons own a smartphone and they extensively use it to gather technical information from the web, transfer money through mobile payments, share images of construction details, and promote their work on social media. The broad use of smartphones shows potential for enhancing construction quality. This article presents a unique analysis of the use of smartphones in the construction of informal settlements in Tanzania, which could be extended to other countries. Based on the findings, new strategies are proposed to engage with local stakeholders and foster the exchange of technical knowledge for safer settlements via smartphones. Housing in informal settlements often lacks construction techniques that adopt criteria of resilience to natural hazards. Smartphones are rapidly diffusing in economically developing countries. The aim of this study was to assess the current use of smartphones by the masons of the informal settlements of Iringa, Tanzania, and to identify pathways for improving their construction practices. Data were collected through a mixed method approach that includes in-depth interviews, surveys, and a focus group with masons. The results reveal that only a few masons received formal training, most of them have never interacted with a qualified engineer nor consulted trained professionals when needed. Most masons own a smartphone and they extensively use it to gather technical information from the web, transfer money through mobile payments, share images of construction details, and promote their work on social media. The broad use of smartphones shows potential for enhancing construction quality. This article presents a unique analysis of the use of smartphones in the construction of informal settlements in Tanzania, which could be extended to other countries. Based on the findings, new strategies are proposed to engage with local stakeholders and foster the exchange of technical knowledge for safer settlements via smartphones.

Abstract:
keep_len="250">This study aimed to evaluate the development of healthcare teamwork during and after the collaboration tabletop exercises, through observation and interview methods. Integration and maturity theoretical models were employed to explain the collaborative challenges in teams that may suffer from unequally distributed power, hierarchies, and fragmentation. Using three-level collaboration tabletop exercises and the Command and control, Safety, Communication, Assessment, Treatment, Triage, Transport (CSCATTT) instrument, 100 healthcare workers were observed during each step in the implementation of the CSCATTT instrument using two simulated scenarios. The results show a lack of integration and team maturity among participants in the first scenario, leading to the delayed start of the activity, task distribution, and decision making. These shortcomings were improved in the second scenario. In-depth interviews with 20 participants in the second phase of the study revealed improved knowledge and practical skills, self-confidence, and ability in team building within trans-professional groups in the second scenario, which in concordance with the integration theory, was due to the attempts made in the first scenario. Additionally, there was an improvement in the team's maturity, which in concordance with the maturity theory, was due to the knowledge and practical skills during scenario plays. These results indicate the importance of continuous tabletop training, and the use of CSCATTT as a collaborative instrument, to promote the development of collaboration and to test the concept of preparedness. This study aimed to evaluate the development of healthcare teamwork during and after the collaboration tabletop exercises, through observation and interview methods. Integration and maturity theoretical models were employed to explain the collaborative challenges in teams that may suffer from unequally distributed power, hierarchies, and fragmentation. Using three-level collaboration tabletop exercises and the Command and control, Safety, Communication, Assessment, Treatment, Triage, Transport (CSCATTT) instrument, 100 healthcare workers were observed during each step in the implementation of the CSCATTT instrument using two simulated scenarios. The results show a lack of integration and team maturity among participants in the first scenario, leading to the delayed start of the activity, task distribution, and decision making. These shortcomings were improved in the second scenario. In-depth interviews with 20 participants in the second phase of the study revealed improved knowledge and practical skills, self-confidence, and ability in team building within trans-professional groups in the second scenario, which in concordance with the integration theory, was due to the attempts made in the first scenario. Additionally, there was an improvement in the team's maturity, which in concordance with the maturity theory, was due to the knowledge and practical skills during scenario plays. These results indicate the importance of continuous tabletop training, and the use of CSCATTT as a collaborative instrument, to promote the development of collaboration and to test the concept of preparedness.

Abstract:
keep_len="250">Overhead electrical power distribution systems (PDS) are very susceptible to extreme wind hazards. Power outages can cause catastrophic consequences, including economic losses, loss of critical services, and disruption to daily life. Therefore, it is very important to model the resilience of PDS against extreme winds to support disaster planning. While several frameworks currently exist to assess the resilience of PDS subjected to extreme winds, these frameworks do not systematically consider the tree-failure risk. In other words, there is no integrated framework that can simultaneously consider tree failures, PDS component failures induced by falling trees, resilience assessment, and evaluation of resilience enhancement with vegetation management. Therefore, this study proposed an integrated simulation framework to model the resilience of PDS against extreme winds, which includes tree fragility modeling, PDS fragility modeling, PDS component failure estimation, system performance evaluation, system restoration modeling, and resilience enhancement evaluation. The framework is demonstrated with a power distribution network in Oklahoma. The results show that the estimated system resilience will reduce if tree failures are considered. Crown thinning can effectively enhance the system's resilience, but the effectiveness is affected by both wind speed and direction. Overhead electrical power distribution systems (PDS) are very susceptible to extreme wind hazards. Power outages can cause catastrophic consequences, including economic losses, loss of critical services, and disruption to daily life. Therefore, it is very important to model the resilience of PDS against extreme winds to support disaster planning. While several frameworks currently exist to assess the resilience of PDS subjected to extreme winds, these frameworks do not systematically consider the tree-failure risk. In other words, there is no integrated framework that can simultaneously consider tree failures, PDS component failures induced by falling trees, resilience assessment, and evaluation of resilience enhancement with vegetation management. Therefore, this study proposed an integrated simulation framework to model the resilience of PDS against extreme winds, which includes tree fragility modeling, PDS fragility modeling, PDS component failure estimation, system performance evaluation, system restoration modeling, and resilience enhancement evaluation. The framework is demonstrated with a power distribution network in Oklahoma. The results show that the estimated system resilience will reduce if tree failures are considered. Crown thinning can effectively enhance the system's resilience, but the effectiveness is affected by both wind speed and direction.

Abstract:
keep_len="250">Climate change can lead to and intensify drought disasters. Quantifying the vulnerability of disaster-affected elements is significant for understanding the mechanisms that transform drought intensity into eventual loss. This study proposed a growth-stage-based drought vulnerability index (GDVI) of soybean using meteorological, groundwater, land use, and field experiment data and crop growth model simulation. The CROPGRO-Soybean model was used to simulate crop growth and water deficit. Four growth stages were considered since the sensitivity of soybean to drought is strictly related to the growth stage. The GDVI was applied to the Huaibei Plain, Anhui Province, China, with the goal of quantifying the spatiotemporal characteristics of soybean drought vulnerability in typical years and growth stages. The results show that:(1) The sensitivity of leaf-related parameters exceeded that of other parameters during the vegetative growth stage, whereas the top weight and grain yield showed a higher sensitivity in the reproductive growth stage; (2) A semi-logarithmic law can describe the relationship between the drought sensitivity indicators and the GDVI during the four growth stages. The pod-filling phase is the most vulnerable stage for water deficit and with the highest loss upper limit (over 70%); (3) The 2001 and 2002 seasons were the driest time during 1997-2006. Fuyang and Huainan Cities were more vulnerable to drought than other regions on the Huaibei Plain in 2001, while Huaibei and Suzhou Cities were the most susceptible areas in 2002. The results could provide effective decision support for the categorization of areas vulnerable to droughts. Climate change can lead to and intensify drought disasters. Quantifying the vulnerability of disaster-affected elements is significant for understanding the mechanisms that transform drought intensity into eventual loss. This study proposed a growth-stage-based drought vulnerability index (GDVI) of soybean using meteorological, groundwater, land use, and field experiment data and crop growth model simulation. The CROPGRO-Soybean model was used to simulate crop growth and water deficit. Four growth stages were considered since the sensitivity of soybean to drought is strictly related to the growth stage. The GDVI was applied to the Huaibei Plain, Anhui Province, China, with the goal of quantifying the spatiotemporal characteristics of soybean drought vulnerability in typical years and growth stages. The results show that:(1) The sensitivity of leaf-related parameters exceeded that of other parameters during the vegetative growth stage, whereas the top weight and grain yield showed a higher sensitivity in the reproductive growth stage; (2) A semi-logarithmic law can describe the relationship between the drought sensitivity indicators and the GDVI during the four growth stages. The pod-filling phase is the most vulnerable stage for water deficit and with the highest loss upper limit (over 70%); (3) The 2001 and 2002 seasons were the driest time during 1997-2006. Fuyang and Huainan Cities were more vulnerable to drought than other regions on the Huaibei Plain in 2001, while Huaibei and Suzhou Cities were the most susceptible areas in 2002. The results could provide effective decision support for the categorization of areas vulnerable to droughts.

Abstract:
keep_len="250">Industrial accidents have shown that many people can be affected, such as in Seveso, Italy, in 1976. Industrial accidents in nuclear power plants have also led to fatalities and evacuations. To better guide preparedness against and mitigation of industrial accidents, an assessment is necessary to evaluate hazard exposure and the type of potentially vulnerable social groups that need to be taken into account. This study conducted a spatial assessment of three types of industrial facilities in Germany:facilities registered under the Seveso Directive, chemical parks, and nuclear power plants. The method consisted of a spatial assessment using a Geographic Information System of exposure around hazardous sites registered under the Seveso Directive in Germany and of census data to analyze social vulnerability. Hazards analyzed included industrial accidents and earthquakes. The results revealed that most industrial sites are in urban areas and that population density, the numbers of foreigners, and smaller housing unit sizes are higher in close proximity to these sites. The buffer zones analyzed in circles between 1 and 40 km show a decreasing vulnerability with more distance. This can guide emergency management planners and other stakeholders to better prepare for major accidents and better devise disaster risk reduction strategies specifically for different social groups. Industrial accidents have shown that many people can be affected, such as in Seveso, Italy, in 1976. Industrial accidents in nuclear power plants have also led to fatalities and evacuations. To better guide preparedness against and mitigation of industrial accidents, an assessment is necessary to evaluate hazard exposure and the type of potentially vulnerable social groups that need to be taken into account. This study conducted a spatial assessment of three types of industrial facilities in Germany:facilities registered under the Seveso Directive, chemical parks, and nuclear power plants. The method consisted of a spatial assessment using a Geographic Information System of exposure around hazardous sites registered under the Seveso Directive in Germany and of census data to analyze social vulnerability. Hazards analyzed included industrial accidents and earthquakes. The results revealed that most industrial sites are in urban areas and that population density, the numbers of foreigners, and smaller housing unit sizes are higher in close proximity to these sites. The buffer zones analyzed in circles between 1 and 40 km show a decreasing vulnerability with more distance. This can guide emergency management planners and other stakeholders to better prepare for major accidents and better devise disaster risk reduction strategies specifically for different social groups.

Abstract:
keep_len="250">Rapid damage prediction for wind disasters is significant in emergency response and disaster mitigation, although it faces many challenges. In this study, a 1-km grid of wind speeds was simulated by the Holland model using the 6-h interval records of maximum wind speed (MWS) for tropical cyclones (TC) from 1949 to 2020 in South China. The MWS during a TC transit was used to build damage rate curves for affected population and direct economic losses. The results show that the Holland model can efficiently simulate the grid-level MWS, which is comparable to the ground observations with R² of 0.71 to 0.93 and mean absolute errors (MAEs) of 3.3 to 7.5 m/s. The estimated damage rates were in good agreement with the reported values with R²=0.69-0.87 for affected population and R²=0.65-0.84 for GDP loss. The coastal areas and the Guangdong-Hong Kong-Macao Greater Bay Area have the greatest risk of wind disasters, mainly due to the region's high density of population and developed economy. Our proposed method is suitable for rapid damage prediction and supporting emergency response and risk assessment at the community level for TCs in the coastal areas of China. Rapid damage prediction for wind disasters is significant in emergency response and disaster mitigation, although it faces many challenges. In this study, a 1-km grid of wind speeds was simulated by the Holland model using the 6-h interval records of maximum wind speed (MWS) for tropical cyclones (TC) from 1949 to 2020 in South China. The MWS during a TC transit was used to build damage rate curves for affected population and direct economic losses. The results show that the Holland model can efficiently simulate the grid-level MWS, which is comparable to the ground observations with R² of 0.71 to 0.93 and mean absolute errors (MAEs) of 3.3 to 7.5 m/s. The estimated damage rates were in good agreement with the reported values with R²=0.69-0.87 for affected population and R²=0.65-0.84 for GDP loss. The coastal areas and the Guangdong-Hong Kong-Macao Greater Bay Area have the greatest risk of wind disasters, mainly due to the region's high density of population and developed economy. Our proposed method is suitable for rapid damage prediction and supporting emergency response and risk assessment at the community level for TCs in the coastal areas of China.

Abstract:
keep_len="250">Fast and accurate prediction of urban flood is of considerable practical importance to mitigate the effects of frequent flood disasters in advance. To improve urban flood prediction efficiency and accuracy, we proposed a framework for fast mapping of urban flood:a coupled model based on physical mechanisms was first constructed, a rainfall-inundation database was generated, and a hybrid flood mapping model was finally proposed using the multi-objective random forest (MORF) method. The results show that the coupled model had good reliability in modelling urban flood, and 48 rainfall-inundation scenarios were then specified. The proposed hybrid MORF model in the framework also demonstrated good performance in predicting inundated depth under the observed and scenario rainfall events. The spatial inundated depths predicted by the MORF model were close to those of the coupled model, with differences typically less than 0.1 m and an average correlation coefficient reaching 0.951. The MORF model, however, achieved a computational speed of 200 times faster than the coupled model. The overall prediction performance of the MORF model was also better than that of the k-nearest neighbor model. Our research provides a novel approach to rapid urban flood mapping and flood early warning. Fast and accurate prediction of urban flood is of considerable practical importance to mitigate the effects of frequent flood disasters in advance. To improve urban flood prediction efficiency and accuracy, we proposed a framework for fast mapping of urban flood:a coupled model based on physical mechanisms was first constructed, a rainfall-inundation database was generated, and a hybrid flood mapping model was finally proposed using the multi-objective random forest (MORF) method. The results show that the coupled model had good reliability in modelling urban flood, and 48 rainfall-inundation scenarios were then specified. The proposed hybrid MORF model in the framework also demonstrated good performance in predicting inundated depth under the observed and scenario rainfall events. The spatial inundated depths predicted by the MORF model were close to those of the coupled model, with differences typically less than 0.1 m and an average correlation coefficient reaching 0.951. The MORF model, however, achieved a computational speed of 200 times faster than the coupled model. The overall prediction performance of the MORF model was also better than that of the k-nearest neighbor model. Our research provides a novel approach to rapid urban flood mapping and flood early warning.

Abstract:
keep_len="250">Extreme surges and rainfall represent major driving factors for compound flooding in estuary regions along the Chinese coast. The combined effect of extreme surges and rainfall (that is, compound floods) might lead to greater impacts than if the drivers occurred in isolation. Hence, understanding the frequency and severity of compound flooding is important for improving flood hazard assessment and compound flood resilience in coastal cities. In this study, we examined the dependence between extreme surges and corresponding rainfall events in 26 catchments along the Chinese coastline during typhoon and non-typhoon seasons using copula functions, to identify where the two drivers more often occur together and the implication for flood management in these locations. We found that the interaction between flood drivers is statistically significant in 10 catchments located around Hainan Island (south) and Shanghai, where surge peaks occur mainly during the typhoon season and around the Bohai Sea (north), where surge peaks occur mainly during the non-typhoon season. We further applied the copula-based framework to model the dependence between surge peaks and associated rainfall and estimate their joint and conditional probability in two specific locations-Hainan Island and the Bohai Sea, where the correlation between flood drivers is statistically significant. We observed that in Hainan Island where most of the surge peaks occur during the typhoon season, extreme rainfall events during the typhoon season are generally more intense compared to annual maxima rainfall. In contrast, around the Bohai Sea where surge peaks occur mainly outside the typhoon season, rainfall is less intense than annual maxima rainfall. These results show that the interaction between extreme surges and rainfall can provide valuable insight when designing coastal and urban infrastructure, especially in highly populated urban areas prone to both coastal and pluvial flooding, such as many Chinese coastal cities. Extreme surges and rainfall represent major driving factors for compound flooding in estuary regions along the Chinese coast. The combined effect of extreme surges and rainfall (that is, compound floods) might lead to greater impacts than if the drivers occurred in isolation. Hence, understanding the frequency and severity of compound flooding is important for improving flood hazard assessment and compound flood resilience in coastal cities. In this study, we examined the dependence between extreme surges and corresponding rainfall events in 26 catchments along the Chinese coastline during typhoon and non-typhoon seasons using copula functions, to identify where the two drivers more often occur together and the implication for flood management in these locations. We found that the interaction between flood drivers is statistically significant in 10 catchments located around Hainan Island (south) and Shanghai, where surge peaks occur mainly during the typhoon season and around the Bohai Sea (north), where surge peaks occur mainly during the non-typhoon season. We further applied the copula-based framework to model the dependence between surge peaks and associated rainfall and estimate their joint and conditional probability in two specific locations-Hainan Island and the Bohai Sea, where the correlation between flood drivers is statistically significant. We observed that in Hainan Island where most of the surge peaks occur during the typhoon season, extreme rainfall events during the typhoon season are generally more intense compared to annual maxima rainfall. In contrast, around the Bohai Sea where surge peaks occur mainly outside the typhoon season, rainfall is less intense than annual maxima rainfall. These results show that the interaction between extreme surges and rainfall can provide valuable insight when designing coastal and urban infrastructure, especially in highly populated urban areas prone to both coastal and pluvial flooding, such as many Chinese coastal cities.

Abstract:
keep_len="250">Based on the seismic source model in the Fifth Generation Seismic Ground Motion Parameters Zonation Map of China (FGSGMPZMC), a new seismic fault model, the new zonation of seismic risk areas (SRAs), and the estimation of seismicity rates for 2021-2030, this study constructed a new time-dependent seismic source model of China's mainland, and used the probabilistic seismic hazard analysis method to calculate seismic hazard by selecting the ground motion models (GMMs) suitable for seismic sources in China. It also provided the probabilities of China's mainland being affected by earthquakes of modified Mercalli intensity (MMI) VI, VII, VIII, IX, and ≥ X in 2021-2030. The spatial pattern of seismic hazards presented in this article is similar to the pattern of the FGSGMPZMC, but shows more details. The seismic hazards in this study are higher than those in the FGSGMPZMC in the SRAs and fault zones that can produce large earthquakes. This indicates that the seismic source model construction in this study is scientific and reasonable. There are certain similarities between the results in this study and those of Rong et al. (2020) and Feng et al. (2020), but also disparities for specific sites due to differences in seismic source models, seismicity parameters, and GMMs. The results of seismic hazard may serve as parameter input for future seismic risk assessments. The hazard results can also be used as a basis for the formulation of earthquake prevention and mitigation policies for China's mainland. Based on the seismic source model in the Fifth Generation Seismic Ground Motion Parameters Zonation Map of China (FGSGMPZMC), a new seismic fault model, the new zonation of seismic risk areas (SRAs), and the estimation of seismicity rates for 2021-2030, this study constructed a new time-dependent seismic source model of China's mainland, and used the probabilistic seismic hazard analysis method to calculate seismic hazard by selecting the ground motion models (GMMs) suitable for seismic sources in China. It also provided the probabilities of China's mainland being affected by earthquakes of modified Mercalli intensity (MMI) VI, VII, VIII, IX, and ≥ X in 2021-2030. The spatial pattern of seismic hazards presented in this article is similar to the pattern of the FGSGMPZMC, but shows more details. The seismic hazards in this study are higher than those in the FGSGMPZMC in the SRAs and fault zones that can produce large earthquakes. This indicates that the seismic source model construction in this study is scientific and reasonable. There are certain similarities between the results in this study and those of Rong et al. (2020) and Feng et al. (2020), but also disparities for specific sites due to differences in seismic source models, seismicity parameters, and GMMs. The results of seismic hazard may serve as parameter input for future seismic risk assessments. The hazard results can also be used as a basis for the formulation of earthquake prevention and mitigation policies for China's mainland.

Abstract:
keep_len="250">China's economic development is closely related to oil and gas resources, and the country is investing heavily in pipeline construction. Slope geological hazards seriously affect the long-term safe operation of buried pipelines, usually causing pipeline leakage, property and environmental losses, and adverse social impacts. To ensure the safety of pipelines and reduce the probability of pipeline disasters, it is necessary to predict and quantitatively evaluate slope hazards. While there has been much research focus in recent years on the evaluation of pipeline slope disasters and the stress calculation of pipelines under hazards, existing methods only provide information on the occurrence probability of slope events, not whether a slope disaster will lead to pipeline damage. Taking the 2015 Xinzhan landslide in Guizhou Province, China, as an example, this study used discrete elements to simulate landslide events and determine the risk level and scope for pipeline damage, and then established a pipe-soil coupling model to quantitatively evaluate the impact of landslide hazards for pipelines in medium- and high-risk areas. The results provide a reference for future pipeline disaster prevention and control. China's economic development is closely related to oil and gas resources, and the country is investing heavily in pipeline construction. Slope geological hazards seriously affect the long-term safe operation of buried pipelines, usually causing pipeline leakage, property and environmental losses, and adverse social impacts. To ensure the safety of pipelines and reduce the probability of pipeline disasters, it is necessary to predict and quantitatively evaluate slope hazards. While there has been much research focus in recent years on the evaluation of pipeline slope disasters and the stress calculation of pipelines under hazards, existing methods only provide information on the occurrence probability of slope events, not whether a slope disaster will lead to pipeline damage. Taking the 2015 Xinzhan landslide in Guizhou Province, China, as an example, this study used discrete elements to simulate landslide events and determine the risk level and scope for pipeline damage, and then established a pipe-soil coupling model to quantitatively evaluate the impact of landslide hazards for pipelines in medium- and high-risk areas. The results provide a reference for future pipeline disaster prevention and control.

Abstract:
keep_len="250">Wildfire occurrence is attributed to the interaction of multiple factors including weather, fuel, topography, and human activities. Among them, weather variables, particularly the temporal characteristics of weather variables in a given period, are paramount in predicting the probability of wildfire occurrence. However, rainfall has a large influence on the temporal characteristics of weather variables if they are derived from a fixed period, introducing additional uncertainties in wildfire probability modeling. To solve the problem, this study employed the weather variables in continuous nonprecipitation days as the "dynamic-step" weather variables with which to improve wildfire probability modeling. Multisource data on weather, fuel, topography, infrastructure, and derived variables were used to model wildfire probability based on two machine learning methods-random forest (RF) and extreme gradient boosting (XGBoost). The results indicate that the accuracy of the wildfire probability models was improved by adding dynamic-step weather variables into the models. The variable importance analysis also verified the top contribution of these dynamic-step weather variables, indicating the effectiveness of the consideration of dynamic-step weather variables in wildfire probability modeling. Wildfire occurrence is attributed to the interaction of multiple factors including weather, fuel, topography, and human activities. Among them, weather variables, particularly the temporal characteristics of weather variables in a given period, are paramount in predicting the probability of wildfire occurrence. However, rainfall has a large influence on the temporal characteristics of weather variables if they are derived from a fixed period, introducing additional uncertainties in wildfire probability modeling. To solve the problem, this study employed the weather variables in continuous nonprecipitation days as the "dynamic-step" weather variables with which to improve wildfire probability modeling. Multisource data on weather, fuel, topography, infrastructure, and derived variables were used to model wildfire probability based on two machine learning methods-random forest (RF) and extreme gradient boosting (XGBoost). The results indicate that the accuracy of the wildfire probability models was improved by adding dynamic-step weather variables into the models. The variable importance analysis also verified the top contribution of these dynamic-step weather variables, indicating the effectiveness of the consideration of dynamic-step weather variables in wildfire probability modeling.

Abstract:
keep_len="250">Preparation of accurate and up-to-date susceptibility maps at the regional scale is mandatory for disaster mitigation, site selection, and planning in areas prone to multiple natural hazards. In this study, we proposed a novel multi-hazard susceptibility assessment approach that combines expert-based and supervised machine learning methods for landslide, flood, and earthquake hazard assessments for a basin in Elazig Province, Türkiye. To produce the landslide susceptibility map, an ensemble machine learning algorithm, random forest, was chosen because of its known performance in similar studies. The modified analytical hierarchical process method was used to produce the flood susceptibility map by using factor scores that were defined specifically for the area in the study. The seismic hazard was assessed using ground motion parameters based on Arias intensity values. The univariate maps were synthesized with a Mamdani fuzzy inference system using membership functions designated by expert. The results show that the random forest provided an overall accuracy of 92.3% for landslide susceptibility mapping. Of the study area, 41.24% were found prone to multi-hazards (probability value > 50%), but the southern parts of the study area are more susceptible. The proposed model is suitable for multi-hazard susceptibility assessment at a regional scale although expert intervention may be required for optimizing the algorithms. Preparation of accurate and up-to-date susceptibility maps at the regional scale is mandatory for disaster mitigation, site selection, and planning in areas prone to multiple natural hazards. In this study, we proposed a novel multi-hazard susceptibility assessment approach that combines expert-based and supervised machine learning methods for landslide, flood, and earthquake hazard assessments for a basin in Elazig Province, Türkiye. To produce the landslide susceptibility map, an ensemble machine learning algorithm, random forest, was chosen because of its known performance in similar studies. The modified analytical hierarchical process method was used to produce the flood susceptibility map by using factor scores that were defined specifically for the area in the study. The seismic hazard was assessed using ground motion parameters based on Arias intensity values. The univariate maps were synthesized with a Mamdani fuzzy inference system using membership functions designated by expert. The results show that the random forest provided an overall accuracy of 92.3% for landslide susceptibility mapping. Of the study area, 41.24% were found prone to multi-hazards (probability value > 50%), but the southern parts of the study area are more susceptible. The proposed model is suitable for multi-hazard susceptibility assessment at a regional scale although expert intervention may be required for optimizing the algorithms.