Pub Date : 2024-09-16DOI: 10.1186/s40677-024-00287-4
Vittoria Capobianco, Rosa Maria Palau, Anders Solheim, Kjersti Gisnås, Graham Gilbert, Per Danielsson, Peter van der Keur
Reliable infrastructure is vital for Nordic societies, but they face escalating climate risks. Climate change is increasing magnitude and frequency of floods, storms, and landslides, making adaptive solutions crucial. This review explores Nature-Based Solutions (NbS) for mitigating natural hazards along Nordic linear infrastructure. The motivation of the review comes as result of a preliminary survey conducted among to the main infrastructure managers in the Fennoscandian peninsula. The objective was to pinpoint the natural hazards that pose greatest concern under future climate scenarios, as well as to understand which specific information is needed to adopt NbS Floods, erosion, landslides and rockfalls emerged as primary hazards of concern for the infrastructure owners, hence the review process was focused only on NbS aimed at mitigating the effects of these specific hazards. A total of 78 documents were identified from the review process and were integrated with examples and case studies from other relevant on-going and past projects. Despite only a few of the NbS identified in these documents were directly implemented for linear infrastructure such as roads and railways, and none dealing with electric grids, several NbS were identified to have a potential for implementation for Nordic linear infrastructure. A list of NbS options, not all implemented along linear infrastructure but with potential for it, is provided. This list is meant to serve as “vade mecum” for a quick and easy access to NbS as mitigation options for linear infrastructure managers in the Nordic Countries. The NbS are classified in green, blue, green/blue and hybrid approaches, and supported by examples of case studies both in the Nordic Countries as well as countries having similar climates. This review underlines the challenges and opportunities of adopting NbS. Challenges such as the lack of expertise, space and climate constraints, and path dependency on adoption of traditional infrastructure must be addressed to mainstream NbS. The review highlights the importance of standardization, European guidelines, and technical manuals in promoting NbS adoption among infrastructure managers, as well as the necessity of accounting for the wider co-benefits of NbS, including carbon sequestration, biodiversity and ecosystem services. This paper contributes to the understanding of NbS as potential natural hazards mitigation options for Nordic infrastructure networks in the face of evolving climate risks, providing valuable insights for infrastructure managers and policymakers alike.
{"title":"The potential use of nature-based solutions as natural hazard mitigation measure for linear infrastructure in the Nordic Countries","authors":"Vittoria Capobianco, Rosa Maria Palau, Anders Solheim, Kjersti Gisnås, Graham Gilbert, Per Danielsson, Peter van der Keur","doi":"10.1186/s40677-024-00287-4","DOIUrl":"https://doi.org/10.1186/s40677-024-00287-4","url":null,"abstract":"Reliable infrastructure is vital for Nordic societies, but they face escalating climate risks. Climate change is increasing magnitude and frequency of floods, storms, and landslides, making adaptive solutions crucial. This review explores Nature-Based Solutions (NbS) for mitigating natural hazards along Nordic linear infrastructure. The motivation of the review comes as result of a preliminary survey conducted among to the main infrastructure managers in the Fennoscandian peninsula. The objective was to pinpoint the natural hazards that pose greatest concern under future climate scenarios, as well as to understand which specific information is needed to adopt NbS Floods, erosion, landslides and rockfalls emerged as primary hazards of concern for the infrastructure owners, hence the review process was focused only on NbS aimed at mitigating the effects of these specific hazards. A total of 78 documents were identified from the review process and were integrated with examples and case studies from other relevant on-going and past projects. Despite only a few of the NbS identified in these documents were directly implemented for linear infrastructure such as roads and railways, and none dealing with electric grids, several NbS were identified to have a potential for implementation for Nordic linear infrastructure. A list of NbS options, not all implemented along linear infrastructure but with potential for it, is provided. This list is meant to serve as “vade mecum” for a quick and easy access to NbS as mitigation options for linear infrastructure managers in the Nordic Countries. The NbS are classified in green, blue, green/blue and hybrid approaches, and supported by examples of case studies both in the Nordic Countries as well as countries having similar climates. This review underlines the challenges and opportunities of adopting NbS. Challenges such as the lack of expertise, space and climate constraints, and path dependency on adoption of traditional infrastructure must be addressed to mainstream NbS. The review highlights the importance of standardization, European guidelines, and technical manuals in promoting NbS adoption among infrastructure managers, as well as the necessity of accounting for the wider co-benefits of NbS, including carbon sequestration, biodiversity and ecosystem services.\u0000This paper contributes to the understanding of NbS as potential natural hazards mitigation options for Nordic infrastructure networks in the face of evolving climate risks, providing valuable insights for infrastructure managers and policymakers alike.","PeriodicalId":37025,"journal":{"name":"Geoenvironmental Disasters","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142256871","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 : 2024-09-10DOI: 10.1186/s40677-024-00289-2
Sanjeev Regmi, Ranjan Kumar Dahal
Nepal has witnessed several instances of slope instability associated with Hydroelectric Projects in the recent decades. Despite this, slope instability tends to receive less attention compared to other hazards. The objective of this study is to investigate fourteen hydroelectric projects with the aim of identifying the types and causes of slope failures. Additionally, it seeks to offer a comprehensive understanding of slope stability conditions and challenges encountered during construction at project sites. To accomplish this, the study employs Key Informant Interviews with Questionnaires to delve into the slope stability concerns within Nepal’s hydropower projects. The findings are then validated through an extensive review of pertinent literature. To conduct a thorough assessment of slope stability, the study relies on on-site observations, measurements, investigations, and both in-situ and laboratory tests. It becomes evident that the careful selection of study sites, the application of geotechnical methods, and the establishment of regular monitoring are pivotal for ensuring favorable slope stability outcomes. A majority of respondents concur that cutslope is the primary factor causing slope instability with 44.4% answering affirmatively. An independent t-test reveals there is no significant difference between the variables. Moreover, the correlation which is closed to 1 suggests that perception of respondents are interconnected and tend to vary in a synchronized manner. Participants in the study widely acknowledge numerical modeling methods as a means to overcome the limitations of slope stability studies.
近几十年来，尼泊尔发生了多起与水电项目相关的斜坡失稳事件。尽管如此，与其他灾害相比，斜坡失稳往往很少受到关注。本研究旨在调查 14 个水电项目，以确定斜坡崩塌的类型和原因。此外，本研究还力求全面了解斜坡稳定性条件以及项目现场施工过程中遇到的挑战。为了实现这一目标，本研究采用了关键知情人访谈和问卷调查的方式，深入研究尼泊尔水电项目中的边坡稳定性问题。然后，通过广泛查阅相关文献，对研究结果进行验证。为了对边坡稳定性进行全面评估，研究依赖于现场观察、测量、调查以及现场和实验室测试。显而易见，谨慎选择研究地点、应用岩土工程方法以及建立定期监测机制对于确保取得有利的斜坡稳定性结果至关重要。大多数受访者都认为切坡是造成边坡不稳定的主要因素，44.4%的受访者给出了肯定的答案。独立 t 检验表明，变量之间没有显著差异。此外，接近 1 的相关性表明，受访者的看法是相互关联的，并趋于同步变化。研究参与者普遍认为数值模拟方法是克服斜坡稳定性研究局限性的一种手段。
{"title":"Consequences of slope instability and existing practices of mitigation in hydropower projects of Nepal","authors":"Sanjeev Regmi, Ranjan Kumar Dahal","doi":"10.1186/s40677-024-00289-2","DOIUrl":"https://doi.org/10.1186/s40677-024-00289-2","url":null,"abstract":"Nepal has witnessed several instances of slope instability associated with Hydroelectric Projects in the recent decades. Despite this, slope instability tends to receive less attention compared to other hazards. The objective of this study is to investigate fourteen hydroelectric projects with the aim of identifying the types and causes of slope failures. Additionally, it seeks to offer a comprehensive understanding of slope stability conditions and challenges encountered during construction at project sites. To accomplish this, the study employs Key Informant Interviews with Questionnaires to delve into the slope stability concerns within Nepal’s hydropower projects. The findings are then validated through an extensive review of pertinent literature. To conduct a thorough assessment of slope stability, the study relies on on-site observations, measurements, investigations, and both in-situ and laboratory tests. It becomes evident that the careful selection of study sites, the application of geotechnical methods, and the establishment of regular monitoring are pivotal for ensuring favorable slope stability outcomes. A majority of respondents concur that cutslope is the primary factor causing slope instability with 44.4% answering affirmatively. An independent t-test reveals there is no significant difference between the variables. Moreover, the correlation which is closed to 1 suggests that perception of respondents are interconnected and tend to vary in a synchronized manner. Participants in the study widely acknowledge numerical modeling methods as a means to overcome the limitations of slope stability studies.","PeriodicalId":37025,"journal":{"name":"Geoenvironmental Disasters","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142214136","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 : 2024-09-05DOI: 10.1186/s40677-024-00288-3
Kyrillos M. P. Ebrahim, Ali Fares, Nour Faris, Tarek Zayed
Landslides pose significant geological hazards, necessitating advanced prediction techniques to protect vulnerable populations. Reviewing landslide time series analysis predictions is found to be missing despite the availability of numerous reviews. Therefore, this paper systematically reviews time series analysis in landslide prediction, focusing on physically based causative models, highlighting data preparation, model selection, optimizations, and evaluations. The review shows that deep learning, particularly the long-short-term memory (LSTM) model, outperforms traditional methods. However, the effectiveness of these models hinges on meticulous data preparation and model optimization. While the existing literature offers valuable insights, we identify key areas for future research, including the impact of data frequency and the integration of subsurface characteristics in prediction models.
{"title":"Exploring time series models for landslide prediction: a literature review","authors":"Kyrillos M. P. Ebrahim, Ali Fares, Nour Faris, Tarek Zayed","doi":"10.1186/s40677-024-00288-3","DOIUrl":"https://doi.org/10.1186/s40677-024-00288-3","url":null,"abstract":"Landslides pose significant geological hazards, necessitating advanced prediction techniques to protect vulnerable populations. Reviewing landslide time series analysis predictions is found to be missing despite the availability of numerous reviews. Therefore, this paper systematically reviews time series analysis in landslide prediction, focusing on physically based causative models, highlighting data preparation, model selection, optimizations, and evaluations. The review shows that deep learning, particularly the long-short-term memory (LSTM) model, outperforms traditional methods. However, the effectiveness of these models hinges on meticulous data preparation and model optimization. While the existing literature offers valuable insights, we identify key areas for future research, including the impact of data frequency and the integration of subsurface characteristics in prediction models.","PeriodicalId":37025,"journal":{"name":"Geoenvironmental Disasters","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213886","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 : 2024-08-21DOI: 10.1186/s40677-024-00286-5
Hengxing Lan, Zhanting Song, Han Bao, Yangfan Ma, Changgen Yan, Shijie Liu, Juntian Wang
Loess is prone to large deformation and flow slide due to natural and artificial interfaces inside. The strength of these interfaces controls the mechanical properties of loess. Obtaining their mechanical parameters through in-situ testing is essential for evaluating the mechanical stability in loess engineering with interfaces. By developing a borehole micro static cone penetration system and creating various types of loess with interfaces, extensive borehole penetration model tests were conducted to observe changes in cone tip resistance during penetration. The response surface method was used to analyze the impact of various test conditions on the calculated resistance. A three-dimensional surface fitting method was employed to establish the relationship between penetration parameters and shear strength parameters, which was validated through in-situ testing. The developed borehole micro static cone penetration system achieves overall miniaturization while providing significant penetration power and ensuring an effective penetration distance. Cone tip resistance development during penetration can be divided into three stages: initial, rapid increase, and slow increase. The transition times between these stages vary for different soils. Calculated resistance is positively correlated with dry density and normal stress and negatively correlated with water content. A quadratic positive correlation was established between calculated resistance and shear strength parameters during penetration. In composite soils, the interaction between water content and normal stress is strong. Compared to intact soil samples, the shear strength parameters of composite soils are more prominently influenced by water content. A system for testing interface mechanical parameters was innovatively developed, fulfilling the need to obtain interface shear strength parameters for deep soil. This study can provide support for ensuring the long-term stability of the loess slope or subgrade with interfaces.
{"title":"Shear strength parameters identification of loess interface based on borehole micro static cone penetration system","authors":"Hengxing Lan, Zhanting Song, Han Bao, Yangfan Ma, Changgen Yan, Shijie Liu, Juntian Wang","doi":"10.1186/s40677-024-00286-5","DOIUrl":"https://doi.org/10.1186/s40677-024-00286-5","url":null,"abstract":"Loess is prone to large deformation and flow slide due to natural and artificial interfaces inside. The strength of these interfaces controls the mechanical properties of loess. Obtaining their mechanical parameters through in-situ testing is essential for evaluating the mechanical stability in loess engineering with interfaces. By developing a borehole micro static cone penetration system and creating various types of loess with interfaces, extensive borehole penetration model tests were conducted to observe changes in cone tip resistance during penetration. The response surface method was used to analyze the impact of various test conditions on the calculated resistance. A three-dimensional surface fitting method was employed to establish the relationship between penetration parameters and shear strength parameters, which was validated through in-situ testing. The developed borehole micro static cone penetration system achieves overall miniaturization while providing significant penetration power and ensuring an effective penetration distance. Cone tip resistance development during penetration can be divided into three stages: initial, rapid increase, and slow increase. The transition times between these stages vary for different soils. Calculated resistance is positively correlated with dry density and normal stress and negatively correlated with water content. A quadratic positive correlation was established between calculated resistance and shear strength parameters during penetration. In composite soils, the interaction between water content and normal stress is strong. Compared to intact soil samples, the shear strength parameters of composite soils are more prominently influenced by water content. A system for testing interface mechanical parameters was innovatively developed, fulfilling the need to obtain interface shear strength parameters for deep soil. This study can provide support for ensuring the long-term stability of the loess slope or subgrade with interfaces.","PeriodicalId":37025,"journal":{"name":"Geoenvironmental Disasters","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213888","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 : 2024-08-08DOI: 10.1186/s40677-024-00284-7
Mohamed S. Abdalzaher, Sayed S. R. Moustafa, W. Farid, Mahmoud M. Salim
{"title":"Enhancing analyst decisions for seismic source discrimination with an optimized learning model","authors":"Mohamed S. Abdalzaher, Sayed S. R. Moustafa, W. Farid, Mahmoud M. Salim","doi":"10.1186/s40677-024-00284-7","DOIUrl":"https://doi.org/10.1186/s40677-024-00284-7","url":null,"abstract":"","PeriodicalId":37025,"journal":{"name":"Geoenvironmental Disasters","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141926478","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}
Mass media are a new and important source of information for any natural disaster, mass emergency, pandemic, economic or political event, or extreme weather event affecting one or more communities in a country. Several techniques have been developed for data mining in social media for many natural events, but few of them have been applied to the automatic extraction of landslide events. In this study, Twitter has been investigated to detect data about landslide events in Italian-language. The main aim is to obtain an automatic text classification on the basis of information about natural hazards. The text classification for landslide events in Italian-language has still not been applied to detect this type of natural hazard. Over 13,000 data were extracted within Twitter considering five keywords referring to landslide events. The dataset was classified manually, providing a solid base for applying deep learning. The combination of BERT + CNN has been chosen for text classification and two different pre-processing approaches and bert-model have been applied. BERT-multicase + CNN without preprocessing archived the highest values of accuracy, equal to 96% and AUC of 0.96. Two advantages resulted from this studio: the Italian-language classified dataset for landslide events fills that present gap of analysing natural events using Twitter. BERT + CNN was trained to detect this information and proved to be an excellent classifier for the Italian language for landslide events.
{"title":"Detecting information from Twitter on landslide hazards in Italy using deep learning models","authors":"Rachele Franceschini, Ascanio Rosi, Filippo Catani, Nicola Casagli","doi":"10.1186/s40677-024-00279-4","DOIUrl":"https://doi.org/10.1186/s40677-024-00279-4","url":null,"abstract":"Mass media are a new and important source of information for any natural disaster, mass emergency, pandemic, economic or political event, or extreme weather event affecting one or more communities in a country. Several techniques have been developed for data mining in social media for many natural events, but few of them have been applied to the automatic extraction of landslide events. In this study, Twitter has been investigated to detect data about landslide events in Italian-language. The main aim is to obtain an automatic text classification on the basis of information about natural hazards. The text classification for landslide events in Italian-language has still not been applied to detect this type of natural hazard. Over 13,000 data were extracted within Twitter considering five keywords referring to landslide events. The dataset was classified manually, providing a solid base for applying deep learning. The combination of BERT + CNN has been chosen for text classification and two different pre-processing approaches and bert-model have been applied. BERT-multicase + CNN without preprocessing archived the highest values of accuracy, equal to 96% and AUC of 0.96. Two advantages resulted from this studio: the Italian-language classified dataset for landslide events fills that present gap of analysing natural events using Twitter. BERT + CNN was trained to detect this information and proved to be an excellent classifier for the Italian language for landslide events.","PeriodicalId":37025,"journal":{"name":"Geoenvironmental Disasters","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141867565","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 : 2024-07-18DOI: 10.1186/s40677-024-00283-8
Jun Sakamoto, Suresh Laudari, Masaki Fujioka, Tadashi Hara
Climate change has made the relationship between disaster and sustainable development more critical. Especially in developing countries, disasters frequently devastate local communities and hinder progress. Children are at a higher risk during catastrophes; however, their knowledge of disaster risk is limited. Education for disaster risk reduction can bring the necessary changes for a safe society. School disaster prevention education can raise awareness through lectures and games, but interactive dialogues between students and teachers can be more effective. Also, GPS tracking can be a valuable tool for understanding people’s behavior during disasters. This study proposes a method to analyze the relationship between students’ daily behavior and their experiences of flood damage and preparedness for disaster risk. It is a simple method using questionnaires and GPS loggers and can be applied in mountainous areas in developing countries where equipment is inadequate. The proposed method will be used to understand the current state of disaster risk among children and identify disaster risk reduction issues. The study area lies in Paluntar Municipality, Gorkha district, Nepal. Forty-five students from two schools in the region participated in the study. The results showed that students who commute to school in areas inundated by heavy rainfall in recent years were not adequately prepared for disasters, even though they had experienced flood damage in the past and were significantly more concerned about future harm. Our field observations and interviews of teacher-student relationships showed that students were very attentive to their teachers’ instruction. Teachers understand the dangers of heavy rainfall and how to prepare for it, but students need to learn how to deal with it. In other words, the school staff’s disaster education to the students effectively reduces disasters. Early warning is needed when severe floods are expected during the rainy season.
{"title":"Proposal of a method to analyze children’s flood risk exposure and risk perception using GPS tracking data and questionnaire survey","authors":"Jun Sakamoto, Suresh Laudari, Masaki Fujioka, Tadashi Hara","doi":"10.1186/s40677-024-00283-8","DOIUrl":"https://doi.org/10.1186/s40677-024-00283-8","url":null,"abstract":"Climate change has made the relationship between disaster and sustainable development more critical. Especially in developing countries, disasters frequently devastate local communities and hinder progress. Children are at a higher risk during catastrophes; however, their knowledge of disaster risk is limited. Education for disaster risk reduction can bring the necessary changes for a safe society. School disaster prevention education can raise awareness through lectures and games, but interactive dialogues between students and teachers can be more effective. Also, GPS tracking can be a valuable tool for understanding people’s behavior during disasters. This study proposes a method to analyze the relationship between students’ daily behavior and their experiences of flood damage and preparedness for disaster risk. It is a simple method using questionnaires and GPS loggers and can be applied in mountainous areas in developing countries where equipment is inadequate. The proposed method will be used to understand the current state of disaster risk among children and identify disaster risk reduction issues. The study area lies in Paluntar Municipality, Gorkha district, Nepal. Forty-five students from two schools in the region participated in the study. The results showed that students who commute to school in areas inundated by heavy rainfall in recent years were not adequately prepared for disasters, even though they had experienced flood damage in the past and were significantly more concerned about future harm. Our field observations and interviews of teacher-student relationships showed that students were very attentive to their teachers’ instruction. Teachers understand the dangers of heavy rainfall and how to prepare for it, but students need to learn how to deal with it. In other words, the school staff’s disaster education to the students effectively reduces disasters. Early warning is needed when severe floods are expected during the rainy season.","PeriodicalId":37025,"journal":{"name":"Geoenvironmental Disasters","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141744904","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 : 2024-07-18DOI: 10.1186/s40677-024-00281-w
Danrong Zhang, Dipali Jindal, Nimisha Roy, Prashanth Vangla, J. David Frost
The Indian Himalayas' susceptibility to landslides, particularly as a location where climate change effects may be event catalysts, necessitates the development of dependable landslide susceptibility maps (LSM). This study diverges from traditional binary classification models, framing LSM as a positive-unlabeled learning problem. This approach acknowledges that regions without recorded landslides are not necessarily at low risk but could simply have not experienced landslides yet. The study utilizes novel positive-unlabeled learning-enhanced algorithms—Random Forest, K-Nearest Neighbor, and Decision Tree—to create LSM for Chamoli district, India. Eleven causative factors for landslides are identified, including elevation, aspect, slope, geology, geomorphology, distance to lineament, lithology, NDVI, distance to river, distance to road and residential land use. To address spatial correlation biases, instead of randomly splitting the dataset, the study adopts spatial splitting to get the training and testing datasets. The study reveals that positive-unlabeled learning substantially improves the Area Under Curve and recall, leading to a more conservative LSM compared to binary classification methods. Analysis shows that the southern region of Chamoli exhibits high recall but lower accuracy, suggesting a latent high landslide susceptibility despite a lack of historical landslides in this region. The study also quantifies the impact of human activity on landslide risk, indicating an elevated threat to life and the local economy, especially in Chamoli's southwestern areas.
{"title":"Enhancing landslide susceptibility mapping using a positive-unlabeled machine learning approach: a case study in Chamoli, India","authors":"Danrong Zhang, Dipali Jindal, Nimisha Roy, Prashanth Vangla, J. David Frost","doi":"10.1186/s40677-024-00281-w","DOIUrl":"https://doi.org/10.1186/s40677-024-00281-w","url":null,"abstract":"The Indian Himalayas' susceptibility to landslides, particularly as a location where climate change effects may be event catalysts, necessitates the development of dependable landslide susceptibility maps (LSM). This study diverges from traditional binary classification models, framing LSM as a positive-unlabeled learning problem. This approach acknowledges that regions without recorded landslides are not necessarily at low risk but could simply have not experienced landslides yet. The study utilizes novel positive-unlabeled learning-enhanced algorithms—Random Forest, K-Nearest Neighbor, and Decision Tree—to create LSM for Chamoli district, India. Eleven causative factors for landslides are identified, including elevation, aspect, slope, geology, geomorphology, distance to lineament, lithology, NDVI, distance to river, distance to road and residential land use. To address spatial correlation biases, instead of randomly splitting the dataset, the study adopts spatial splitting to get the training and testing datasets. The study reveals that positive-unlabeled learning substantially improves the Area Under Curve and recall, leading to a more conservative LSM compared to binary classification methods. Analysis shows that the southern region of Chamoli exhibits high recall but lower accuracy, suggesting a latent high landslide susceptibility despite a lack of historical landslides in this region. The study also quantifies the impact of human activity on landslide risk, indicating an elevated threat to life and the local economy, especially in Chamoli's southwestern areas.","PeriodicalId":37025,"journal":{"name":"Geoenvironmental Disasters","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141745097","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 : 2024-07-17DOI: 10.1186/s40677-024-00282-9
G. A. Nugroho, Halimurrahman, A. Awaludin, I. Fathrio, N. J. Trilaksono, E. Maryadi, T. Sinatra, F. Renggono, Didi Satiadi, Erwin Makmur, Agie Wandala Putra, N. Cholianawati, A. Indrawati, Tesalonika Angela Putri Madethen, R. I. Hapsari
{"title":"A 4-years of radar-based observation of bow echo over Bandung basin Indonesia","authors":"G. A. Nugroho, Halimurrahman, A. Awaludin, I. Fathrio, N. J. Trilaksono, E. Maryadi, T. Sinatra, F. Renggono, Didi Satiadi, Erwin Makmur, Agie Wandala Putra, N. Cholianawati, A. Indrawati, Tesalonika Angela Putri Madethen, R. I. Hapsari","doi":"10.1186/s40677-024-00282-9","DOIUrl":"https://doi.org/10.1186/s40677-024-00282-9","url":null,"abstract":"","PeriodicalId":37025,"journal":{"name":"Geoenvironmental Disasters","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141639965","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 : 2024-07-02DOI: 10.1186/s40677-024-00280-x
Ngoc Ha Do, Satoshi Goto, Hirotaka Ochiai, Shiho Asano, Huy Loi Doan, Thanh Binh Huynh, Junji Yoshida
A large-scale flume experiment was performed to evaluate the mechanism of landslide occurrence due to rainfall using weathered granite sand. The dimensions of the flume were 9 m (length), 1 m (width), and 1 m (depth). The weathered granite sand from the actual landslide site at Da Nang City, Vietnam was used. The pore water pressure was measured by a pore-water pressure transducer at two depths (middle and bottom) to determine the process of rainwater infiltration into the soil. The surface deformation was measured with extensometers at three positions of the slope. The deformation of the entire slope was determined by the 160 cylindrical-shaped makers evenly spaced in the slope and three cameras. The results showed that the rainfall infiltrated into the slope process, increasing from negative pore water pressure to approximately 0. The maximum shear strain contour has been plotted in total and in time increments. The shear band was detected from the time increments maximum shear strain contour. The localization in the shear band formed just before failure. To the best of our knowledge, this is the largest scale laboratory test ever conducted to calculate the shear band. Moreover, it was found that the failure occurred when the sand was in an unsaturated phase. Failure does not seem to depend on the increase in pore water pressure but on the maximum shear strain. This feature can be used to explain the phenomenon of landslides that occur even when the groundwater level does not increase but large deformation occurs.
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