Pub Date : 2025-09-01DOI: 10.1016/j.nhres.2025.01.009
Imran Khan, Md Raihanul Islam
The Sylhet division, located in the northeastern part of Bangladesh, experienced severe flooding in June 2024, forcing thousands to seek shelter. The flooding occurred in two phases: the first following Cyclone Remal, which made landfall in Bangladesh on May 26, 2024, and the second by mid-June. This study aimed to estimate the extent of inundation, the population and buildings affected, and the damage to crops caused by the flood. It utilized Sentinel-1 (A & B) microwave and Sentinel-2 (A & B) optical data, population data from WorldPoP and Bangladesh Bureau of Statistics (2023a) and building data from Open Buildings. Image processing and GIS techniques were applied to extract and analyze information obtained from the satellite data. The study reveals that approximately 66% of the Sylhet division was inundated as of June 19, 2024. The flooding affected around 6.25 million people and exposed about 607,000 buildings. Regarding agricultural impacts, about 93% of crops planted during the Boro season of 2024 had already been harvested before the flooding. However, flood damage occurred on approximately 14,700 ha of remaining cropland. As the flooding occurred at the onset of the monsoon season rather than during the pre-monsoon period, the extent of crop damage was relatively lower. Nevertheless, major cities like Sylhet and Sunamganj were inundated, severely affecting large populations.
{"title":"Earth observation data-based assessment of the impacts of June 2024 flooding in the Sylhet division of Bangladesh","authors":"Imran Khan, Md Raihanul Islam","doi":"10.1016/j.nhres.2025.01.009","DOIUrl":"10.1016/j.nhres.2025.01.009","url":null,"abstract":"<div><div>The Sylhet division, located in the northeastern part of Bangladesh, experienced severe flooding in June 2024, forcing thousands to seek shelter. The flooding occurred in two phases: the first following Cyclone Remal, which made landfall in Bangladesh on May 26, 2024, and the second by mid-June. This study aimed to estimate the extent of inundation, the population and buildings affected, and the damage to crops caused by the flood. It utilized Sentinel-1 (A & B) microwave and Sentinel-2 (A & B) optical data, population data from WorldPoP and Bangladesh Bureau of Statistics (2023a) and building data from Open Buildings. Image processing and GIS techniques were applied to extract and analyze information obtained from the satellite data. The study reveals that approximately 66% of the Sylhet division was inundated as of June 19, 2024. The flooding affected around 6.25 million people and exposed about 607,000 buildings. Regarding agricultural impacts, about 93% of crops planted during the Boro season of 2024 had already been harvested before the flooding. However, flood damage occurred on approximately 14,700 ha of remaining cropland. As the flooding occurred at the onset of the monsoon season rather than during the pre-monsoon period, the extent of crop damage was relatively lower. Nevertheless, major cities like Sylhet and Sunamganj were inundated, severely affecting large populations.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 3","pages":"Pages 554-562"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145128447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01DOI: 10.1016/j.nhres.2024.12.006
Arijit Ghosh , Azizur Rahman Siddiqui
Floods are the topmost alarming hydrometeorological calamities around the globe. The Ganga-Yamuna interfluve region faces several flood hazards due to its topographical and environmental conditions. In modern times, the application of advanced technology has been implemented to predict flood susceptible regions and it predicts accurately. The principal objective of this study is to predict flood susceptible regions of the Prayagraj district of North India using advanced machine-learning models based on assessing critical flood causative factors. In addition, support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost) and logistic regression (LR) have been applied based on fifteen topographical, hydrological, and environmental variables. The result indicates that about 15% of the area comes under high to very high flood susceptible regions. The area under the curve (AUC) result indicates that AUC values of RF, SVM, XGBoost, and LR are 0.84, 0.79, 0.85, and 0.94 respectively. The outcomes will be helpful for local administrators to take necessary action for hazard mitigation planning in flood-prone regions.
{"title":"Prediction of flood susceptibility in an inter-fluvial region of Northern India using machine learning algorithms","authors":"Arijit Ghosh , Azizur Rahman Siddiqui","doi":"10.1016/j.nhres.2024.12.006","DOIUrl":"10.1016/j.nhres.2024.12.006","url":null,"abstract":"<div><div>Floods are the topmost alarming hydrometeorological calamities around the globe. The Ganga-Yamuna interfluve region faces several flood hazards due to its topographical and environmental conditions. In modern times, the application of advanced technology has been implemented to predict flood susceptible regions and it predicts accurately. The principal objective of this study is to predict flood susceptible regions of the Prayagraj district of North India using advanced machine-learning models based on assessing critical flood causative factors. In addition, support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost) and logistic regression (LR) have been applied based on fifteen topographical, hydrological, and environmental variables. The result indicates that about 15% of the area comes under high to very high flood susceptible regions. The area under the curve (AUC) result indicates that AUC values of RF, SVM, XGBoost, and LR are 0.84, 0.79, 0.85, and 0.94 respectively. The outcomes will be helpful for local administrators to take necessary action for hazard mitigation planning in flood-prone regions.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 3","pages":"Pages 468-480"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145128453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01DOI: 10.1016/j.nhres.2025.01.001
Ashish N. Bhandari , Harsharaj L. Wankhade
Landslides are viewed as a persistent problem in the Nainital and Almora districts of Kumaon Himalayas, since long. In this region, landslides have not only caused damage to property and life, but also affected the society by disrupting the utility services and economic activities. This study investigates application of eight crucial geo-factors that affect the frequency and distribution of landslides in Nainital town and its surroundings using the weighted multiclass index overlay method in geographic information system (GIS). The macro-scale landslide inventory map was prepared using the landslide locations identified from multi-temporal google imageries, field checks and the old landslide reports of the area. A total of 981 landslides were identified, mostly characterized under shallow translational rock and debris slides. For landslide susceptibility analysis, 70% of landslides were used, while the remaining 30% of landslides were considered for validation. Association between landslides and geo-factors were computed by means of Yules co-efficient (Yc) values and predictor ratings. The integrated landslide susceptibility map (LSM) was classified into two distinct categories through natural break method, a) three and b) five. Both these categorized maps reveal that nearly one-tenth of the study area is extremely susceptible to slope failures. The validation and accuracy assessment of maps display a score of more than 78% through receiver operating characteristic (ROC) curve. Besides, the landslide density index (R) also indicate a strong positive association of more than 65%.
{"title":"Bivariate landslide susceptibility analysis for parts of Kumaon Himalayas: A case study of Nainital town and its surroundings, India","authors":"Ashish N. Bhandari , Harsharaj L. Wankhade","doi":"10.1016/j.nhres.2025.01.001","DOIUrl":"10.1016/j.nhres.2025.01.001","url":null,"abstract":"<div><div>Landslides are viewed as a persistent problem in the Nainital and Almora districts of Kumaon Himalayas, since long. In this region, landslides have not only caused damage to property and life, but also affected the society by disrupting the utility services and economic activities. This study investigates application of eight crucial geo-factors that affect the frequency and distribution of landslides in Nainital town and its surroundings using the weighted multiclass index overlay method in geographic information system (GIS). The macro-scale landslide inventory map was prepared using the landslide locations identified from multi-temporal google imageries, field checks and the old landslide reports of the area. A total of 981 landslides were identified, mostly characterized under shallow translational rock and debris slides. For landslide susceptibility analysis, 70% of landslides were used, while the remaining 30% of landslides were considered for validation. Association between landslides and geo-factors were computed by means of Yules co-efficient (Yc) values and predictor ratings. The integrated landslide susceptibility map (LSM) was classified into two distinct categories through natural break method, a) three and b) five. Both these categorized maps reveal that nearly one-tenth of the study area is extremely susceptible to slope failures. The validation and accuracy assessment of maps display a score of more than 78% through receiver operating characteristic (ROC) curve. Besides, the landslide density index (R) also indicate a strong positive association of more than 65%.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 3","pages":"Pages 481-494"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145128510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01DOI: 10.1016/j.nhres.2025.01.011
Francis Kofi Tetteh , Samuel J. Abbey , Colin A. Booth , Promise D. Nukah
This study provides a systematic literature review on the current understanding and uncertainties related to climate change and its impact on slope stability, a critical issue in civil engineering and disaster management. Climate change disrupts precipitation patterns, increases soil saturation, and alters vegetation dynamics, significantly affecting slope stability. The review, supported by bibliometric analysis, offers a comprehensive overview of existing knowledge, highlighting key uncertainties and their implications for slope stability.
A detailed search of the Scopus database identified 881 relevant research articles published between 2000 and 2023, with 172 publications selected after rigorous screening. Emerging keywords from the literature indicate a growing focus on high-impact research areas, such as the relationship between climate change and slope stability. The study underscores the critical role of heavy rainfall, especially in clayey soils, in causing slope instability due to increased pore-water pressure and reduced shear strength. Additionally, slope geometry, precisely height and angle, is vital in stability assessments under extreme weather conditions.
It was suggested that Seepage analyses help predict changes in pore-water pressure, informing timely slope stability interventions while heavy rainfall increases pore-water pressure in clayey soils, lowering shear strength and raising landslide risks. Urbanisation and deforestation exacerbate slope instability. The issue of sustainable land management practices, such as reforestation and responsible urban planning, are essential to mitigate climate change impacts and stabilize slopes to addressing these combined natural and human-induced risks.
From the analysis, a typical design safety threshold is FOS >1.0, which indicates stability under most conditions. It is demonstrated from this study that slopes steeper than 30° frequently show FOS <1.0, highlighting a high risk of instability, hence proper drainage measures and slope reinforcement are crucial for steep slopes to mitigate failure risks, as excess water can lead to pore pressure build-up, reducing effective stress and shear strength. Steep slopes (≥30°) should be reinforced using retaining walls, soil nailing, or vegetation with deep root systems to enhance stability.
{"title":"Current understanding and uncertainties associated with climate change and the impact on slope stability: A systematic literature review","authors":"Francis Kofi Tetteh , Samuel J. Abbey , Colin A. Booth , Promise D. Nukah","doi":"10.1016/j.nhres.2025.01.011","DOIUrl":"10.1016/j.nhres.2025.01.011","url":null,"abstract":"<div><div>This study provides a systematic literature review on the current understanding and uncertainties related to climate change and its impact on slope stability, a critical issue in civil engineering and disaster management. Climate change disrupts precipitation patterns, increases soil saturation, and alters vegetation dynamics, significantly affecting slope stability. The review, supported by bibliometric analysis, offers a comprehensive overview of existing knowledge, highlighting key uncertainties and their implications for slope stability.</div><div>A detailed search of the Scopus database identified 881 relevant research articles published between 2000 and 2023, with 172 publications selected after rigorous screening. Emerging keywords from the literature indicate a growing focus on high-impact research areas, such as the relationship between climate change and slope stability. The study underscores the critical role of heavy rainfall, especially in clayey soils, in causing slope instability due to increased pore-water pressure and reduced shear strength. Additionally, slope geometry, precisely height and angle, is vital in stability assessments under extreme weather conditions.</div><div>It was suggested that Seepage analyses help predict changes in pore-water pressure, informing timely slope stability interventions while heavy rainfall increases pore-water pressure in clayey soils, lowering shear strength and raising landslide risks. Urbanisation and deforestation exacerbate slope instability. The issue of sustainable land management practices, such as reforestation and responsible urban planning, are essential to mitigate climate change impacts and stabilize slopes to addressing these combined natural and human-induced risks.</div><div>From the analysis, a typical design safety threshold is FOS >1.0, which indicates stability under most conditions. It is demonstrated from this study that slopes steeper than 30° frequently show FOS <1.0, highlighting a high risk of instability, hence proper drainage measures and slope reinforcement are crucial for steep slopes to mitigate failure risks, as excess water can lead to pore pressure build-up, reducing effective stress and shear strength. Steep slopes (≥30°) should be reinforced using retaining walls, soil nailing, or vegetation with deep root systems to enhance stability.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 3","pages":"Pages 563-595"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145128442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Earthquakes can trigger landslides in unstable slopes leading to a sequence of loading in structures. Although seismic vulnerability and landslide vulnerability are considered in many analyses, cascading multi-hazard analysis is not commonly reported in the existing literature. Thus, to replicate the more realistic scenario of multi-hazard cascade, earthquake only and earthquake and triggered landslide vulnerability analyses of representative code non-conforming reinforced concrete (RC) building are performed. Aggravation due to the post-seismic occurrence of landslide debris is quantified for both bare and infill frames. Fragility functions are developed for single and multi-hazard scenarios for bare and infill frame cases. The results reflect that infills can effectively control displacement, which signifies the beneficial effects of infills. It is concluded that the static loading (height of debris) is more sensitive for bare frames, whereas dynamic loading (velocity of flow) is more sensitive for infill frames. The sum of findings highlights that the effects of cascading hazard would be prominent basically at stronger ground shaking rather than the code recommended shaking scenarios.
{"title":"Multi-hazard vulnerability of code non-conforming RC buildings under earthquake followed by cascading landslide","authors":"Akanksha Kunwar , Rabindra Adhikari , Dipendra Gautam","doi":"10.1016/j.nhres.2025.01.013","DOIUrl":"10.1016/j.nhres.2025.01.013","url":null,"abstract":"<div><div>Earthquakes can trigger landslides in unstable slopes leading to a sequence of loading in structures. Although seismic vulnerability and landslide vulnerability are considered in many analyses, cascading multi-hazard analysis is not commonly reported in the existing literature. Thus, to replicate the more realistic scenario of multi-hazard cascade, earthquake only and earthquake and triggered landslide vulnerability analyses of representative code non-conforming reinforced concrete (RC) building are performed. Aggravation due to the post-seismic occurrence of landslide debris is quantified for both bare and infill frames. Fragility functions are developed for single and multi-hazard scenarios for bare and infill frame cases. The results reflect that infills can effectively control displacement, which signifies the beneficial effects of infills. It is concluded that the static loading (height of debris) is more sensitive for bare frames, whereas dynamic loading (velocity of flow) is more sensitive for infill frames. The sum of findings highlights that the effects of cascading hazard would be prominent basically at stronger ground shaking rather than the code recommended shaking scenarios.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 3","pages":"Pages 609-617"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145128444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01DOI: 10.1016/j.nhres.2025.02.004
Emmanuel A. Onsay , Rolan Jon G. Bulao , Jomar F. Rabajante
This work was conducted just two days after the onslaught of Bagyong Kristine (TS Trami) in October 2024 that flooded the Bicol Region, Philippines. We combined quantitative approaches (machine learning and econometrics) and qualitative techniques (hermeneutic phenomenological, narrative, thematic, and anthropology-at-home) to forecast future flood risks, predict disaster risk preparedness (DRP), and explore the lived experiences of households in Camarines Sur. We utilized both secondary and primary data to offer more robust analysis to support local government, uplift flooded localities, and advance scientific communities. Coastal communities of San Jose are particularly at risk, with varying flood susceptibility levels. Support Vector Machine (SVM) was used to forecast flood risks indicating moderate-to-high risks. The study explores multidimensional factors influencing DRP, floods, and calamity experiences utilizing significant indicators as a priori predictors in ML runs. Improved housing, income, and digital access are associated with higher disaster risk preparedness (DRP). Conversely, living in non-concrete housing, lacking access to basic services, experiencing poverty, and engaging in informal livelihoods elevate risk levels. Experiences with floods are linked to place of residence, water and sanitation, garbage collection, and education. Calamity experiences are associated with housing, access to amenities, informal livelihoods, and preparedness. ML predictions suggest that SVM and Random forests yield the best performance in predicting DRP. Hermeneutic analyses offer valuable and fresh insights for policymaking. It has been revealed that the region is very accustomed to typhoons but not to severe flooding. Geographical vulnerabilities near water bodies underscore the constant threat of floods, emphasizing the mix of resilience, faith, fear, and community solidarity among respondents. By blending scientific methods with indigenous wisdom, a comprehensive analysis was conducted to develop culturally integrated policies. The unexpected challenges faced reveal unpreparedness for extreme rainfall events. Community cooperation, government accountability in disaster management, and environmental conservation efforts are emphasized, advocating for proactive measures, accurate forecasting, and sustainable practices to reduce flooding disasters.
{"title":"Bagyong Kristine (TS Trami) in bicol, Philippines: Flood risk forecasting, disaster risk preparedness predictions and lived experiences through machine learning (ML), econometrics, and hermeneutic analysis","authors":"Emmanuel A. Onsay , Rolan Jon G. Bulao , Jomar F. Rabajante","doi":"10.1016/j.nhres.2025.02.004","DOIUrl":"10.1016/j.nhres.2025.02.004","url":null,"abstract":"<div><div>This work was conducted just two days after the onslaught of <em>Bagyong Kristine</em> (TS Trami) in October 2024 that flooded the Bicol Region, Philippines. We combined quantitative approaches (machine learning and econometrics) and qualitative techniques (hermeneutic phenomenological, narrative, thematic, and anthropology-at-home) to forecast future flood risks, predict disaster risk preparedness (DRP), and explore the lived experiences of households in <em>Camarines Sur</em>. We utilized both secondary and primary data to offer more robust analysis to support local government, uplift flooded localities, and advance scientific communities. Coastal communities of <em>San Jose</em> are particularly at risk, with varying flood susceptibility levels. Support Vector Machine (SVM) was used to forecast flood risks indicating moderate-to-high risks. The study explores multidimensional factors influencing DRP, floods, and calamity experiences utilizing significant indicators as a priori predictors in ML runs. Improved housing, income, and digital access are associated with higher disaster risk preparedness (DRP). Conversely, living in non-concrete housing, lacking access to basic services, experiencing poverty, and engaging in informal livelihoods elevate risk levels. Experiences with floods are linked to place of residence, water and sanitation, garbage collection, and education. Calamity experiences are associated with housing, access to amenities, informal livelihoods, and preparedness. ML predictions suggest that SVM and Random forests yield the best performance in predicting DRP. Hermeneutic analyses offer valuable and fresh insights for policymaking. It has been revealed that the region is very accustomed to typhoons but not to severe flooding. Geographical vulnerabilities near water bodies underscore the constant threat of floods, emphasizing the mix of resilience, faith, fear, and community solidarity among respondents. By blending scientific methods with indigenous wisdom, a comprehensive analysis was conducted to develop culturally integrated policies. The unexpected challenges faced reveal unpreparedness for extreme rainfall events. Community cooperation, government accountability in disaster management, and environmental conservation efforts are emphasized, advocating for proactive measures, accurate forecasting, and sustainable practices to reduce flooding disasters.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 3","pages":"Pages 644-677"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145128449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01DOI: 10.1016/j.nhres.2024.10.005
Demeke Wendim , Mamaru Genetu
Dam failure can occur due to foundation instability, downstream and upstream slopes instabilities. This study assesses the stability of upstream and downstream slope cuts at Khalid-Dijo irrigation dam project, which is located in Southern Ethiopia, 3 km south of Werabe town. Limit equilibrium and finite element shear strength reduction methods are adopted. Validation of results and comparisons between those methods are carried out. The analysis considers anticipated site conditions, including static dry, static saturated, dynamic dry and dynamic saturated conditions. Slope material properties are measured from insitu, laboratory tests and used as input parameters for the analysis to obtain factor of safety and critical strength reduction factors. The properties considered in the analysis include unit weight, cohesion, angle of internal friction, poison's ratio, dilation angle and Young's modulus. The analysis indicates that the factor of safety values for limit equilibrium methods and the critical strength reduction factor for finite element method are very similar across the three slope cuts under all anticipated conditions. The lowest factor of safety and critical strength reduction factor is 1.56 and 2.07 respectively. Generally, the proposed dam project is safe against upstream and downstream slope failures. These studies suggest that maintained the average safety factor values of both methods during the design stage are crucial to avoid unnecessary risk.
{"title":"Application of limit equilibrium and shear strength reduction techniques for stability assessment of slope cuts- a case study of khalid-Dijo dam project, southern Ethiopia","authors":"Demeke Wendim , Mamaru Genetu","doi":"10.1016/j.nhres.2024.10.005","DOIUrl":"10.1016/j.nhres.2024.10.005","url":null,"abstract":"<div><div>Dam failure can occur due to foundation instability, downstream and upstream slopes instabilities. This study assesses the stability of upstream and downstream slope cuts at Khalid-Dijo irrigation dam project, which is located in Southern Ethiopia, 3 km south of Werabe town. Limit equilibrium and finite element shear strength reduction methods are adopted. Validation of results and comparisons between those methods are carried out. The analysis considers anticipated site conditions, including static dry, static saturated, dynamic dry and dynamic saturated conditions. Slope material properties are measured from insitu, laboratory tests and used as input parameters for the analysis to obtain factor of safety and critical strength reduction factors. The properties considered in the analysis include unit weight, cohesion, angle of internal friction, poison's ratio, dilation angle and Young's modulus. The analysis indicates that the factor of safety values for limit equilibrium methods and the critical strength reduction factor for finite element method are very similar across the three slope cuts under all anticipated conditions. The lowest factor of safety and critical strength reduction factor is 1.56 and 2.07 respectively. Generally, the proposed dam project is safe against upstream and downstream slope failures. These studies suggest that maintained the average safety factor values of both methods during the design stage are crucial to avoid unnecessary risk.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 2","pages":"Pages 276-286"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01DOI: 10.1016/j.nhres.2024.12.003
Akila Agnes Sundaresan, Appadurai Arun Solomon
Natural disasters, particularly floods, have become increasingly frequent and intense in recent times, posing significant threats to human lives and infrastructure, especially in developing countries. Efficient flood detection and damage assessment are critical for effective disaster response and recovery. This study applies the DeepLabv3+ model with UAS imagery to achieve precise flood area delineation. The DeepLabv3+ model employs an encoder-decoder architecture, integrating Atrous Spatial Pyramid Pooling (ASPP) and atrous convolutions to capture multi-scale contextual features while preserving spatial details. To evaluate its performance, the study experiments with various backbone architectures, including ResNet-18, ResNet-50, MobileNetV2, and Xception, under different configurations of downsampling rates (8 and 16) and atrous rates (8, 12, and 16). ResNet-50 proves to be the most effective backbone, achieving the optimal balance between segmentation accuracy and computational efficiency. The ASPP module enhances global and local feature extraction, while the decoder combines low-level spatial and high-level semantic features for precise pixel-wise segmentation. Experimental results reveal that the DeepLabv3+ model significantly enhances the detection of flooded regions and the delineation of flood extents, providing a reliable tool for real-time disaster management and contributing to improved flood management practices. This research offers valuable insights into leveraging deep learning models for enhanced disaster response in regions where rapid and accurate flood detection is crucial.
{"title":"Post-disaster flooded region segmentation using DeepLabv3+ and unmanned aerial system imagery","authors":"Akila Agnes Sundaresan, Appadurai Arun Solomon","doi":"10.1016/j.nhres.2024.12.003","DOIUrl":"10.1016/j.nhres.2024.12.003","url":null,"abstract":"<div><div>Natural disasters, particularly floods, have become increasingly frequent and intense in recent times, posing significant threats to human lives and infrastructure, especially in developing countries. Efficient flood detection and damage assessment are critical for effective disaster response and recovery. This study applies the DeepLabv3+ model with UAS imagery to achieve precise flood area delineation. The DeepLabv3+ model employs an encoder-decoder architecture, integrating Atrous Spatial Pyramid Pooling (ASPP) and atrous convolutions to capture multi-scale contextual features while preserving spatial details. To evaluate its performance, the study experiments with various backbone architectures, including ResNet-18, ResNet-50, MobileNetV2, and Xception, under different configurations of downsampling rates (8 and 16) and atrous rates (8, 12, and 16). ResNet-50 proves to be the most effective backbone, achieving the optimal balance between segmentation accuracy and computational efficiency. The ASPP module enhances global and local feature extraction, while the decoder combines low-level spatial and high-level semantic features for precise pixel-wise segmentation. Experimental results reveal that the DeepLabv3+ model significantly enhances the detection of flooded regions and the delineation of flood extents, providing a reliable tool for real-time disaster management and contributing to improved flood management practices. This research offers valuable insights into leveraging deep learning models for enhanced disaster response in regions where rapid and accurate flood detection is crucial.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 2","pages":"Pages 363-371"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01DOI: 10.1016/j.nhres.2024.11.004
Muhammad Waqas , Usa Wannasingha Humphries , Bunthid Chueasa , Angkool Wangwongchai
Can artificial intelligence (AI) models beat traditional numerical weather prediction (NWP) models based on physical principles? The rapid advancement of AI, inherent computational limitations of NWP models, and the lack of access to big data drive this question in terms of resolution and complexity. This survey offers a systematic review of studies that integrate AI with NWP models at various stages of weather and climate modeling. It aims to address key research questions, including the types of forecasting models, the integration of AI into NWP systems, and the comparative efficacy of AI-based approaches versus conventional NWP models. It covered peer-reviewed literature from 2000 to 2024. This technical survey highlights key advancements in the application of AI within NWP modeling in data assimilation, augmentation, pre-processing, adaptive parameter tuning, optimization, uncertainty quantification, extreme event prediction, post-processing, and the interpretation of NWP outputs. While AI demonstrates significant potential in post-processing NWP outputs, pre-processing remains challenging. This survey also presents state-of-the-art AI-based hybrid models and assesses their applicability to weather data. It highlights the promise of AI in potentially replacing traditional NWP models but emphasizes the need for further advancements in model development and application. The study also offers a detailed classification of forecasting models and outlines promising directions for future research.
{"title":"Artificial intelligence and numerical weather prediction models: A technical survey","authors":"Muhammad Waqas , Usa Wannasingha Humphries , Bunthid Chueasa , Angkool Wangwongchai","doi":"10.1016/j.nhres.2024.11.004","DOIUrl":"10.1016/j.nhres.2024.11.004","url":null,"abstract":"<div><div>Can artificial intelligence (AI) models beat traditional numerical weather prediction (NWP) models based on physical principles? The rapid advancement of AI, inherent computational limitations of NWP models, and the lack of access to big data drive this question in terms of resolution and complexity. This survey offers a systematic review of studies that integrate AI with NWP models at various stages of weather and climate modeling. It aims to address key research questions, including the types of forecasting models, the integration of AI into NWP systems, and the comparative efficacy of AI-based approaches versus conventional NWP models. It covered peer-reviewed literature from 2000 to 2024. This technical survey highlights key advancements in the application of AI within NWP modeling in data assimilation, augmentation, pre-processing, adaptive parameter tuning, optimization, uncertainty quantification, extreme event prediction, post-processing, and the interpretation of NWP outputs. While AI demonstrates significant potential in post-processing NWP outputs, pre-processing remains challenging. This survey also presents state-of-the-art AI-based hybrid models and assesses their applicability to weather data. It highlights the promise of AI in potentially replacing traditional NWP models but emphasizes the need for further advancements in model development and application. The study also offers a detailed classification of forecasting models and outlines promising directions for future research.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 2","pages":"Pages 306-320"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01DOI: 10.1016/j.nhres.2024.12.001
Cesilia Mambile, Shubi Kaijage, Judith Leo
Forest fires (FFs) are a growing threat to ecosystems and human settlements, particularly in vulnerable regions such as Mount Kilimanjaro, Tanzania. Accurate and timely fire prediction is essential to mitigate these risks and improve fire management strategies. This study develops and evaluates advanced Deep Learning (DL) models for FF prediction by integrating spatiotemporal vegetation indices, environmental data, and human activity indicators. Specifically, Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNNs), and Convolutional Long Short-Term Memory (ConvLSTM) models were employed to analyze Sentinel-2 satellite imagery and weather data, along with anthropogenic factors such as beekeeping, tourism, agriculture, and deforestation rates. Leveraging this diverse, high-dimensional dataset, the ConvLSTM model engineered to capture intricate spatial and temporal relationships delivered superior performance, achieving an AUROC of 0.9785 and Accuracy 98.08%, surpassing the LSTM and CNN models. Integrating human-induced activities with environmental data, these models provide accurate and actionable predictions for fire management in high-risk areas. This study demonstrates the potential of ConvLSTM in developing operational tools for early fire detection, streamlining data-driven decision-making, improving resource allocation, and guiding preventive strategies in fire-prone regions such as Mount Kilimanjaro.
{"title":"Deep learning models for enhanced forest-fire prediction at Mount Kilimanjaro, Tanzania: Integrating satellite images, weather data and human activities data","authors":"Cesilia Mambile, Shubi Kaijage, Judith Leo","doi":"10.1016/j.nhres.2024.12.001","DOIUrl":"10.1016/j.nhres.2024.12.001","url":null,"abstract":"<div><div>Forest fires (FFs) are a growing threat to ecosystems and human settlements, particularly in vulnerable regions such as Mount Kilimanjaro, Tanzania. Accurate and timely fire prediction is essential to mitigate these risks and improve fire management strategies. This study develops and evaluates advanced Deep Learning (DL) models for FF prediction by integrating spatiotemporal vegetation indices, environmental data, and human activity indicators. Specifically, Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNNs), and Convolutional Long Short-Term Memory (ConvLSTM) models were employed to analyze Sentinel-2 satellite imagery and weather data, along with anthropogenic factors such as beekeeping, tourism, agriculture, and deforestation rates. Leveraging this diverse, high-dimensional dataset, the ConvLSTM model engineered to capture intricate spatial and temporal relationships delivered superior performance, achieving an AUROC of 0.9785 and Accuracy 98.08%, surpassing the LSTM and CNN models. Integrating human-induced activities with environmental data, these models provide accurate and actionable predictions for fire management in high-risk areas. This study demonstrates the potential of ConvLSTM in developing operational tools for early fire detection, streamlining data-driven decision-making, improving resource allocation, and guiding preventive strategies in fire-prone regions such as Mount Kilimanjaro.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 2","pages":"Pages 335-347"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号