Hazard Maps Research Articles

Mapping Geospatial AI Flood Risk in National Road Networks

Previous studies have utilized machine learning algorithms that incorporate topographic and geological characteristics to model flood susceptibility, resulting in comprehensive flood maps. This study introduces an innovative integration of geospatial artificial intelligence for hazard mapping to assess flood risks on road networks within Portuguese municipalities. Additionally, it incorporates OpenStreetMap’s road network data to study vulnerability, offering a descriptive statistical interpretation. Through spatial overlay techniques, road segments are evaluated for flood risk based on their proximity to identified hazard zones. This method facilitates the detailed mapping of flood-impacted road networks, providing essential insights for infrastructure planning, emergency preparedness, and mitigation strategies. The study emphasizes the importance of integrating geospatial analysis tools with open data to enhance the resilience of critical infrastructure against natural hazards. The resulting maps are instrumental for understanding the impact of floods on transportation infrastructures and aiding informed decision-making for policymakers, the insurance industry, and road infrastructure asset managers.

1D-Flood Hazard Mapping and Analysis Using HEC-RAS Modelling, A case study of the Seti River, West Central Nepal

1D-Flood Hazard Mapping and Analysis Using HEC-RAS Modelling, A case study of the Seti River, West Central Nepal

Optimized ensemble-based flood hazard mapping in low altitude subtropical riverine terrane

Among various natural hazards, those induced by hydrological and meteorological factors have emerged as the most frequent and damaging. India is at the second from the top, after Bangladesh, of flood hazard-affected countries of the world, causing immense damage to life and property every year. The Middle Ganga Plain in the country is one of the most severely affected regions due to the convergence of Himalayan rivers in the Ganga (Ganges) plain. This study focuses on optimizing novel ensemble models to enhance the mapping of flood hazard susceptibility in specific topoclimatic setting, characterized by a low-altitude-range, sub-tropical monsoonal climate, and a riverine floodplain environment of the tectonically active Middle Ganga Plain. This region forms part of the Ganga Foreland Basin prone to frequent floods. We employ a comprehensive flood inventory and twelve selected flood conditioning factors and is the first study that has developed these ensemble models in low relief riverine floodplain environmental setting with high resolution geomorphology as one of the conditioning factors. Four artificial intelligence ensemble models were employed in this work: Logistic Regression-Evidential Belief Function (LR-EBF), Logistic Regression-Frequency Ratio (LR-FR), Multi-Layer Perceptron-Evidential Belief Function (MLP-EBF), and Multi-Layer Perceptron-Frequency Ratio (MLP-FR). Results showed that LR-based ensembles outperformed MLP-based ensembles in the studied topoclimatic setting, with LR-EBF model achieving 87.2% and 84.7% success and prediction rates respectively. Our results reveal consistent performance differences between LR-based ensembles with FR and EBF models across different environmental settings. MLP-based ensembles, particularly with Frequency Ratio, displayed more significant performance variations. The findings underscore the importance of considering specific topoclimatic and altitudinal range environments when applying flood hazard susceptibility models. Our study also provides valuable insights for planning and policymaking in flood management practices in similar topoclimatic settings worldwide.

Hazardous Waste Litigation and Mapping: A template for designing maps of climate change attribution

Hazardous Waste Litigation and Mapping: A template for designing maps of climate change attribution

Flood Modelling of Madi River Using HEC-RAS by Rain on Grid Approach

Indispensable for prognosticating flood impact in areas with limited data availability, reliable flood models are crucial for the analysis and mitigation of flood hazard. This study provides insight to accurately analyse flood scenarios by preparing a well calibrated 2D hydraulic model in HEC-RAS along with the Rain on Grid approach, which is subsequently used for hazard map preparation of Madi River. Hazard classification of flood depth indicated that for a 20-year return period, Moderate hazard levels covered 16.499%, High hazard levels covered 14.831%, and Very High hazard levels covered 68.670% of the total inundated area with reference to the depth hazard categories. Since, significant portion of the inundated area is classified as Very High Hazard; the finding of this research emphasizes the need of effective mitigation measure and provides essential insights crucial for flood risk assessment.

Multi-hazard assessment for flood and Landslide risk in Kalimantan and Sumatra: Implications for Nusantara, Indonesia's new capital

Situated within the Ring of Fire and characterized by a tropical climate and high seismic activity, Indonesia is uniquely vulnerable to natural disasters such as floods and landslides. These events pose significant threats to both the population and infrastructure. This study predicts areas exposed to flood and landslide risk by considering various environmental factors related to climate, topography, and land use. The predictive performance of three machine learning models—naïve Bayes, k-nearest neighbors, and random forest (RF)—was evaluated by comparing the AUC, RMSE, and R2 values of each model. Ultimately, the RF model, which demonstrated the highest accuracy, was used to prioritize disaster impact factors and generate hazard maps. The results identified the interaction of rainfall, land use, and slope aspect as the most critical determinants of hazard occurrence. The predicted hazard maps revealed that approximately 26.7 % of the study area was vulnerable to either floods or landslides, with 16.8 % of the area experiencing both. The new capital of Nusantara showed a relatively higher multi-hazard risk than did the overall study area and protected zones, with 22.1 % of the hazard area vulnerable to both flooding and landslides. In single hazard zones, areas classified as at risk for floods had a higher mean probability of experiencing both hazards (43 %), as compared to areas classified as at risk for landslides (22 %). As a result, urban planners and relevant stakeholders can now utilize the hazard maps developed in this study to prioritize infrastructure reinforcement and disaster risk areas, integrating land use planning with risk assessment to mitigate the impact of disasters. By employing these strategies, Indonesia and other countries facing similar challenges can now enhance their disaster preparedness and response capabilities in new capital regions and other areas, ultimately planning for more sustainable urban development.

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.

A data-driven framework for enhancing coastal flood resilience in resource-crunched developing nations

A comprehensive Flood Resilient Scenario Model ‘FReSMo’ employs a data-driven, evidence-based approach for assessing climate-induced flood risk and validating the efficacy of mangroves (as context-specific adaptation measure) in reducing residential building damage. Based on an improvised Source-Pathway-Receptor-Consequence-Evidence concept, FReSMo employs a three-step analysis. First, hazard mapping estimates coastal flood extents for various return period under different Shared Socioeconomic Pathways. Second, the model maps the exposure of residential buildings to these flood extents by projecting built-up area for 2050 using the FUTURES model, based on physiographic, socio-demographic, and economic parameters. Finally, a data-driven probabilistic damage model is applied to estimate the built-up area damage for a 100-year coastal flood event (SSP-2.6). The pre-and post-adaptation damage estimate demonstrates the efficacity of NBS (mangroves) in reducing coastal flood risk. A 100 m mangrove patch on the Sagar coastline reduced the building damage cost by 70% for a 48-hr and 75% for a 24-hr flood. Considering a plantation cost for 6.2 km2, the total benefit, despite persistent losses, was a 222% return on investment. FReSMo transcends conventional risk assessment frameworks by offering a comprehensive approach for evaluating the cost-effectiveness of adaptation investments in developing countries, making it an invaluable tool in the face of climate change.

SITE-SPECIFIC DISAGGREGATION ANALYSIS OF RECENT EARTHQUAKE ON THE AMBON COAST

Seismic activity in the eastern region of Indonesia, particularly in Ambon, has been a matter of great concern because of its high tectonic activity. The devastating magnitude 6.5 earthquake in September 2019, which resulted in fatalities and significant damage, highlighted the region’s vulnerability and emphasized the importance of understanding the area’s seismic hazards. Although previous studies have been conducted, this study aims to fill critical gaps in the literature by using the latest seismic data until 2022 and updated attenuation equations. Disaggregation analysis is employed to identify ground motion characteristics, that are crucial for calculating synthetic ground motion acceleration. Under risk-targeted maximum considered earthquake (MCER) conditions, the study results show a peak ground acceleration of 0.444 g, with spectral acceleration at 0.2 s reaching 1.093 g, which decreases to 0.350 g at 1.0 s. Notably, background earthquake sources play a dominant role in contributing to the region’s high seismic hazard potential. The results of this study highlight the urgency of updating Indonesia’s national seismic hazard map. The study also recommends future studies to explore additional parameters of newly discovered faults to enhance understanding of fault movements and their impacts in the study area.

InSAR-Driven Dynamic Landslide Hazard Mapping in Highly Vegetated Area

Landslide hazard mapping is important to urban construction and landslide risk management. Dynamic landslide hazard mapping considers landslide deformation with changes in the environment. It can show more details of the landslide process state. Landslides in highly vegetated areas are difficult to observe directly, which makes landslide hazard mapping much more challenging. The application of multi-InSAR opens new ideas for dynamic landslide hazard mapping. Specifically, landslide susceptibility mapping reflects the spatial probability of landslides. For rainfall-induced landslides, the scale exceedance probability reflects the temporal probability. Based on the coupling of them, dynamic landslide hazard mapping further considers the landslide deformation intensity at different times. Zigui, a highly vegetation-covered area, was taken as the study area. The landslide displacement monitoring effect of different band SAR datasets (ALOS-2, Sentinel-1A) and different interpretation methods (D-InSAR, PS-InSAR, SBAS-InSAR) were studied to explore a combined application method. The deformation interpreted by SBAS-InSAR was taken as the main part, PS-InSAR data were used in towns and villages, and D-InSAR was used for the rest. Based on the preliminary evaluation and the displacement interpreted by fusion InSAR, the dynamic landslide hazard mappings of the study area from 2019 to 2021 were finished. Compared with the preliminary evaluation, the dynamic mapping approach was more focused and accurate in predicting the deformation of landslides. The false positives in very-high-hazard zones were reduced by 97.8%, 60.4%, and 89.3%. Dynamic landslide hazard mapping can summarize the development of and change in landslides very well, especially in highly vegetated areas. Additionally, it can provide trend prediction for landslide early warning and provide a reference for landslide risk management.

Analysis and Geovisualization of Tsunami Hazard and Evacuation Routes in the Opak-Progo Coastal Area

The southern coastal area of Java has a high risk of tsunamis. Additionally, the presence of rivers flowing in the south of Java Island poses a greater tsunami threat because these rivers can act as "toll roads" for tsunami waves to enter the land. Therefore, this study was conducted in the coastal area of the Opak-Progo watershed to determine the level of tsunami hazards and plan effective evacuation routes. The hazard map was created using the Berryman method, which employs parameters such as slope, surface roughness coefficient, coastline, and tsunami run-up height scenario; a 3-metre scenario was used in this study. Network analysts also determined evacuation routes using the nearest facility method. Network analysis was used to identify an optimised route with four evacuation sites. This research has the potential to significantly contribute to tsunami mitigation and evacuation planning in the coastal areas of the Opak-Progo watershed.

Urban flash flood hazard mapping using machine learning, Bahir Dar, Ethiopia

ABSTRACT Increased frequency and magnitude of flooding pose a significant natural hazard to urban areas worldwide. Mapping flood hazard areas are crucial for mitigating potential damage to human life and property. However, conventional hydrodynamic approaches are hindered by their extensive data requirements and computational expenses. As an alternative solution, this paper explores the use of machine learning (ML) techniques to map flood hazards based on readily available geo-environmental variables. We employed various ML classifiers, including decision tree (DT), random forest (RF), XGBoost (XGB), and k-nearest neighbor (kNN), to assess their performance in flood hazard mapping. Model evaluation was conducted using the area under the receiver operating characteristic curve (AUC) and root mean square error (RMSE). Our results demonstrated promising outcomes, with AUC values of 93% (DT), 97% (RF), 98% (XGB), and 91% (kNN) for the validation dataset. RF and XGB have slightly higher performance than DT and kNN and distance to river was the most important factor. The study highlights the potential of ML for urban flood modeling, offering reasonable accuracy and supporting early warning systems. By leveraging available geo-environmental variables, ML techniques provide valuable insights into flood hazard mapping, aiding in effective urban planning and disaster management strategies.

Probabilistic Cascade Modeling for Enhanced Flood and Landslide Hazard Assessment: Integrating Multi-Model Approaches in the La Liboriana River Basin

Extreme rainfall events in Andean basins frequently trigger landslides, obstructing river channels and causing flash flows, loss of lives, and economic damage. This study focused on improving the modeling of these events to enhance risk management, specifically in the La Liboriana basin in Salgar (Colombia). A cascading modeling methodology was developed, integrating the spatially distributed rainfall intensities, hazard zoning with the SLIDE model, propagation modeling with RAMMS using calibrated soil rheological parameters, the distributed hydrological model TETIS, and flood mapping with IBER. Return periods of 2.33, 5, 10, 25, 50, and 100 years were defined and applied throughout the methodology. A specific extreme event (18 May 2015) was modeled for calibration and comparison. The spatial rainfall intensities indicated maximum concentrations in the northwestern upper basin and southeastern lower basin. Six landslide hazard maps were generated, predicting landslide-prone areas with a slightly above random prediction rate for the 2015 event. The RAMMS debris flow modeling involved 30 simulations, indicating significant deposition within the river channel and modifying the terrain. Hydraulic modeling with the IBER model revealed water heights ranging from 0.23 to 7 m and velocities from 0.34 m/s to 6.98 m/s, with urban areas showing higher values, indicating increased erosion and infrastructure damage potential.

Integration of Building Age into Flood Hazard Mapping: A Case Study of Al Ain City, United Arab Emirates

Accurate and timely information on building age is essential for mitigating the impacts of natural disasters such as earthquakes and floods. Traditional methods for collecting these data are often inefficient and costly. This study leverages remote sensing and machine learning to classify building age and integrate this information into a comprehensive flood hazard map for Al Ain City. By combining building age with elevation, topography, land use, population density, and other factors, we generated a flood hazard map that effectively identifies high-risk areas. Our results show that a significant portion of the city is has low potential for flooding (61%), while others face moderate (32%), high, or very high flood hazards. This study provides crucial information for informed decision-making regarding future urban planning, disaster management, and development strategies in Al Ain City. By identifying vulnerable buildings in advance, it supports efforts to reduce disaster risks, protect lives, and optimize resource allocation.

Indian landslide tragedy demands a rethink of hazard mapping in a changing climate.

Indian landslide tragedy demands a rethink of hazard mapping in a changing climate.

Design response spectrum based on shock-waveform decomposition method

This study applies the shock-waveform (SW) decomposition method, originally developed for mechanical shock analysis, to earthquake ground motions. It reveals a general shape similarity between the envelope of the Pseudo-Spectral Accelerations (PSAs) of SW decomposed components and the PSA of the corresponding ground motion. Based on this similarity, a novel method to determine the characteristic period Tg, the long-period transition period TD, the shape correction period Tβ, and the Design Response Spectrum (DRS) is proposed and evaluated. New methods to determine Tg, TD and Tβ from the signal decomposition perspective are integrated into the normalized DRS in Eurocode 8–2022, enabling the construction of the normalized DRS based on SW method (SWDRS). Furthermore, simple ground motion attenuation regression equations are derived to relate the parameters (Tg, TD, Tβ) and the corresponding spectral ordinates of the normalized SWDRS model with seismic magnitude and site conditions. The SWDRS model is validated by randomly selecting two sites in United States. For each site, the SWDRS is determined by using the attenuation regression equations and the DRS spectral plateau value from the official seismic hazard map. Comparisons between the SWDRS, the latest local DRS, and the severest historical PSA recorded at the specific site demonstrate that the SWDRS provides more accurate spectral values over intermediate- and long-period ranges for structural seismic design.

A Preliminary Hazard Assessment of Kolumbo Volcano (Santorini, Greece)

Volcanic eruptions stand as destructive threats to adjacent communities, unleashing multiple hazards such as earthquakes, tsunamis, pyroclastic flows, and toxic gases. The imperative for proactive management of volcanic risks and communities’ adaptation cannot be overstated, particularly in densely populated areas where the potential for widespread devastation looms large. Kolumbo, an active submarine volcano located approximately 7 km northeast of Santorini Island in Greece, serves as a pertinent case. Its historical record is characterised by an eruption in 1650 CE that produced a catastrophic tsunami. The aftermath witnessed havoc on neighbouring islands, coupled with casualties stemming from noxious gases in Santorini. Eyewitness accounts mention maximum water run-up heights of 20 m on the southern coast of Ios, inundation of an area of 240 m inland on Sikinos, and a flooding of up to 2 km2 inland on the eastern coast of Santorini. Recent studies suggest that a potential future eruption of Kolumbo poses a substantial hazard to the northern and eastern coasts of Santorini. Unfortunately, the absence of a concrete management protocol leaves these areas vulnerable to an impending threat that demands immediate attention. Therefore, it is recommended that a comprehensive approach be adopted, involving scientific research (active monitoring, hazard maps), community engagement, preparedness planning with government agencies, and the development of timely response strategies to reduce the associated risks, prevent casualties, and mitigate the potential consequences on the region’s economy and infrastructure.

Mapping pedestrian network level outdoor heat hazard distributions in Philadelphia

With the rise of global temperature, many cities are suffering from more and more frequent extreme heat in hot summers. Quantitative information on the spatial distributions of urban heat has become more and more important for extreme heat mitigation and adaptation in cities. This study first investigated the fine-level heat hazard distributions at the sidewalk and building block level from the pedestrian perspective in Philadelphia, Pennsylvania. The urban microclimate modeling based on a high-resolution urban geometrical model was used to generate the 1m resolution outdoor heat hazard map in the study area. The sidewalk map was overlaid on the generated high-resolution heat hazard map to estimate the sidewalk level heat hazard. Based on the sidewalk level heat hazard map, this study further calculated the heat hazard level in the 400m walkshed along sidewalks for each building block. The building level hazard data were then aggregated at the census tract level to compare with the socioeconomic and racial/ethnic variables. The result shows that neighborhoods with higher proportion of African Americans have a higher heat hazard level in Philadelphia. This study would provide new insights for developing more thermally comfortable and pedestrian-friendly neighborhoods in the context of climate change.

Optimization of a coal mine roof characterization model using machine learning

Predicting areas of increased propensity for roof deformation is crucial for the proactive management of geotechnical risk in underground coal mines. Current practices rely largely on assessing rock mass strength or characterization indices in isolation. Validation of applied ground support systems typically, and in part, comprises a review of observed deformation and analysis of extensometer data from adjacent previously mined areas. This information is seldom routinely assessed relative to the rock mass strength or characterization data. Typically, roof deformation is a response to several combining factors such as mining-induced stress, roof lithology, rock mass strength, and roadway geometry. Therefore, it is optimal to develop an approach to integrate these factors to quantify their relative significance to causing roof deformation, with the goal of establishing a reliable quantitative model for predicting roof deformation.This paper provides a case study in machine learning using Artificial Neural Network (ANN) and geophysical log data information from an underground coal mine located in the Bowen Basin (Australia), whereby a suite of parameters is considered pertinent to excavation stability. This machine learning predictive model analyses key geotechnical parameters including field and mining-induced stress, rock mass strength, clay/shale content, and strata monitoring data to identify strata hazards. Finally, the classification model can indicate the risk of roof displacement and observed roof deformation as communicated through a contoured ‘geotechnical hazard map’. This hazard map can assist site engineers understand roof conditions in underground coal mining and thus put in place operational mitigation processes to reduce geotechnical risk on mining. This could potentially improve the identification of high-risk areas and inform future bolting requirements to maintain a safe working environment and minimise production interruptions.