The Experts below are selected from a list of 11685 Experts worldwide ranked by ideXlab platform
Jianming Kuang - One of the best experts on this subject based on the ideXlab platform.
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spatial prediction of aftershocks triggered by a Major Earthquake a binary machine learning perspective
ISPRS international journal of geo-information, 2019Co-Authors: Sadra Karimzadeh, Masashi Matsuoka, Jianming KuangAbstract:Small Earthquakes following a large event in the same area are typically aftershocks, which are usually less destructive than mainshocks. These aftershocks are considered mainshocks if they are larger than the previous mainshock. In this study, records of aftershocks (M > 2.5) of the Kermanshah Earthquake (M 7.3) in Iran were collected from the first second following the event to the end of September 2018. Different machine learning (ML) algorithms, including naive Bayes, k-nearest neighbors, a support vector machine, and random forests were used in conjunction with the slip distribution, Coulomb stress change on the source fault (deduced from synthetic aperture radar imagery), and orientations of neighboring active faults to predict the aftershock patterns. Seventy percent of the aftershocks were used for training based on a binary (“yes” or “no”) logic to predict locations of all aftershocks. While untested on independent datasets, receiver operating characteristic results of the same dataset indicate ML methods outperform routine Coulomb maps regarding the spatial prediction of aftershock patterns, especially when details of neighboring active faults are available. Logistic regression results, however, do not show significant differences with ML methods, as hidden information is likely better discovered using logistic regression analysis.
Hua Fu Chen - One of the best experts on this subject based on the ideXlab platform.
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Brain structural plasticity in survivors of a Major Earthquake.
Journal of psychiatry & neuroscience : JPN, 2013Co-Authors: Su Lui, Long Chen, Li Yao, Yuan Xiao, Junran Zhang, Xiao Qi Huang, Wei Zhang, Yu Qin Wang, Hua Fu ChenAbstract:Background: Stress responses have been studied extensively in animal models, but effects of Major life stress on the human brain remain poorly understood. The aim of this study was to determine whether survivors of a Major Earthquake, who were presumed to have experienced extreme emotional stress during the disaster, demonstrate differences in brain anatomy relative to individuals who have not experienced such stressors. Methods: Healthy survivors living in an area devastated by a Major Earthquake and matched healthy controls underwent 3-dimentional high-resolution magnetic resonance imaging (MRI). Survivors were scanned 13-25 days after the Earthquake; controls had undergone MRI for other studies not long before the Earthquake. We used optimized voxel-based morphometry analysis to identify regional differences of grey matter volume between the survivors and controls. Results: We included 44 survivors (17 female, mean age 37 [standard deviation (SD) 10.6] yr) and 38 controls (14 female, mean age 35.3 [SD 11.2] yr) in our analysis. Compared with controls, the survivors showed significantly lower grey matter volume in the bilateral insula, hippocampus, left caudate and putamen, and greater grey matter volume in the bilateral orbitofrontal cortex and the parietal lobe (all p < 0.05, corrected for multiple comparison). Limitations: Differences in the variance of survivor and control data could impact study findings. Conclusion: Acute anatomic alterations could be observed in Earthquake survivors in brain regions where functional alterations after stress have been described. Anatomic changes in the present study were observed earlier than previously reported and were seen in prefrontal-limbic, parietal and striatal brain systems. Together with the results of previous functional imaging studies, our observations suggest a complex pattern of human brain response to Major life stress affecting brain systems that modulate and respond to heightened affective arousal.
Brett Robinson - One of the best experts on this subject based on the ideXlab platform.
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heavy metals in suburban gardens and the implications of land use change following a Major Earthquake
Applied Geochemistry, 2018Co-Authors: Seyedardalan Ashrafzadeh, Niklas J Lehto, Gareth Oddy, Ron G Mclaren, Lingfen Kang, Nicholas M Dickinson, Johannes Welsch, Brett RobinsonAbstract:Abstract Numerous studies have shown that urban soils can contain elevated concentrations of heavy metals (HMs). Christchurch, New Zealand, is a relatively young city (150 years old) with a population of 390,000. Most soils in Christchurch are sub-urban, with food production in residential gardens a popular activity. Earthquakes in 2010 and 2011 have resulted in the re-zoning of 630 ha of Christchurch, with suggestions that some of this land could be used for community gardens. We aimed to determine the HM concentrations in a selection of suburban gardens in Christchurch as well as in soils identified as being at risk of HM contamination due to hazardous former land uses or nearby activities. Heavy metal concentrations in suburban Christchurch garden soils were higher than normal background soil concentrations. Some 46% of the urban garden samples had Pb concentrations higher than the residential land use national standard of 210 mg kg −1 , with the most contaminated soil containing 2615 mg kg −1 Pb. Concentrations of As and Zn exceeded the residential land use national standards (20 mg kg −1 As and 400 mg kg −1 Zn) in 20% of the soils. Older neighbourhoods had significantly higher soil HM concentrations than younger neighbourhoods. Neighbourhoods developed pre-1950s had a mean Pb concentration of 282 mg kg −1 in their garden soils. Soil HM concentrations should be key criteria when determining the future land use of former residential areas that have been demolished because of the Earthquakes in 2010 and 2011. Redeveloping these areas as parklands or forests would result in less human HM exposure than agriculture or community gardens where food is produced and bare soil is exposed.
Sadra Karimzadeh - One of the best experts on this subject based on the ideXlab platform.
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spatial prediction of aftershocks triggered by a Major Earthquake a binary machine learning perspective
ISPRS international journal of geo-information, 2019Co-Authors: Sadra Karimzadeh, Masashi Matsuoka, Jianming KuangAbstract:Small Earthquakes following a large event in the same area are typically aftershocks, which are usually less destructive than mainshocks. These aftershocks are considered mainshocks if they are larger than the previous mainshock. In this study, records of aftershocks (M > 2.5) of the Kermanshah Earthquake (M 7.3) in Iran were collected from the first second following the event to the end of September 2018. Different machine learning (ML) algorithms, including naive Bayes, k-nearest neighbors, a support vector machine, and random forests were used in conjunction with the slip distribution, Coulomb stress change on the source fault (deduced from synthetic aperture radar imagery), and orientations of neighboring active faults to predict the aftershock patterns. Seventy percent of the aftershocks were used for training based on a binary (“yes” or “no”) logic to predict locations of all aftershocks. While untested on independent datasets, receiver operating characteristic results of the same dataset indicate ML methods outperform routine Coulomb maps regarding the spatial prediction of aftershock patterns, especially when details of neighboring active faults are available. Logistic regression results, however, do not show significant differences with ML methods, as hidden information is likely better discovered using logistic regression analysis.
Jun Xie - One of the best experts on this subject based on the ideXlab platform.
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modeling sediment movement and channel response to rainfall variability after a Major Earthquake
Geomorphology, 2018Co-Authors: Jun Xie, Ming Wang, Kai Liu, Tom J CoulthardAbstract:Abstract The 2008 Wenchuan Ms 8.0 Earthquake caused severe destruction in the mountainous areas of Sichuan Province, China. Landslips and mass movements led to substantial amounts of loose sediment accumulating in valleys that subsequently led to widespread riverbed aggradation. In addition to erosion and deposition hazards, this aggradation produced rivers in Earthquake affected areas that were more susceptible to flash floods under extreme rainfall events. However, fluvial processes and sediment movement after a Major Earthquake, as well as the re-working of sediments in future events, are not well studied. In this paper, we investigate the response of sediment and river channel evolution due to different rainfall scenarios after the Wenchuan Earthquake by using the CAESAR-Lisflood model. This is the first time that this landscape evolution model has been employed to explore material migration processes in a post-Earthquake area, and to test its applicability to real landform changes in the studied catchment. The CAESAR-Lisflood model is well suited to simulate sediment movement, particularly the fluvial processes driven by severe rainfall after an Earthquake. We calibrated the model parameters to the 2013 extreme rainfall event using high-resolution satellite images. Under rainfall scenarios of different intensity and frequency over a 10-yr period, landform evolution and sediment migration in the post-Earthquake area were simulated. The results showed that the sediment yield could be significantly increased under enhanced and intensified rainfall scenarios compared to a normal rainfall scenario. These findings are of importance for the planning of post-Earthquake rehabilitation and regional sustainable development, which considers risk prevention and mitigation.
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Using NDVI time series to diagnose vegetation recovery after Major Earthquake based on dynamic time warping and lower bound distance.
Ecological Indicators, 2018Co-Authors: Xuelei Zhang, Ming Wang, Kai Liu, Jun XieAbstract:Abstract Major Earthquake occurred in mountainous areas usually cause large number of landslides that lead to severe impact to local vegetation cover and growth. The negative influence to vegetation may last for many year and vegetation recovery may experience dynamic fluctuation. Existing methods for vegetation recovery diagnosis face difficulty in capturing the dynamic behaviours both within and between years that makes the interannual comparison impossible. This paper proposes a new method to diagnose regional vegetation recovery after a Major Earthquake by defining a difference measurement index (DMI) using MODIS NDVI time series at 8-day interval. This differs from many existing methods in its quantification of the difference between the studied time series and historical samples, by using a proposed algorithm consisting of lower bound distance and dynamic time warping. This algorithm can better differentiate vegetation disturbance from its natural fluctuation. Second, the method investigates relatively regional vegetation recovery via a dynamic index, the DMI. Vegetation conditions in different years can be compared with a historical benchmark and measured by DMI. This makes it possible to diagnose dynamic vegetation recovery and generate a series of interannual spatial distributions of regional vegetation state.
