Risk Map Research Articles

Analysis of Short-Term Heavy Rainfall-Based Urban Flood Disaster Risk Assessment Using Integrated Learning Approach

Accurate and timely risk assessment of short-term rainstorm-type flood disasters is very important for ecological environment protection and sustainable socio-economic development. Given the complexity and variability of different geographical environments and climate conditions, a single machine learning model may lead to overfitting issues in flood disaster assessment, limiting the generalization ability of such models. In order to overcome this challenge, this study proposed a short-term rainstorm flood disaster risk assessment framework under the integrated learning model, which is divided into two stages: The first stage uses microwave remote sensing images to extract flood coverage and establish disaster samples, and integrates multi-source heterogeneous data to build a flood disaster risk assessment index system. The second stage, under the constraints of Whale Optimization Algorithm (WOA), optimizes the integration of random forest (RF), support vector machine (SVM), and logistic regression (LR) base models, and then the WRSL-Short-Term Flood Risk Assessment Model is established. The experimental results show that the Area Under Curve (AUC) accuracy of the WRSL-Short-Term Flood Risk Assessment Model is 89.27%, which is 0.95%, 1.77%, 2.07%, 1.86%, and 0.47% higher than RF, SVM, LR, XGBoost, and average weight RF-SVM-LR, respectively. The accuracy evaluation metrics for accuracy, Recall, and F1 Score have improved by 5.84%, 21.50%, and 11.06%, respectively. In this paper, WRSL-Short-Term Flood Risk Assessment Model is used to carry out the risk assessment of flood and waterlogging disasters in Henan Province, and ArcGIS is used to complete the short-term rainstorm city flood and waterlogging risk map. The research results will provide a scientific assessment basis for short-term rainstorm city flood disaster risk assessment and provide technical support for regional flood control and risk management.

Assessing soil erosion risk through geospatial analysis and magnetic susceptibility: A study in the Oued Ghiss dam watershed, Central Rif, Morocco

In northern Morocco, water erosion presents a critical risk for soil degradation and dam sedimentation, attributable to extreme climatic conditions, variegated topography, lithological vulnerabilities, and anthropogenic pressures. This study quantitatively and spatially evaluates soil erosion susceptibility within the catchment area of the Oued Ghiss dam in Central Rif, Morocco. Utilizing the Revised Universal Soil Loss Equation (RUSLE) integrated with Geographic Information Systems (GIS) and calibrated through magnetic susceptibility metrics, the study provides a detailed analysis of erosion factors. The model was applied using GIS to spatially analyze data on rainfall, soil type, topography, land cover, and management practices, with magnetic susceptibility measurements used to calibrate the model by identifying stable and erosion-prone areas. The resultant soil loss map reveals a mean erosion rate of 24 t/ha/year. Specifically, 37.28 % of the catchment area exhibits an erosion rate under 5 t/ha/year, 40.16 % between 5 and 10 t/ha/year, 22.77 % between 10 and 20 t/ha/year, and 0.86 % surpasses 20 t/ha/year. Magnetic susceptibility measurements across various soil profiles indicated that stable soils showed increased susceptibility towards the surface, while eroded soils displayed decreased susceptibility, with significant correlations found between magnetic susceptibility variations and the final risk map. This innovative calibration of the RUSLE model through magnetic susceptibility improves erosion vulnerability assessments and results in a more detailed soil loss map. The findings provide critical information for policymakers to develop targeted management plans for erosion-prone areas, particularly benefiting the Ghiss dam watershed, where these insights will aid in preventing dam siltation and enhancing water resource management. This confirms magnetic susceptibility's reliability in assessing soil erosion vulnerability and improving numerical models.

Utilization of publicly available data to summarize spatio-temporal patterns of fish health events of Atlantic salmon (Salmo salar) reported by marine finfish industries in British Columbia (BC), Canada.

Atlantic salmon aquaculture companies in British Columbia (BC) must report fish health events to Fisheries and Oceans Canada (DFO) as part of their licensing conditions. Our study aimed to summarize these fish health events reported by Atlantic salmon sites in BC to identify spatial and spatio-temporal clusters. We conducted descriptive, retrospective global, and local cluster analyses using Moran's I and scan statistics. Between 2016 and 2022, 265 fish health events were reported. The annual incidence ranged from 5.60 (95% CI: 3.90-7.80) to 6.86 (95% CI: 4.70-9.60) health events per 100 active site-months. The most common events were yellow mouth (60.75%; 161/265) and salmonid rickettsial septicaemia (SRS) (15.47%; 41/265). The Moran's I index was positive and significant for yellow mouth, SRS, and overall fish health events at different distance bands. Most of the spatial and spatio-temporal clusters were identified in the west-central and southwestern parts of Vancouver Island. Our study hypothesizes that management practices, environmental conditions, and water quality parameters may have influenced the increased reporting of fish health events in these regions. Overall, the study highlights the potential of publicly available data for practical risk mapping in understanding the patterns of farmed Atlantic salmon diseases in BC.

Urban Flood Risk Assessment and Mapping Using GIS-DEMATEL Method: Case of the Serafa River Watershed, Poland

This paper develops a method integrating Geographic Information Systems (GIS) and the Decision-Making Trials and Evaluation Laboratory (DEMATEL) for the analysis of factors influencing urban flood risk and the identification of flood-prone areas. The method is based on nine selected factors: land use/land cover (LULC: the ratio of built-up areas, the ratio of greenery areas), elevation, slope, population density, distance from the river, soil, Topographic Wetness Index (TWI), and Normalized Difference Vegetation Index (NDVI). The DEMATEL method is used to determine the cause–effect relationship between selected factors, allowing for key criteria and their weights to be determined. LULC and population density were identified as the most important risk factors for urban floods. The method was applied to a case study—the Serafa River watershed (Poland), an urbanized catchment covering housing estates of cities of Kraków and Wieliczka frequently affected by flooding. GIS analysis based on publicly available data using QGIS with weights obtained from DEMATEL identified the vulnerable areas. 45% of the total catchment area was classified as areas with a very high or high level of flood risk. The results match the actual data on inundation incidents that occurred in recent years in this area. The study shows the potential and possibility of using the DEMATEL-GIS method to determine the significance of factors and to designate flood-prone areas.

National risk assessment of Italian school buildings: The MARS project experience

Strategic buildings, such as schools, play a crucial role in civil society. They are essential not only for the well-being of communities but also for their significance during the emergency and recovery phases following an earthquake. Reliable risk assessment is a powerful tool for implementing effective mitigation strategies. While considerable work has been already done in the literature to develop fragility curves and risk studies for residential buildings, there remains a noticeable gap in addressing risk assessment for school portfolios. As part of the 2019–2021 MARS (Seismic Risk MAps) project, funded by the Italian Civil Protection Department and involving the ReLUIS consortium along with the EUCENTRE foundation, a specific task was dedicated to evaluating seismic risk for the Italian school buildings. In this context, eight research units combined their expertise to develop a consensus-based vulnerability model, known as the “School-MARS model”. This model integrates fragility curves based on various approaches (i.e. empirical, mechanical-analytical, mechanical-numerical and hybrid) and is tailored to archetypes representative of the Italian school buildings. The main goal of the paper is outlining the methodology used to define the “School-MARS vulnerability model”. Moreover, the paper demonstrates the application of the latter for performing intensity-based and time-based risk analyses. Specifically, the results are presented in terms of damage distribution and usability ratings expected for the entire stock of Italian school building. Notably, these results indicate that approximately 26 % of Reinforced Concrete (RC) buildings and 31 % of Unreinforced Masonry (URM) structures may become unusable due to potential earthquakes expected in the next 50 years.

Risk and vulnerability analysis of road network in landslide prone areas in Munnar region, India

Catastrophes like landslides have the potential to impair critical transportation infrastructure, particularly road networks. The hilly regions in the state of Kerala in India are particularly prone to hazards and changing climate conditions. During the monsoon season, landslides are common in the Western Ghats, and the intensity of adverse impacts is more severe due to its densely populated regions. The study area of this research is in Idukki district of Kerala, where over 60 % of the land is prone to landslides. The monsoon rains bring with them a slew of disastrous landslides in the region. Most of the roadways in the study area are often disrupted due to landslides. Landslide risk assessment (LRA) is a crucial component of research on adverse impacts of landslide. The use of Geographical information System (GIS) and Multi Criteria Decision Analysis (MCDA) using Analytical Hierarchical Process (AHP) for susceptibility mapping and hazard risk assessment assists in the identification of disaster-prone areas. The data collected by field surveys were used to confirm the study results and the high-risk zones of the region were identified and the risk maps are prepared for the region as well as for the road network in the region. The risk of landslides may be described as the possibility of negative repercussions on road network thereby adversely impacting the inhabitants of the region. The landslide risk assessment of the road network in a hilly region is carried out which gives important insights for risk management and for future planning of resilient development in the region. This study enhances the knowledge for management of road network risk and vulnerabilities in intricate hilly region settings by developing a thorough vulnerability analysis framework. The research advances by giving transportation engineers a useful quantitative tool for identifying the risk and vulnerabilities of road network and directing design strategies and mitigation measures to lessen the possible negative effects of disruptive events like landslides on road infrastructure. The research findings could be an input for policy makers to plan for alternative resilient strategies for landslide risk management in road networks. The rational methodology adopted here could be replicated for carrying out risk and vulnerability assessment in other landslide prone areas.

A novel framework for multiple thermokarst hazards risk assessment and controlling environmental factors analysis on the Qinghai-Tibet Plateau

Due to the influence of climate warming, the degradation of permafrost on the Qinghai-Tibet Plateau (QTP) has become evident. The formation of thermokarst hazards induced by the degradation of ice-rich permafrost has a significant impact on infrastructure construction and local ecology; therefore, it is necessary to assess its risk. Currently, high-precision and harmonized assessment tools for multiple thermokarst hazards risk assessment are lacking, and the mechanisms governing the environmental interactions of thermokarst hazards have not been fully clarified. In this study, a novel multiple thermokarst hazards risk assessment framework was proposed by combining stacking machine learning and potential environmental factors to assess the risk of thermokarst hazards in the Yangtze River source region (YRSR). In addition, model performance was improved by model optimization. Finally, structural equation modeling (SEM) was used to assess the controlling environmental factors for the thermokarst hazards in the YRSR. The results show that slope and precipitation contribute the most to the modeling accuracy of thermokarst lakes and thaw slumps, respectively. Model optimization improved the base model modeling accuracy by approximately 2 % ∼ 7 %, with XGBoost having the highest sensitivity to model optimization and the highest modeling accuracy. In terms of the ensemble strategy, the stacking model and ensemble model significantly improved the risk mapping accuracy, and the stacking model was better than the ensemble model, with accuracies of 92.39 % and 93.36 % for thermokarst lakes and thaw slumps, respectively. Compared with previous results, the results of this study are more representative of the YRSR. Finally, via SEM, terrain factors and soil factors were identified as controlling environmental factors for the risk of thaw slumps and thermokarst lakes, respectively. This study proposes a high-precision risk assessment method for thermokarst hazards in permafrost regions, and contributes to a deeper understanding of the interaction mechanisms between thermokarst hazards and environmental factors.

Development and application of a WebGIS-based prediction system for multi-criteria decision analysis of porcine pasteurellosis

Porcine pasteurellosis is an infectious disease caused by Pasteurella multocida (P. multocida), which seriously endangers the healthy development of pig breeding industry. Early detection of disease transmission in animals is a crucial early warning for humans. Therefore, predicting risk areas for disease is essential for public health authorities to adopt preventive measures and control strategies against diseases. In this study, we developed a predictive model based on multi-criteria decision analysis (MCDA) and assessed risk areas for porcine pasteurellosis in the Chinese mainland. By using principal component analysis, the weights of seven spatial risk factors were determined. Fuzzy membership function was used to standardize all risk factors, and weight linear combination was used to create a risk map. The sensitivity of the risk map was analyzed by calculating the mean of absolute change rates of risk factors, as well as calculating an uncertainty map. The results showed that risk areas for porcine pasteurellosis were predicted to be locate in the south-central of the Chinese mainland, including Sichuan, Chongqing, Guangdong, and Guangxi. The maximum standard deviation of the uncertain map was less than 0.01and the ROC results showed that the prediction model has moderate predictive performance with the area under the curve (AUC) value of 0.80 (95% CI 0.75–0.84). Based on the above process, MCDA was combined with WebGIS technology to construct a system for predicting risk areas of porcine pasteurellosis. Risk factor data was directly linked to the developed model, providing decision support for disease prevention and control through monthly updates.

Assessing the impact of rising ambient temperatures on urban mortality rates using satellite mapping

Introduction. In the context of the change in global climate, the gain in the mortality rate due to an elevation of the temperature in the urban environment with the appearance of “heat waves” becomes an urgent problem. Materials and methods. To analyze the spatial distribution of risks and damages from premature mortality, there were selected cities located in several climatic zones of Russia including Omsk, Astrakhan, Rostov-on-Don, Lipetsk, Arkhangelsk. For satellite mapping of risks and damages from premature deaths caused by an elevation of the temperature in urban spaces, materials from summer surveys by Landsat series satellites were used. Results. The analysis of all satellite maps allows concluding the risks of an increase in urban temperature, potential deaths, and economic damage within the territories of the studied cities to be unevenly distributed. Advanced values of potential mortality are localized in places where high population density coincides with an enhanced risk of the temperature rise. The highest damage values were noted in Rostov-on-Don and Astrakhan, the lowest – in Arkhangelsk. Limitations. Mortality analysis was carried out without taking into account the age and sex structure of the population. Conclusion. The results of applying the methodology for satellite mapping of risks and economic losses from premature mortality due to rising urban temperatures showed accuracy acceptable for making management decisions to protect the health of the urban population. The results of satellite mapping of these events can be used in the development of preventive measures aimed at reducing premature mortality.

Comprehensive Task Optimization Architecture for Urban UAV-Based Intelligent Transportation System

This paper tackles the problem of resource sharing and dynamic task assignment in a task scheduling architecture designed to enable a persistent, safe, and energy-efficient Intelligent Transportation System (ITS) based on multi-rotor Unmanned Aerial Vehicles (UAVs). The addressed task allocation problem consists of heterogenous pick-up and delivery tasks with time deadline constraints to be allocated to a heterogenous fleet of UAVs in an urban operational area. The proposed architecture is distributed among the UAVs and inspired by market-based allocation algorithms. By exploiting a multi-auctioneer behavior for allocating both delivery tasks and re-charge tasks, the fleet of UAVs is able to (i) self-balance the utilization of each drone, (ii) assign dynamic tasks with high priority within each round of the allocation process, (iii) minimize the estimated energy consumption related to the completion of the task set, and (iv) minimize the impact of re-charge tasks on the delivery process. A risk-aware path planner sampling a 2D risk map of the operational area is included in the allocation architecture to demonstrate the feasibility of deployment in urban environments. Thanks to the message exchange redundancy, the proposed multi-auctioneer architecture features improved robustness with respect to lossy communication scenarios. Simulation results based on Monte Carlo campaigns corroborate the validity of the approach.

Understanding Socioeconomic Risk and Vulnerability to Climate Change–Induced Disasters: The Case of Informal Settlements in KwaZulu-Natal, South Africa

This study employs a qualitative research approach to spatially delineate the risk and vulnerability of informal settlements in KwaZulu-Natal, South Africa. This comes after devastating floods that occurred in KwaZulu-Natal, South Africa, in April 2022. This disaster emphasised the necessity for South Africa to proactively address natural hazards by implementing risk reduction strategies rather than attempting to mitigate the impact after the damage has occurred. Evidence indicates that the risk and vulnerability associated with climate-induced disasters, such as flooding, are exacerbated by socioeconomic factors, including housing shortages, which compel individuals to construct dwellings in flood-prone areas. Opportunities exist to enhance the understanding of disaster risk reduction in Africa, and South Africa in particular, through science-informed initiatives employing spatial techniques, including geographic information systems, hazard exposure mapping, and socioeconomic risk and vulnerability mapping. These techniques are crucial for humanitarian planning focused on long-term risk reduction, early recovery, shelter, reconstruction, and psychosocial support for climate change disaster recovery. Furthermore, an understanding of the nature and location of areas with high disaster risk, such as flooding, is essential for the formulation of disaster risk reduction strategies, including early warning systems.

Performance of a Five-Layer ANN Model for Earthquake Magnitude Prediction and Spatial Risk Mapping in Turkey

Turkey faces significant seismic risks, necessitating accurate earthquake forecasting for effective disaster preparedness. This study employs advanced Artificial Neural Networks (ANN) to predict earthquake magnitudes and assess risks specific to Turkey. Focusing on a distinct segment of the USGS earthquake catalogue from January 2014 to August 2023, the research tailors ANN algorithms with five layers to Turkey's seismic challenges. Rigorous dataset cleaning and processing ensure accuracy, with the ANN model demonstrating exceptional alignment with earthquake data ( RMSE: 0.078, R2: 0.89). Comparative evaluations highlight the effectiveness of ANN models in forecasting earthquake magnitudes in Turkey. The study explores the spatial distribution of earthquake risk across Turkey through an ANN-based map, emphasizing the critical window for preventive measures in this seismically active region. The analysis ensures further enhancement of model accuracy in seismic-prone areas globally. This study advances earthquake prediction by showcasing the high accuracy of our five-layer ANN model in forecasting magnitudes and spatial risk, significantly improving disaster preparedness and risk management in regions such as Turkey.

A novel ANN-CA and MCDA integrated framework for predicting urban expansion and its implications on future flood risk, Accra Metropolis

Urban development in African countries significantly impacts environmental sustainability and city resilience, particularly in flood risk management. The Accra Metropolis, in particular, faces increasingly prevalent annual floods that disproportionately affect the urban poor living in informal settlements, resulting in significant loss of life and disruption of livelihoods. This study developed a GIS-based flood risk mapping framework that integrates artificial neural network (ANN) and cellular automata (CA) modelling with multi-criteria decision analysis. This hybrid approach was employed to predict flood scenarios for the Metropolis in 2032 and 2042. The framework incorporated six hydro-geomorphological indicators influencing extreme events: LULC, slope, elevation, drainage density, soil type, and proximity to rivers. The study analysed LULC projections, revealing a trend of substantial urban expansion with an estimated 10.9% increase in built-up areas within the next 20 years. This growth is expected to significantly heighten future flood risk in both the central upstream and downstream portions of the Metropolis, particularly in low-lying informal settlements close to the Odaw River and Korle Lagoon. Model performance was validated by ROC curve analysis, with AUC values of 0.927 and 0.922 for 2032 and 2042, respectively, which resonates with the records of previous flood distribution studies of the area. This study highlights the crucial need for sustainable urban development, improved infrastructure, and proactive flood mitigation measures in Accra to assist urban planners, policymakers, and stakeholders in managing flood risks effectively.

Unravelling flood risk in the Rel River watershed, Gujarat using coupled earth observations, multi criteria decision making and Google Earth Engine

Socioeconomic developments, ineffective drainage systems, and insufficient river control, all contribute to significant loss of property and life due to the constant threat of floods. Therefore, controlling flood threats across Rel River, Dhanera, Gujarat has become even more crucial due to floods causing strain throughout the area during monsoon season. The different 52 micro-watersheds were formed across the study region using earth observations for the estimation of flood hazards, vulnerability, and risk. The AHP-MCDM was employed to assign priority rank, weightage, and risk category for each micro watershed. The flood hazard zone was mapped and its vulnerability was characterized in different categories varying from very low to very high. The normalized weights of each factor i.e, hazard indicator (soil, elevation, slope, flow accumulation, rainfall) and vulnerability indicator (LULC, distance from the hospital, population density map) were estimated employing the AHP-MCDM technique whereas LULC along with most of other factors were derived from GEE. The integration of vulnerability and hazard indicators, provides insights into understanding the flood sensitivity, facilitating the preparation of the flood risk map. 20 micro-watersheds were susceptible to high to very high risk and covered an area of 213.15 km2 whereas 32 micro-watersheds were in the range of very low to moderate category which covers the area of 228.41 km2. Therefore, the integration of GEE and spectral indices for obtaining various hazard & vulnerability indicators, which were prioritized and ranked using AHP is a unique methodology, facilitating a robust evaluation of flood risk mapping.

Integrating drones into disaster town watching: Participatory action data collection of local knowledge in Tuguegarao City, Philippines

Disaster town watching comprises a participatory technique by a community to observe, identify, understand, and build resilience to natural or human-induced hazards. Participants walk through their communities, taking notes and imagery for subsequent participatory risk mapping exercises. The goal is to develop a more nuanced and contextualized understanding of the community's state of disaster risk management. This article explores the integration of drones in the acquisition of imagery for disaster town watching. It followed standard practices for conducting disaster town watching research in a community in the Philippines. However, in addition to standard walking teams, one team learned to pilot drone missions, take aerial images, and then interpret them. The imagery they produced elicited more spatially significant relationships between and across features and locations in the community. The types of local knowledge identified differed significantly from that taken from images produced with cellular phones or Google Earth. This study revealed that drone imagery can provide new insights into local knowledge on hazards, vulnerability, and resources and the enhancement of social, economic, and environmental resilience it engenders. Participants build greater confidence in their ability to determine self-help solutions and mutual help countermeasures for enhancing disaster preparedness and community resilience against disaster.

Potential Establishment, Prevalence of Dengue Vector, Aedes sp. and its Risk Map in Hurghada Region, Red Sea, Egypt

Potential Establishment, Prevalence of Dengue Vector, Aedes sp. and its Risk Map in Hurghada Region, Red Sea, Egypt

Risk Identification, Prioritization, and Mapping for Early Construction Cost Contingency in Transportation Infrastructure Projects

Risk Identification, Prioritization, and Mapping for Early Construction Cost Contingency in Transportation Infrastructure Projects

Flood risk decomposed: Optimized machine learning hazard mapping and multi-criteria vulnerability analysis in the city of Zaio, Morocco

Urban flood risk mapping has become crucial for effective mitigation and urban planning. This study assesses and maps flood risk in the city of Zaio, Morocco, using machine learning and Multi-Criteria Decision Analysis (MCDA) techniques to overcome data scarcity challenges. We employed the Random Forest (RF) model with nine flood conditioning factors for flood hazard and the Analytical Hierarchy Process (AHP) for vulnerability assessment. To enhance RF model performance, we compared three hyperparameter tuning techniques: Bayesian Optimization (RF-BO), Genetic Algorithm (RF-GA), and Grid Search (RF-GS). RF-BO demonstrated superior accuracy in flood hazard modelling. Flood vulnerability was assessed using AHP, incorporating social and physical factors. The final flood risk map was produced by combining the RF-BO hazard model with the AHP vulnerability assessment. Results indicate that flood hazard in Zaio is significantly affected by factors such as topography and distance to rivers. Moreover, areas with high population density closer to rivers, especially in the south-western residential area, were found to be more exposed to flood risk. The findings highlight the utility of ML models, MCDA, and hyperparameter optimization in urban flood risk mapping, enabling the identification of high-risk urban areas that should be prioritized for flood protection efforts. This approach proves especially valuable in ungauged regions with limited hydrological data.

Landslide risk assessment combining kernel extreme learning machine and information value modeling-A case study of Jiaxian Country of loess plateau, China

Landslide risk mapping can be an effective reference for disaster mitigation and land use planning, but the modelling process involves multidisciplinary knowledge which leads to its complexity. In this study, Jiaxian County in Shaanxi Province on the Loess Plateau of China, served as the study area, primarily characterized by Quaternary loess-covered geomorphology, with an average rainfall of about 400 mm annually. Soil erosion and human engineering activities have contributed to significant slope failures, posing threats to local residents and infrastructure. A reasonable inventory of landslides in the region was established by field survey combined with aerial imagery, allowing for characterization of their development and spatial distribution. Nine thematic maps related to landslide occurring and three vulnerability maps were prepared as influencing factors for landslide risk assessment. Subsequently, landslide susceptibility and hazard were evaluated using a kernel extreme learning machine (KELM) and information value (IV) model, followed by map validation. A decision table was then employed to generate the landslide risk map. The results of landslide hazard mapping showed that the historical landslide events were mainly developed in the central part of the study area, particularly concentrated near the developed river network. Integration of overall risk elements suggested that landslide risks in the study area were generally at a low level. Besides, a total of 0.25 % and 2.05 % of the areas were classified as having very high and high landslide risk levels, respectively, where 65.11 % of inventory landslides occurred. Therefore, the proposed procedure is a valuable tool for assessing landslide risk in Jiaxian Country.

Prevalence, spatial distribution and risk mapping of Dirofilaria immitis in wild canids in southern Québec, Canada

Domestic dogs (Canis familiaris) and wild canids, including coyotes (Canis latrans) and red foxes (Vulpes vulpes), serve as definitive hosts for Dirofilaria immitis, a parasitic nematode causing the heartworm disease. Understanding infection risks in wildlife reservoirs in relation to environmental factors is crucial for assessing exposure risk in domestic dogs. The regional prevalence of D. immitis infection was estimated in trapped wild coyotes and red foxes across Québec, Canada. Spatial clusters of infection were detected using Kulldorff's spatial scan statistics. A series of logistic regression models predicting the D. immitis status in coyotes were built from heartworm development unit (HDU) estimates and cumulative precipitation variables over various time periods. Between October 2020 and March 2021, 421 coyotes and 284 red foxes were examined for the presence of D. immitis. The parasite was found in 43 coyotes and 1 red fox. A high-risk infection cluster was detected in coyotes in southwestern Québec. The best model included as sole predictor the average cumulative HDU contributing to risk of D. immitis in the three years preceding coyote capture. This model significantly predicted infection status with an area under the curve of 76.1%. The cumulative precipitation had no notable effect in any model. This study highlights a high prevalence of D. immitis in coyotes in Québec with regional differences correlated to temperature-derived predictors. The spatial risk of infection in this population likely represents the environmental risk of exposure to the parasite given that coyotes do not receive preventive treatment compared to domestic dogs. Our findings are important for veterinarians in the application of prevention strategies for heartworm disease in domestic dogs.