Pub Date : 2024-03-26DOI: 10.1186/s40677-024-00275-8
A. Jaya Prakash, Sazeda Begam, Vít Vilímek, Sujoy Mudi, Pulakesh Das
Operational large-scale flood monitoring using publicly available satellite data is possible with the advent of Sentinel-1 microwave data, which enables near-real-time (at 6-day intervals) flood mapping day and night, even in cloudy monsoon seasons. Automated flood inundation area identification in near-real-time involves advanced geospatial data processing platforms, such as Google Earth Engine and robust methodology (Otsu’s algorithm). The current study employs Sentinel-1 microwave data for flood extent mapping using machine learning (ML) algorithms in Assam State, India. We generated a flood hazard and soil erosion susceptibility map by combining multi-source data on weather conditions and soil and terrain characteristics. Random Forest (RF), Classification and Regression Tool (CART), and Support Vector Machine (SVM) ML algorithms were applied to generate the flood hazard map. Furthermore, we employed the multicriteria evaluation (MCE) analytical hierarchical process (AHP) for soil erosion susceptibility mapping. The highest prediction accuracy was observed for the RF model (overall accuracy [OA] > 82%), followed by the SVM (OA > 82%) and CART (OA > 81%). Over 26% of the study area indicated high flood hazard-prone areas, and approximately 60% showed high and severe potential for soil erosion due to flooding. The automated flood mapping platform is an essential resource for emergency responders and decision-makers, as it helps to guide relief activities by identifying suitable regions and appropriate logistic route planning and improving the accuracy and timeliness of emergency response efforts. Periodic flood inundation maps will help in long-term planning and policymaking, flood management, soil and biodiversity conservation, land degradation, planning sustainable agriculture interventions, crop insurance, and climate resilience studies.
{"title":"Development of an automated method for flood inundation monitoring, flood hazard, and soil erosion susceptibility assessment using machine learning and AHP–MCE techniques","authors":"A. Jaya Prakash, Sazeda Begam, Vít Vilímek, Sujoy Mudi, Pulakesh Das","doi":"10.1186/s40677-024-00275-8","DOIUrl":"https://doi.org/10.1186/s40677-024-00275-8","url":null,"abstract":"Operational large-scale flood monitoring using publicly available satellite data is possible with the advent of Sentinel-1 microwave data, which enables near-real-time (at 6-day intervals) flood mapping day and night, even in cloudy monsoon seasons. Automated flood inundation area identification in near-real-time involves advanced geospatial data processing platforms, such as Google Earth Engine and robust methodology (Otsu’s algorithm). The current study employs Sentinel-1 microwave data for flood extent mapping using machine learning (ML) algorithms in Assam State, India. We generated a flood hazard and soil erosion susceptibility map by combining multi-source data on weather conditions and soil and terrain characteristics. Random Forest (RF), Classification and Regression Tool (CART), and Support Vector Machine (SVM) ML algorithms were applied to generate the flood hazard map. Furthermore, we employed the multicriteria evaluation (MCE) analytical hierarchical process (AHP) for soil erosion susceptibility mapping. The highest prediction accuracy was observed for the RF model (overall accuracy [OA] > 82%), followed by the SVM (OA > 82%) and CART (OA > 81%). Over 26% of the study area indicated high flood hazard-prone areas, and approximately 60% showed high and severe potential for soil erosion due to flooding. The automated flood mapping platform is an essential resource for emergency responders and decision-makers, as it helps to guide relief activities by identifying suitable regions and appropriate logistic route planning and improving the accuracy and timeliness of\u0000emergency response efforts. Periodic flood inundation maps will help in long-term planning and policymaking, flood management, soil and biodiversity conservation, land degradation, planning sustainable agriculture interventions, crop insurance, and climate resilience studies.","PeriodicalId":37025,"journal":{"name":"Geoenvironmental Disasters","volume":"52 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140297875","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 : 2024-03-04DOI: 10.1186/s40677-023-00262-5
Unashish Mondal, Anish Kumar, S. K. Panda, Devesh Sharma, Someshwar Das
The current research investigates into the application of various thunderstorm indices to predict severe thunderstorm occurrences during the monsoon season across four distinct regions in India. Methods: The study assesses the prediction model’s efficacy using various skill scores and the Weather Research and Forecasting (WRF) model has been integrated for 30 h with double moment microphysics scheme NSSL-17 which accurately reproduces vertical and meteorological measures. Furthermore, it investigates fifteen thunderstorm indices derived from the ERA5 dataset to identify the most effective index for forecasting severe thunderstorms. The results indicate that combining thunderstorm indices with skill scores, such as the Heidke Skill Score and True Skill Statistic, enhances the accuracy of severe thunderstorm predictions in the Indian monsoon season. The accurate predictions rely on determining optimal thresholds for each index. The study emphasizes the importance of using multiple indices rather relying solely on single measure for predicting severe thunderstorms. Advanced indices like the Energy Helicity Index (EHI) and Supercell Composite Parameter (SCP) perform well in forecasting extreme severe thunderstorms due to their strong reliance on wind shears. The EHI (> 1), and SCP (≥ 3.5), STP (≥ 1.2) along with low SRH at 3 km (100 m2/s2), indicated no evidence of helicity or tornado activity during the event. On the other hand, the CAPE, K Index, and VT Index demonstrate robust predictive capabilities for non-severe category thunderstorms. Integrating numerous thunderstorm indices improves meteorologists’ forecasts, ensuring public safety. Based on this work, future research can improve severe weather forecasting models’ accuracy and reliability.
{"title":"Comprehensive study of thunderstorm indices threshold favorable for thunderstorms during monsoon season using WRF–ARW model and ERA5 over India","authors":"Unashish Mondal, Anish Kumar, S. K. Panda, Devesh Sharma, Someshwar Das","doi":"10.1186/s40677-023-00262-5","DOIUrl":"https://doi.org/10.1186/s40677-023-00262-5","url":null,"abstract":"The current research investigates into the application of various thunderstorm indices to predict severe thunderstorm occurrences during the monsoon season across four distinct regions in India. Methods: The study assesses the prediction model’s efficacy using various skill scores and the Weather Research and Forecasting (WRF) model has been integrated for 30 h with double moment microphysics scheme NSSL-17 which accurately reproduces vertical and meteorological measures. Furthermore, it investigates fifteen thunderstorm indices derived from the ERA5 dataset to identify the most effective index for forecasting severe thunderstorms. The results indicate that combining thunderstorm indices with skill scores, such as the Heidke Skill Score and True Skill Statistic, enhances the accuracy of severe thunderstorm predictions in the Indian monsoon season. The accurate predictions rely on determining optimal thresholds for each index. The study emphasizes the importance of using multiple indices rather relying solely on single measure for predicting severe thunderstorms. Advanced indices like the Energy Helicity Index (EHI) and Supercell Composite Parameter (SCP) perform well in forecasting extreme severe thunderstorms\u0000due to their strong reliance on wind shears. The EHI (> 1), and SCP (≥ 3.5), STP (≥ 1.2) along with low SRH at 3 km (100 m2/s2), indicated no evidence of helicity or tornado activity during the event. On the other hand, the CAPE, K Index, and VT Index demonstrate robust predictive capabilities for non-severe category thunderstorms. Integrating numerous thunderstorm indices improves meteorologists’ forecasts, ensuring public safety.\u0000Based on this work, future research can improve severe weather forecasting models’ accuracy and reliability.","PeriodicalId":37025,"journal":{"name":"Geoenvironmental Disasters","volume":"90 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140034476","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 : 2024-02-26DOI: 10.1186/s40677-024-00274-9
M. G. Winter, T. Waaser, G. Fiddes
In August 2004 a series of debris flows caused significant disruption to the Scottish (strategic) Trunk Road Network. The subsequent Scottish Road Network Landslides Study identified a number of sites considered to be at highest risk. Some of these sites have been the subject of formal quantitative assessment of the risk from debris flow to mobile road users in vehicles. The A82 in Glen Coe has the added complication that two car parks have developed on debris fans exposing significant numbers of people to the risk while, they are essentially static and largely outside their vehicles. The risk to road users is determined using a previously developed probabilistic methodology for mobile road users (mobile elements at risk) and a new and related methodology developed for static road users (static elements at risk) is described and applied. Within the latter, an entirely new metric of Annual Average Daily Visits is used to allow the temporal component of the probability of a landslide impacting a person to be determined given the occurrence of an event. While Personal Individual Risk is at an acceptable level, including for frequent users, the risk presented to society as a whole presents a rather different picture; this is largely due to the number of visitors. The results assess the overall, societal risk for mobile elements at risk as As Low As reasonably Practicable, being at a similar level to other sites, albeit with a higher risk associated with higher numbers of fatalities. The results for the static elements at risk on the other hand suggest that the risks are classified as Unacceptable for higher numbers of fatalities. The assessment of the total societal risk, for mobile and static elements at risk, at the A82 Glen Coe suggests As Low As Reasonably Practicable for low numbers of fatalities but classify as Unacceptable for higher numbers of fatalities (around 20 to 250).
{"title":"Quantitative risk assessment for static and mobile road users: methodology and application at A82 Glen Coe, Scotland","authors":"M. G. Winter, T. Waaser, G. Fiddes","doi":"10.1186/s40677-024-00274-9","DOIUrl":"https://doi.org/10.1186/s40677-024-00274-9","url":null,"abstract":"In August 2004 a series of debris flows caused significant disruption to the Scottish (strategic) Trunk Road Network. The subsequent Scottish Road Network Landslides Study identified a number of sites considered to be at highest risk. Some of these sites have been the subject of formal quantitative assessment of the risk from debris flow to mobile road users in vehicles. The A82 in Glen Coe has the added complication that two car parks have developed on debris fans exposing significant numbers of people to the risk while, they are essentially static and largely outside their vehicles. The risk to road users is determined using a previously developed probabilistic methodology for mobile road users (mobile elements at risk) and a new and related methodology developed for static road users (static elements at risk) is described and applied. Within the latter, an entirely new metric of Annual Average Daily Visits is used to allow the temporal component of the probability of a landslide impacting a person to be determined given the occurrence of an event. While Personal Individual Risk is at an acceptable level, including for frequent users, the risk presented to society as a whole presents a rather different picture; this is largely due to the number of visitors. The results assess the overall, societal risk for mobile elements at risk as As Low As reasonably Practicable, being at a similar level to other sites, albeit with a higher risk associated with higher numbers of fatalities. The results for the static elements at risk on the other hand suggest that the risks are classified as Unacceptable for higher numbers of fatalities. The assessment of the total societal risk, for mobile and static elements at risk, at the A82 Glen Coe suggests As Low As Reasonably Practicable for low numbers of fatalities but classify as Unacceptable for higher numbers of fatalities (around 20 to 250).","PeriodicalId":37025,"journal":{"name":"Geoenvironmental Disasters","volume":"24 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139969668","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 : 2024-02-21DOI: 10.1186/s40677-024-00272-x
Kongming Yan, Masakatsu Miyajima, Halil Kumsar, Ömer Aydan, Reşat Ulusay, Zhigang Tao, Ye Chen, Fawu Wang
On February 6, a successive rupture of major faults in the Eastern Anatolian Fault Zone and Cardak-Surgu fault triggered a strong mainshock (Mw 7.7) and a major aftershock (Mw 7.6) in Kahramanmaras. The successive earthquake sequence hit southern provinces in Türkiye and northern regions in Syria, causing severe fatality and economic loss. After the earthquakes, the International Consortium on Geo-disaster Reduction (ICGdR) organized an investigation team, involving specialists from China, Japan and Türkiye, to conduct a primary field reconnaissance on seismic damage of infrastructure and ground failures. The 10-day reconnaissance, including a mini-symposium at the Istanbul Technical University (ITU), was conducted from 25 March to 3 April and specifically focused on fault ruptures, liquefaction, landslide, rockfall and lateral spreading along the major ruptured faults from Antakya in Hatay to Goksun in Kahramanmaras, passing through provinces of Gaziantep, Adıyaman and Malatya. By this reconnaissance, a large amount of original seismic data was collected and a primary understanding was established for further steps on mitigation and reduction of seismic damages and its secondary geohazards.
{"title":"Preliminary report of field reconnaissance on the 6 February 2023 Kahramanmaras Earthquakes in Türkiye","authors":"Kongming Yan, Masakatsu Miyajima, Halil Kumsar, Ömer Aydan, Reşat Ulusay, Zhigang Tao, Ye Chen, Fawu Wang","doi":"10.1186/s40677-024-00272-x","DOIUrl":"https://doi.org/10.1186/s40677-024-00272-x","url":null,"abstract":"On February 6, a successive rupture of major faults in the Eastern Anatolian Fault Zone and Cardak-Surgu fault triggered a strong mainshock (Mw 7.7) and a major aftershock (Mw 7.6) in Kahramanmaras. The successive earthquake sequence hit southern provinces in Türkiye and northern regions in Syria, causing severe fatality and economic loss. After the earthquakes, the International Consortium on Geo-disaster Reduction (ICGdR) organized an investigation team, involving specialists from China, Japan and Türkiye, to conduct a primary field reconnaissance on seismic damage of infrastructure and ground failures. The 10-day reconnaissance, including a mini-symposium at the Istanbul Technical University (ITU), was conducted from 25 March to 3 April and specifically focused on fault ruptures, liquefaction, landslide, rockfall and lateral spreading along the major ruptured faults from Antakya in Hatay to Goksun in Kahramanmaras, passing through provinces of Gaziantep, Adıyaman and Malatya. By this reconnaissance, a large amount of original seismic data was collected and a primary understanding was established for further steps on mitigation and reduction of seismic damages and its secondary geohazards.","PeriodicalId":37025,"journal":{"name":"Geoenvironmental Disasters","volume":"43 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139921428","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}
A huge number of sinkhole events has been recorded in different Italian urban areas, with an occurrence frequency largely increasing in the last decades, sometimes even causing loss of human lives. The main reason for such catastrophic events is the presence of man-made underground cavities, excavated within soft rocks, several decades ago and then abandoned, at shallow depths. Here, the possibility of interaction with overlying buildings and infrastructures and the corresponding sinkhole hazard is relatively high. In such contexts, the low mechanical properties of the soft rock formations where the cavities have been excavated, like those formed of calcarenites, which outcrop in large areas of Southern Italy, and their high susceptibility to weathering processes, represent one of the most important predisposing factors for instability. Therefore, assessing the stability of underground cavities is crucial for land management and planning purposes. The mechanically-based stability charts developed by Perrotti et al. (Int J Geomech 18(7):04018071, 2018) have proved to be a valid tool for preliminary stability assessment and, although allow to identify an eventual proneness of the cave to instability, they do not provide quantitative assessment about the safety margin itself. In that regard, this study intends to present the most recent outcomes obtained in the development of the methodology and is aimed at promoting an enhanced way for their application, so that the charts can become an operative tool for preliminary sinkhole hazard assessment in similar regions in the world.
{"title":"Assessing the stability of underground caves through iSUMM (innovative, straightforward, user-friendly, mechanically-based method)","authors":"Federica Angela Mevoli, Nunzio Luciano Fazio, Michele Perrotti, Piernicola Lollino","doi":"10.1186/s40677-023-00264-3","DOIUrl":"https://doi.org/10.1186/s40677-023-00264-3","url":null,"abstract":"A huge number of sinkhole events has been recorded in different Italian urban areas, with an occurrence frequency largely increasing in the last decades, sometimes even causing loss of human lives. The main reason for such catastrophic events is the presence of man-made underground cavities, excavated within soft rocks, several decades ago and then abandoned, at shallow depths. Here, the possibility of interaction with overlying buildings and infrastructures and the corresponding sinkhole hazard is relatively high. In such contexts, the low mechanical properties of the soft rock formations where the cavities have been excavated, like those formed of calcarenites, which outcrop in large areas of Southern Italy, and their high susceptibility to weathering processes, represent one of the most important predisposing factors for instability. Therefore, assessing the stability of underground cavities is crucial for land management and planning purposes. The mechanically-based stability charts developed by Perrotti et al. (Int J Geomech 18(7):04018071, 2018) have proved to be a valid tool for preliminary stability assessment and, although allow to identify an eventual proneness of the cave to instability, they do not provide quantitative assessment about the safety margin itself. In that regard, this study intends to present the most recent outcomes obtained in the development of the methodology and is aimed at promoting an enhanced way for their application, so that the charts can become an operative tool for preliminary sinkhole hazard assessment in similar regions in the world.","PeriodicalId":37025,"journal":{"name":"Geoenvironmental Disasters","volume":"4 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139764709","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 and landslides pose significant threats to human safety and property, necessitating early warning systems. However, the high construction costs of earthquake early warning systems present a challenge. Landslide warnings are more prevalent, so linking them to earthquake warnings could address cost concerns. Hence, it is crucial to validate the feasibility of utilizing GNSS landslide monitoring as assistance for earthquake early warning systems. This paper analyzes acceleration anomaly data from 31 GNSS landslide monitoring points near the epicenter of the May 2, 2023, MW = 5.2 Baoshan earthquake in Yunnan. The response time was determined as the time difference between an earthquake's occurrence and GNSS's acceleration anomalies. This calculation helps measure the time delay and sensitivity between these two events. Data were obtained from the geological disaster monitoring and early warning management system. GNSS landslide monitoring showed high sensitivity to nearby earthquakes. The fastest response time among the 31 data points was 8 seconds, while the slowest was 56 seconds, all falling within the one-minute mark. A linear correlation was found between acceleration anomaly response time and distance from the epicenter, indicating the feasibility of GNSS landslide monitoring-assisted earthquake monitoring. A proposal is made for a GNSS landslide monitoring cluster to establish a multi-dimensional landslideearthquake disaster warning system. This approach offers new methods for combining earthquake and landslide early warning systems, leveraging existing infrastructure for cost-effectiveness and enhancing disaster preparedness.
{"title":"Study on the correlation between real-time GNSS landslide acceleration monitoring and earthquake response: a case of May 2, 2023, MW = 5.2 Baoshan earthquake, Yunnan","authors":"Zhigang Tao, Mengnan Li, Qiru Sui, Yuting Mao, Manchao He, Yuebin Jiang","doi":"10.1186/s40677-024-00273-w","DOIUrl":"https://doi.org/10.1186/s40677-024-00273-w","url":null,"abstract":"Earthquakes and landslides pose significant threats to human safety and property, necessitating early warning systems. However, the high construction costs of earthquake early warning systems present a challenge. Landslide warnings are more prevalent, so linking them to earthquake warnings could address cost concerns. Hence, it is crucial to validate the feasibility of utilizing GNSS landslide monitoring as assistance for earthquake early warning systems. This paper analyzes acceleration anomaly data from 31 GNSS landslide monitoring points near the epicenter of the May 2, 2023, MW = 5.2 Baoshan earthquake in Yunnan. The response time was determined as the time difference between an earthquake's occurrence and GNSS's acceleration anomalies. This calculation helps measure the time delay and sensitivity between these two events. Data were obtained from the geological disaster monitoring and early warning management system. GNSS landslide monitoring showed high sensitivity to nearby earthquakes. The fastest response time among the 31 data points was 8 seconds, while the slowest was 56 seconds, all falling within the one-minute mark. A linear correlation was found between acceleration anomaly response time and distance from the epicenter, indicating the feasibility of GNSS landslide monitoring-assisted earthquake monitoring. A proposal is made for a GNSS landslide monitoring cluster to establish a multi-dimensional landslideearthquake disaster warning system. This approach offers new methods for combining earthquake and landslide early warning systems, leveraging existing infrastructure for cost-effectiveness and enhancing disaster preparedness.","PeriodicalId":37025,"journal":{"name":"Geoenvironmental Disasters","volume":"15 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139764701","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}
Ground failures in a slope due to gravity, are commonly known as landslides. Depend on the compositional, geological, and structural characteristics of the unstable initiation zone and the erosional composition of the propagation zone decide the complete particle size distribution of the moving mass and its gradation. This information is most important for the study of downslope movement. Only laboratory sieve analysis cannot fulfil this target because the natural debris contains a wide range of particle sizes, especially boulders. The combined method of scaled image analysis and laboratory sieve analysis or the combined method of line-grid analysis and laboratory sieve analysis was proposed to fulfil the requirement. To study the proposed combined methods, five different locations within the downslope propagation zone from the Aranayake landslide in Sri Lanka were surveyed and analyzed. In image analysis, the high-resolution scaled image of deposited debris was analyzed by computer-based image analysis for particle sizes. Small particles were addressed by the laboratory sieve analysis using the representative debris sample taken from the same location. If the boulder sizes within the debris are too big to address this method, then the Line-grid method was performed. The particles in every 0.5 m along a measured line of debris deposition were measured in this method. If the selected location contains small particles that cannot measured manually, the representative sample was used for the laboratory sieve analysis to fulfil this range. The results of three locations indicated a 40% distribution of < 10 mm and a 60% distribution of > 10 mm representing the general distribution of the debris. Two distributions deviated from the general distribution that was surveyed and analyzed from special locations of the “near boundary of flow path” and “slope change zone” of the landslide. The combined methodology yielded successful results of complete particle size distribution for the wide range of particle sizes in debris. The variation of the particle size distribution curves of debris along the downslope depositions is planned to be used for the study of downslope propagation, damage zone assessment studies, and predicting the representative composition of future failures.
{"title":"Identification of the complete particle size distribution of landslide debris by the combined method of scaled image analysis, line-grid analysis and laboratory sieve analysis","authors":"Sandaruwan Karunarathna, Satoshi Goto, Sajith Bandaranayake, Priyantha Bandara","doi":"10.1186/s40677-024-00270-z","DOIUrl":"https://doi.org/10.1186/s40677-024-00270-z","url":null,"abstract":"Ground failures in a slope due to gravity, are commonly known as landslides. Depend on the compositional, geological, and structural characteristics of the unstable initiation zone and the erosional composition of the propagation zone decide the complete particle size distribution of the moving mass and its gradation. This information is most important for the study of downslope movement. Only laboratory sieve analysis cannot fulfil this target because the natural debris contains a wide range of particle sizes, especially boulders. The combined method of scaled image analysis and laboratory sieve analysis or the combined method of line-grid analysis and laboratory sieve analysis was proposed to fulfil the requirement. To study the proposed combined methods, five different locations within the downslope propagation zone from the Aranayake landslide in Sri Lanka were surveyed and analyzed. In image analysis, the high-resolution scaled image of deposited debris was analyzed by computer-based image analysis for particle sizes. Small particles were addressed by the laboratory sieve analysis using the representative debris sample taken from the same location. If the boulder sizes within the debris are too big to address this method, then the Line-grid method was performed. The particles in every 0.5 m along a measured line of debris deposition were measured in this method. If the selected location contains small particles that cannot measured manually, the representative sample was used for the laboratory sieve analysis to fulfil this range. The results of three locations indicated a 40% distribution of < 10 mm and a 60% distribution of > 10 mm representing the general distribution of the debris. Two distributions deviated from the general distribution that was surveyed and analyzed from special locations of the “near boundary of flow path” and “slope change zone” of the landslide. The combined methodology yielded successful results of complete particle size distribution for the wide range of particle sizes in debris. The variation of the particle size distribution curves of debris along the downslope depositions is planned to be used for the study of downslope propagation, damage zone assessment studies, and predicting the representative composition of future failures.","PeriodicalId":37025,"journal":{"name":"Geoenvironmental Disasters","volume":"19 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139764705","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 : 2024-02-12DOI: 10.1186/s40677-024-00271-y
Seung-Min Lee, Seung-Jae Lee
Landslide susceptibility assessment (LSA) is a crucial indicator of landslide hazards, and its accuracy is improving with the development of artificial intelligence (AI) technology. However, the AI algorithms are inconsistent across regions and strongly dependent on input variables. Additionally, LSA must include historical data, which often restricts the assessment to the local scale and single landslide events. In this study, we performed an LSA for the entirety of South Korea. A total of 30 input variables were constructed, consisting of 9 variables from past climate model data MK-PRISM, 12 topographical factors, and 9 environmental factors. Sixteen machine learning algorithms were used as basic classifiers, and a stacking ensemble was used on the four algorithms with the highest area under the curve (AUC). Additionally, a separate assessment model was established for areas with a risk of landslides affecting areas larger than 1 ha. The highest-performing classifier was CatBoost, with an AUC of ~ 0.89 for both assessments. Among the input variables, distance of road, daily maximum precipitation, digital elevation model, and soil depth were the most influential. In all landslide events, CatBoost, lightGBM, XGBoost, and Random Forest had the highest AUC in descending order; in large landslide events, the order was CatBoost, XGBoost, Extra Tree, and lightGBM. The stacking ensemble enabled the construction of two landslide susceptibility maps. Our findings provide a statistical method for constructing a high-resolution (30 m) landslide susceptibility map on a country scale using diverse natural factors, including past climate data.
{"title":"Landslide susceptibility assessment of South Korea using stacking ensemble machine learning","authors":"Seung-Min Lee, Seung-Jae Lee","doi":"10.1186/s40677-024-00271-y","DOIUrl":"https://doi.org/10.1186/s40677-024-00271-y","url":null,"abstract":"Landslide susceptibility assessment (LSA) is a crucial indicator of landslide hazards, and its accuracy is improving with the development of artificial intelligence (AI) technology. However, the AI algorithms are inconsistent across regions and strongly dependent on input variables. Additionally, LSA must include historical data, which often restricts the assessment to the local scale and single landslide events. In this study, we performed an LSA for the entirety of South Korea. A total of 30 input variables were constructed, consisting of 9 variables from past climate model data MK-PRISM, 12 topographical factors, and 9 environmental factors. Sixteen machine learning algorithms were used as basic classifiers, and a stacking ensemble was used on the four algorithms with the highest area under the curve (AUC). Additionally, a separate assessment model was established for areas with a risk of landslides affecting areas larger than 1 ha. The highest-performing classifier was CatBoost, with an AUC of ~ 0.89 for both assessments. Among the input variables, distance of road, daily maximum precipitation, digital elevation model, and soil depth were the most influential. In all landslide events, CatBoost, lightGBM, XGBoost, and Random Forest had the highest AUC in descending order; in large landslide events, the order was CatBoost, XGBoost, Extra Tree, and lightGBM. The stacking ensemble enabled the construction of two landslide susceptibility maps. Our findings provide a statistical method for constructing a high-resolution (30 m) landslide susceptibility map on a country scale using diverse natural factors, including past climate data.","PeriodicalId":37025,"journal":{"name":"Geoenvironmental Disasters","volume":"2 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139773079","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 : 2024-02-10DOI: 10.1186/s40677-024-00269-6
F x Anjar Tri Laksono, Manoranjan Mishra, Budi Mulyana, János Kovács
The Mediterranean Sea is a region characterized by high seismic activity, with at least 200 tsunami events recorded from the fourth century to the present twenty-first century. Numerous studies have been conducted to understand past tsunami events, earthquake–tsunami generation, tsunami recurrence periods, tsunami vulnerability zones, and tsunami hazard mitigation strategies. Therefore, gaining insights into future trends and opportunities in Mediterranean Sea tsunami research is crucial for significantly contributing to all relevant aspects. This study aims to assess such trends and opportunities through a scientometric analysis of publications indexed by Web of Science from 2000 to 2023. Based on a selection of 329 publications, including research articles, review articles, book chapters, and conference papers, published between 2000 and 2023, Italy has the highest number of publications and citations in this field. The number of publications has increased significantly, especially after the 2004 Indian Ocean, 2011 Tohoku, and 2018 Palu tsunamis. According to the keyword analysis, the terms “tsunami”, “earthquake”, “hazard”, “wave”, “Mediterranean”, “coast”, and “tectonic” were the most frequently used in these publications. Research themes consist of four classifications: motor themes, such as seismic hazard; specific but well-developed themes, like tsunamiite; emerging or disappearing themes, for example, climate change; and general or basic themes, such as equations and megaturbidite. The number of publications related to the motor theme classification continued to grow throughout 2000–2023. Topics from 2011–2023 are more complex compared to 2000–2010, characterized by the emergence of new keywords such as evacuation planning, risk reduction, risk mitigation, building vulnerability, coastal vulnerability, climate change, probabilistic tsunami hazard assessment (PTVA-3 and PTVA-4). However, topics that were popular in the 2000–2010 period (e.g., paleotsunami deposits, earthquake, and tsunami propagation analysis) also increased in 2011–2023. Research topics with high centrality and density such as seismic hazard will continue to develop and prospect. The cluster network of this topic includes seismoturbidites, sedimentary features, tsunami modeling, active faults, catalog, and historical earthquakes.
{"title":"Exploring the Mediterranean tsunami research landscape: scientometric insights and future prospects","authors":"F x Anjar Tri Laksono, Manoranjan Mishra, Budi Mulyana, János Kovács","doi":"10.1186/s40677-024-00269-6","DOIUrl":"https://doi.org/10.1186/s40677-024-00269-6","url":null,"abstract":"The Mediterranean Sea is a region characterized by high seismic activity, with at least 200 tsunami events recorded from the fourth century to the present twenty-first century. Numerous studies have been conducted to understand past tsunami events, earthquake–tsunami generation, tsunami recurrence periods, tsunami vulnerability zones, and tsunami hazard mitigation strategies. Therefore, gaining insights into future trends and opportunities in Mediterranean Sea tsunami research is crucial for significantly contributing to all relevant aspects. This study aims to assess such trends and opportunities through a scientometric analysis of publications indexed by Web of Science from 2000 to 2023. Based on a selection of 329 publications, including research articles, review articles, book chapters, and conference papers, published between 2000 and 2023, Italy has the highest number of publications and citations in this field. The number of publications has increased significantly, especially after the 2004 Indian Ocean, 2011 Tohoku, and 2018 Palu tsunamis. According to the keyword analysis, the terms “tsunami”, “earthquake”, “hazard”, “wave”, “Mediterranean”, “coast”, and “tectonic” were the most frequently used in these publications. Research themes consist of four classifications: motor themes, such as seismic hazard; specific but well-developed themes, like tsunamiite; emerging or disappearing themes, for example, climate change; and general or basic themes, such as equations and megaturbidite. The number of publications related to the motor theme classification continued to grow throughout 2000–2023. Topics from 2011–2023 are more complex compared to 2000–2010, characterized by the emergence of new keywords such as evacuation planning, risk reduction, risk mitigation, building vulnerability, coastal vulnerability, climate change, probabilistic tsunami hazard assessment (PTVA-3 and PTVA-4). However, topics that were popular in the 2000–2010 period (e.g., paleotsunami deposits, earthquake, and tsunami propagation analysis) also increased in 2011–2023. Research topics with high centrality and density such as seismic hazard will continue to develop and prospect. The cluster network of this topic includes seismoturbidites, sedimentary features, tsunami modeling, active faults, catalog, and historical earthquakes.","PeriodicalId":37025,"journal":{"name":"Geoenvironmental Disasters","volume":"14 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139764883","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 : 2024-01-27DOI: 10.1186/s40677-024-00268-7
Brecya Isa Siburian, Marzuki Marzuki, Ashar Muda Lubis
The Suban area of Curup Rejang Lebong is a tourist region in Bengkulu Province, Indonesia, close to the active Ketaun and Musi faults, which are segments of the Sumatra Fault System (SFS). However, no studies have been conducted in this area to assess how geological structures affect seismic ground motions and contribute to seismic hazard and risk assessment. The first study of seismic microzonation in the Suban area of Curup City by ambient noise measurements was conducted at 100 sites, spaced ~ 1 km apart, with 60 min of data acquisition for each site. All microseismic data were processed using the Horizontal to Vertical Spectral Ratios (HVSR) method. The HVSR method revealed the amplification factors (A0) ranging from 1.23 to 8.26 times, corresponding to natural frequency (f0) variations between 1.24 and 9.67 Hz. About 13% and 55% of the sites show high (6 ≤ A0 ≤ 9) and medium (3 ≤ A0 ≤ 6) amplifications, respectively, predominantly in the western parts of the study area, consistent with a high seismic vulnerability index (Kg). Furthermore, we also estimated the ground shear strain (GSS) of the region using the Kanai method with two large historical earthquakes at the Ketahun segment in 1943 (Mw 7.4) and the Musi segment in 1979 (Mw 6.0). The Kg value is consistent with the GSS values and indicates areas of severe damage during the historic earthquakes. Thus, the western parts of the Suban region are vulnerable to severe damage from an earthquake. These findings could provide valuable insights for future planning and risk management efforts aimed at minimizing the impact of earthquakes in the Suban region.
{"title":"Local site effects and seismic microzonation around Suban Area, Curup Rejang Lebong, Bengkulu deduced by ambient noise measurements","authors":"Brecya Isa Siburian, Marzuki Marzuki, Ashar Muda Lubis","doi":"10.1186/s40677-024-00268-7","DOIUrl":"https://doi.org/10.1186/s40677-024-00268-7","url":null,"abstract":"The Suban area of Curup Rejang Lebong is a tourist region in Bengkulu Province, Indonesia, close to the active Ketaun and Musi faults, which are segments of the Sumatra Fault System (SFS). However, no studies have been conducted in this area to assess how geological structures affect seismic ground motions and contribute to seismic hazard and risk assessment. The first study of seismic microzonation in the Suban area of Curup City by ambient noise measurements was conducted at 100 sites, spaced ~ 1 km apart, with 60 min of data acquisition for each site. All microseismic data were processed using the Horizontal to Vertical Spectral Ratios (HVSR) method. The HVSR method revealed the amplification factors (A0) ranging from 1.23 to 8.26 times, corresponding to natural frequency (f0) variations between 1.24 and 9.67 Hz. About 13% and 55% of the sites show high (6 ≤ A0 ≤ 9) and medium (3 ≤ A0 ≤ 6) amplifications, respectively, predominantly in the western parts of the study area, consistent with a high seismic vulnerability index (Kg). Furthermore, we also estimated the ground shear strain (GSS) of the region using the Kanai method with two large historical earthquakes at the Ketahun segment in 1943 (Mw 7.4) and the Musi segment in 1979 (Mw 6.0). The Kg value is consistent with the GSS values and indicates areas of severe damage during the historic earthquakes. Thus, the western parts of the Suban region are vulnerable to severe damage from an earthquake. These findings could provide valuable insights for future planning and risk management efforts aimed at minimizing the impact of earthquakes in the Suban region.","PeriodicalId":37025,"journal":{"name":"Geoenvironmental Disasters","volume":"19 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139584156","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}
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