Showing papers in "Natural Hazards in 2022"

Machine learning and landslide studies: recent advances and applications

Abstract: Abstract Upon the introduction of machine learning (ML) and its variants, in the form that we know today, to the landslide community, many studies have been carried out to explore the usefulness of ML in landslide research and to look at some classic landslide problems from an ML point of view. ML techniques, including deep learning methods, are becoming popular to model complex landslide problems and are starting to demonstrate promising predictive performance compared to conventional methods. Almost all the studies published in the literature in recent years belong to one of the following three broad categories: landslide detection and mapping, landslide spatial forecasting in the form of susceptibility mapping, and landslide temporal forecasting. In this paper, we present a brief overview of ML techniques, provide a general summary of the landslide studies conducted, in recent years, in the three above-mentioned categories, and make an attempt to critically evaluate the use of ML methods to model landslide processes. The paper also provides suggestions for future use of these powerful data-driven techniques in landslide studies.

Deduction of sudden rainstorm scenarios: integrating decision makers' emotions, dynamic Bayesian network and DS evidence theory

Abstract: Event scenarios serve as the basis for emergency decision making after sudden disasters, and the accuracy of scenario deduction directly determines the effectiveness of emergency management implementation. On July 20, 2021, an exceptionally heavy rainstorm disaster occurred in Zhengzhou, Henan Province, China, causing serious urban waterlogging, river floods, flash floods and landslides and resulting in major casualties and property losses:14.79 million people affected, 398 people killed or missing (380 people in Zhengzhou) and a direct economic loss of 120.06 billion RMB. In order to investigate the complex evolution process of this disaster, a dynamic Bayesian network, evidence theory and emotion update mechanism are integrated to develop an efficient and effective scenario deduction model, with an emphasis on combining subjective and objective factors. In this model, more attention is given to subjective factors such as decision makers' emotions. The elements of scenario deduction are classified into the situation status, meteorological factor, emergency activities, decision makers' emotions and emergency goals, the coupling relationship between the elements are comprehensively analyzed, and the influence of these elements on the evolution mechanism of the rainstorm disaster is investigated, so as to facilitate targeted emergency management measures for the rescue operations. The empirical results show that the proposed dynamic Bayesian network can effectively simulate the dynamic change process of scenario deduction, the improved Dempster–Shafer evidence theory can reduce the subjectivity of the model in dealing with the uncertainty of the evolution process, and the emotion update mechanism can adequately quantify and decrease the influence caused by the emotional changes of decision makers. The model may better replicate actual events, and it may apply to the scenario deduction of other disasters, making an impact on the study of sudden catastrophes.

Could road constructions be more hazardous than an earthquake in terms of mass movement?

Abstract: Abstract Roads can have a significant impact on the frequency of mass wasting events in mountainous areas. However, characterizing the extent and pervasiveness of mass movements over time has rarely been documented due to limitations in available data sources to consistently map such events. We monitored the evolution of a road network and assessed its effect on mass movements for a 11-year window in Arhavi, Turkey. The main road construction projects run in the area are associated with a hydroelectric power plant as well as other road extension works and are clearly associated with the vast majority (90.1%) of mass movements in the area. We also notice that the overall number and size of the mass movements are much larger than in the naturally occurring comparison area. This means that the sediment load originating from the anthropogenically induced mass movements is larger than its counterpart associated with naturally occurring landslides. Notably, this extra sediment load could cause river channel aggregation, reduce accommodation space and as a consequence, it could lead to an increase in the probability and severity of flooding along the river channel. This marks a strong and negative effect of human activities on the natural course of earth surface processes. We also compare frequency-area distributions of human-induced mass movements mapped in this study and co-seismic landslide inventories from the literature. By doing so, we aim to better understand the consequences of human effects on mass movements in a comparative manner. Our findings show that the damage generated by the road construction in terms of sediment loads to river channels is compatible with the possible effect of a theoretical earthquake with a magnitude greater than M w = 6.0.

Evaluating the stability of Outang landslide in the Three Gorges Reservoir area considering the mechanical behavior with large deformation of the slip zone

Abstract: The large deformation mechanical properties of slip zone soil play an important role in the stability evolution of landslide. The traditional landslide stability evaluation method can only be used to calculate a single stability factor, which cannot dynamically evaluate the landslide stability as it evolves. The large deformation mechanical properties of slip zone soil from Outang landslide in the Three Gorges Reservoir area are investigated by the indoor repeated direct shear test. Based on the damage theory, the shear damage behavior of slip zone soil with large shear displacement is analyzed, and a mechanical model describing the relationship between shear stress and shear displacement in accordance with the mechanical mechanism of landslide is established. Then, the stability of Outang landslide is dynamically evaluated by skillfully combining the mechanical model and the residual thrust method. The results show that the slip zone soil has obvious softening behavior and constant residual strength under the condition of large deformation. The model with clear physical meaning can reflect the large displacement shear mechanical properties of slip zone soil, which is consistent with the test results. The stability factor of Outang landslide gradually decreases and tends to be constant as landslide moves. The mechanical mechanism of the landslide stability evolving with deformation is the strain softening behavior of the slip zone soil, and the mechanical mechanism of the landslide stability evolving with water level is the reduction of effective stress in anti-sliding section under the influence of reservoir water. It is suggested that active measures should be taken in time in the prevention and control of landslide, and the construction of drainage engineering should be paid attention to for large-scale bank landslides.

UAV-borne, LiDAR-based elevation modelling: a method for improving local-scale urban flood risk assessment

Abstract: Abstract In this study, we present the first findings of the potential utility of miniaturized light and detection ranging (LiDAR) scanners mounted on unmanned aerial vehicles (UAVs) for improving urban flood modelling and assessments at the local scale. This is done by generating ultra-high spatial resolution digital terrain models (DTMs) featuring buildings and urban microtopographic structures that may affect floodwater pathways (DTMbs). The accuracy and level of detail of the flooded areas, simulated by a hydrologic screening model (Arc-Malstrøm), were vastly improved when DTMbs of 0.3 m resolution representing three urban sites surveyed by a UAV-LiDAR in Accra, Ghana, were used to supplement a 10 m resolution DTM covering the region’s entire catchment area. The generation of DTMbs necessitated the effective classification of UAV-LiDAR point clouds using a morphological and a triangulated irregular network method for hilly and flat landscapes, respectively. The UAV-LiDAR data enabled the identification of archways, boundary walls and bridges that were critical when predicting precise run-off courses that could not be projected using the coarser DTM only. Variations in a stream’s geometry due to a one-year time gap between the satellite-based and UAV-LiDAR data sets were also observed. The application of the coarser DTM produced an overestimate of water flows equal to 15% for sloping terrain and up to 62.5% for flat areas when compared to the respective run-offs simulated from the DTMbs. The application of UAV-LiDAR may enhance the effectiveness of urban planning by projecting precisely the locations, extents and run-offs of flooded areas in dynamic urban settings.

Digital twin-driven intelligence disaster prevention and mitigation for infrastructure: advances, challenges, and opportunities

Abstract: Natural hazards, which have the potential to cause catastrophic damage and loss to infrastructure, have increased significantly in recent decades. Thus, the construction demand for disaster prevention and mitigation for infrastructure (DPMI) systems is increasing. Many studies have applied intelligence technologies to solve key aspects of infrastructure, such as design, construction, disaster prevention and mitigation, and rescue and recovery; however, systematic construction is still lacking. Digital twin (DT) is one of the most promising technologies for multi-stage management which has great potential to solve the above challenges. This paper initially puts forward a scientific concept, in which DT drives the construction of intelligent disaster prevention and mitigation for infrastructure (IDPMI) systematically. To begin with, a scientific review of DT and IDPMI is performed, where the development of DT is summarized and a DT-based life cycle of infrastructures is defined. In addition, the intelligence technologies used in disaster management are key reviewed and their relative merits are illustrated. Furthermore, the development and technical feasibility of DT-driven IDPMI are illustrated by reviewing the relevant practice of DT in infrastructure. In conclusion, a scientific framework of DT-IDPMI is programmed, which not only provides some guidance for the deep integration between DT and IDPMI but also identifies the challenges that inspire the professional community to advance these techniques to address them in future research.

Detection and characterization of active landslides with multisource SAR data and remote sensing in western Guizhou, China

Abstract: The western part of Guizhou is located in the second step of East Asia. Although the area is stratigraphically continuous and the surface is dominated by hard limestone and sandstone, catastrophic landslides often occur, seriously threatening residents' lives and the safety of property. Accurate identification of landslides and analysis of their developmental patterns are vital to prevent and reduce the threat of geological disasters. No active landslide survey data cover this region, so this paper identifies the active landslides in the western part of Guizhou by combining surface deformation information, multitemporal optical remote sensing images, geological lithology, and geomorphic features to obtain deformation information from multisource synthetic aperture radar surface data. This process increases the accuracy and reliability of identifying unstable slopes in areas with dense vegetation and steep terrain. By processing 283 Sentinel-1 and PALSAR-2 synthetic aperture radar data, 578 active landslides, 18 of which are high-risk large-scale landslides (landslide groups), are delineated for the first time in a range of 4.64 × 104 km2 in the study area. The active landslides mainly include natural landslides, reservoir landslides, and mining-induced landslides, accounting for 2.4%, 4.2 %, and 93.4%, respectively. The spatial distribution of landslides is banded along the cuesta at the edge of an outcrop of coal strata. Landslides are mainly distributed at elevations of 1800–2000 m, with an elevation difference of 50 ~ 100 m and a slope range of 35° ~ 40°. The landslides are characterized by steep slopes, small scales, mass occurrences, and no dominant slope direction, classifying them as cuesta landslides induced by mining disturbance. Furthermore, nuanced remote sensing interpretation of the disaster elements, such as cuesta cliff, tensile cracks, deep and sizeable tensile channels, isolated rock masses, and collapse debris, and their processes of change, reveals that coal mining-disturbed landslides in this region have experienced four primary stages: natural unloading, mining disturbance, displacement acceleration, and slope failure. This is of great significance for understanding the genetic mechanism and developmental patterns, as well as the risk assessment, of this region.

Homogeneity and trend analysis of rainfall and droughts over Southeast Australia

Abstract: Abstract This study investigates rainfall and drought characteristics in southeastern Australia (New South Wales and Victoria) using data from 45 rainfall stations. Four homogeneity tests are adopted to determine inhomogeneities in the annual total rainfall (ATR) and monthly rainfall data, namely The Pettitt test, the SNHT, the Buishand range test and the Von Neumann ratio test at significance levels of 1%, 5%, and 10%. Temporal trends in rainfall (ATR, monthly, and seasonal) and droughts are examined using autocorrelated Mann–Kendall (A-MK) trend test at 1%, 5%, and 10% significance levels. We also assess meteorological droughts by using multiple drought indices (3-, 6-, 9-, 12-, 24-, and 36-month Standardized Precipitation Index (SPI) and Effective Drought Index (EDI)). Furthermore, spatial variability of temporal trends in rainfall and drought are investigated through interpolation of Sen’s slope estimator. The results represent an increasing trend in ATR between 1920 and 2019. However, southeast Australia is highly dominated by a significant negative trend in the medium term between 1970 and 2019. Winter is found to be dominated by a significantly negative trend, whereas summer and spring seasons are dominated by a positive trend. April is detected as the driest month according to magnitude of Sen’s slope and the A-MK test result. Positive trends on droughts are observed at inner parts of the study area, whereas a negative trend is detected in the south, southeast, and northeast of the study area based on SPIs and EDI. The findings of this study help to understand changes in rainfall and droughts in southeastern Australia.

Review on visualization technology in simulation training system for major natural disasters

Abstract: Major natural disasters have occurred frequently in the last few years, resulting in increased loss of life and economic damage. Most emergency responders do not have first-hand experience with major natural disasters, and thus, there is an urgent need for pre-disaster training. Due to the scenes unreality of traditional emergency drills, the failure to appeal to the target audience and the novel coronavirus pandemic, people are forced to maintain safe social distancing. Therefore, it is difficult to carry out transregional or transnational emergency drills in many countries under the lockdown. There is an increasing demand for simulation training systems that use virtual reality, augmented reality, and mixed reality visualization technologies to simulate major natural disasters. The simulation training system related to natural disasters provides a new way for popular emergency avoidance science education and emergency rescue personnel to master work responsibilities and improve emergency response capabilities. However, to our knowledge, there is no overview of the simulation training system for major natural disasters. Hence, this paper uncovers the visualization techniques commonly used in simulation training systems, and compares, analyses and summarizes the architecture and functions of the existing simulation training systems for different emergency phases of common natural disasters. In addition, the limitations of the existing simulation training system in practical applications and future development directions are discussed to provide reference for relevant researchers to better understand the modern simulation training system.

Evolution and analysis of urban resilience and its influencing factors: a case study of Jiangsu Province, China

Abstract: This research uses panel data of cities in Jiangsu from 2009 to 2018 to construct a resilience framework that measures the level of urban resilience. A combination of the entropy method, Theil index, Moran'sI , and the Spatial Durbin Model (SDM) is used to explore regional resilience development differences, the spatial correlation characteristics of urban resilience, and its influencing factors. The study finds that: (1) The spatial heterogeneity of regional resilience development is significant, as the overall level of resilience presents a spatial distribution pattern of descending from southern Jiangsu to central Jiangsu and to northern Jiangsu. (2) The total Theil index shows a wave-like downward trend during the study period. The differences between southern Jiangsu, central Jiangsu, and northern Jiangsu make up the main reason for the overall difference of urban resilience in Jiangsu Province. Among the three regions, the gap in resilience development level within southern Jiangsu is the largest. (3) There is a clear positive spatial correlation between urban resilience in the province and an obvious agglomeration trend of urban resilience levels. Among all subsystems, urban ecological resilience is the weakest and needs to be further improved. (4) Lastly, among the five factors affecting urban resilience, general public fiscal expenditure/GDP, which characterizes government factors, has the largest positive impact on urban resilience, while foreign trade has a negative impact. In the following studies, the theme of urban resilience should be constantly deepened, and more extensive data monitoring should be carried out for the urban system to improve the diversity of data sources, so as to assess urban resilience more accurately.The online version contains supplementary material available at 10.1007/s11069-022-05368-x.

Trend analysis of lakes and sinkholes in the Konya Closed Basin, in Turkey

Abstract: This study aims to investigate the trend of water-level changes in lakes (Lake Tuz and Lake Beyşehir) and sinkholes (Timraş and Kızören) in the Konya Closed Basin located in Turkey. Water-level changes in these lakes and sinkholes were investigated along with changes in meteorological parameters (precipitation, temperature, and evaporation) and groundwater trends that indicate the climate in the region. Several statistical tests can be used to determine the significance of hydrological trends over time. These tests are divided into two categories: parametric and nonparametric. In this study, the nonparametric Innovate Sen trend test, the Modified Mann–Kendall trend test, and the parametric Linear trend test were used. According to the trend analysis, the water levels of Kızören and Timraş sinkholes decreased over time, while the water levels of lakes Tuz and Beyşehir increased. These results are supported by the trends in the meteorological data and groundwater level data of the stations determined with the Thiessen polygons and sub-basin boundaries.

Early detection of emergency events from social media: a new text clustering approach

Abstract: Emergency events require early detection, quick response, and accurate recovery. In the era of big data, social media users can be seen as social sensors to monitor real-time emergency events. This paper proposed an integrated approach to detect all four kinds of emergency events early, including natural disasters, man-made accidents, public health events, and social security events. First, the BERT-Att-BiLSTM model is used to detect emergency-related posts from massive and irrelevant data. Then, the 3 W attribute information (what, where, and when) of the emergency event is extracted. With the 3 W attribute information, we create an unsupervised dynamical event clustering algorithm based on text similarity and combine it with the supervised logistical regression model to cluster posts into different events. Experiments on Sina Weibo data demonstrate the superiority of the proposed framework. Case studies on some real emergency events show that the proposed framework has good performance and high timeliness. Practical applications of the framework are also discussed, followed by future directions for improvement.

Climate extremes related with rainfall in the State of Rio de Janeiro, Brazil: a review of climatological characteristics and recorded trends

Abstract: This paper presents a synthesis of the main characteristics of precipitation in the State of Rio de Janeiro (Brazil) based on extreme rainfall indicators. Daily precipitation data are derived from 56 rainfall stations during the second half of the twentieth century and the 2000s. Eight indices related to extreme precipitation were analyzed. The Mann–Kendall nonparametric test and the Sen's Curvature were employed to evaluate the significance and magnitude of trends. The primary climatological aspects and identified trends throughout the last decades are discussed, besides the hydrometeorological impacts associated with them. Lower values of annual total precipitation are recorded in northern Rio de Janeiro (around 800 mm) and higher in the southern State (up to 2,200 mm). The Serra do Mar affects the frequency of heavy precipitation, and the areas near the sea and high relief present the highest values of consecutive days with expressive rainfall (more than 150 mm in 5 days). These areas also showed a high concentration of flood and landslides events. Most of Rio de Janeiro exhibits precipitation intensity of about 13 mm/day. The maximum number of consecutive dry days shows a gradient from the coast (about 30 days) to the State's interior (around 50 days). Regarding trends, there is a growth of accumulated extreme precipitation in various stations near the ocean. The extreme rainfall in 24 h displays an increase in most Rio de Janeiro (+ 1 to + 5 mm/decade). The consecutive dry and rainy days present similar signs of decreasing trends, suggesting irregularly distributed precipitation in the State. This study is especially relevant for decision-makers who need detailed information in the short and long term to prevent natural hazards like floods and landslides and the related impacts in the environmental and socioeconomic sectors of the Rio de Janeiro.