Estimating landslide susceptibility areas considering the uncertainty inherent in modeling methods

Landslides are one of the most dangerous types of natural disasters, and damage due to landslides has been increasing in certain regions of the world because of increased precipitation. Policy decision makers require reliable information that can be used to establish spatial adaptation plans to protect people from landslide hazards. Researchers presently identify areas susceptible to landslides using various spatial distribution models. However, such data are associated with a high amount of uncertainty. This study focuses on quantifying the uncertainty of several spatial distribution models and identifying the effectiveness of various ensemble methods that can be used to provide reliable information to support policy decisions. The area of study was Inje-gun, Republic of Korea. Ten models were selected to assess landslide susceptibility. Moreover, five ensemble methods were selected for the aggregated results of the 10 models. The uncertainty was quantified using the coefficient of variation and the uncertainty map we developed revealed areas with strongly differing values among single models. A matrix map was created using an ensemble map and a coefficient of variation map. Using matrix analysis, we identified the areas that are most susceptible to landslides according to the ensemble model with a low uncertainty. Thus, the ensemble model can be a useful tool for supporting decision makers. The framework of this study can also be employed to support the establishment of landslide adaptation plans in other areas of the Republic of Korea and in other countries.     

Future hydro-meteorological drought of the Johor River Basin,Malaysia, based on CORDEX-SEA projections

Water scarcity issues in the Johor River Basin (JRB) could affect the populations of Malaysia and Singapore. This study provides an overview of future hydro-meteorological droughts using climate projections from an ensemble of four Coordinated Regional Climate Downscaling Experiments – Southeast Asia (CORDEX-SEA) domain outputs under the Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios for the 2021–2050 and 2071–2100 periods. The climate projections were bias corrected using the quantile mapping approach before being incorporated into the Soil and Water Assessment Tool (SWAT) hydrological model. The Standardized Precipitation Index (SPI) and Standardized Streamflow Index (SSI) were used to examine the meteorological and hydrological droughts, respectively. Overall, future annual precipitation, streamflow, and maximum and minimum temperatures are projected to change by about ?44.2 to 24.3%, ?88.7 to 42.2%, 0.8 to 3.7ºC and 0.7 to 4.7ºC, respectively. The results show that the JRB is likely to receive more frequent meteorological droughts in the future.     

柴达木盆地西台吉乃尔盐湖沉积的年代学研究

柴达木盆地中部西台吉乃尔盐湖成因及其年代框架还存在争议。通过应用光释光测年法（OSL）和放射性碳测年法（AMS¹⁴C）测定两支分别来自该盐湖中部及东部边缘区域的岩芯,获得4个OSL和5个AMS¹⁴C年代数据。结果表明：（1） 对于老于30 ka的样品放射性碳测年法存在明显的年代低估,而对于年轻样品则存在约4.0 ka的碳库效应,相对而言光释光测年方法具有一定的合理性。（2） 两支岩芯顶部粉砂质粘土层形成于约0.3 ka,中部含粉砂质粘土盐层形成于大约4.0 ka,底部粉砂质粘土层则至少形成于70 ka 之前。     

Spatial variation of abundant picoeukaryotes in the subtropical Kuroshio Current in winter

To understand the importance of picoeukaryotes in the biogeochemical cycle in the subtropical Kuroshio Current, a year-round survey of the hydrography and the distribution of picoeukaryotes were conducted in four oceanographic cruises from October 2012 to July 2013. In comparison with other seasons, the highest abundancy of photosynthetic picoeukaryotes, with concentrations >10⁴ cells/ml, was observed around the eastern boundary of the Kuroshio in the winter. Accordingly, the composition of picoeukaryotes in this cold season was further studied by a metabarcoding analysis of the 18S rRNA gene. The majority of picoeukaryotes comprised Alveolata, followed by Haptophyta and Stramenopiles. Their composition was diverse in the waters affected by the Kuroshio and in the offshore province. For Haptophyta, in contrast to clade A prevailing in the Kuroshio waters, clade B1, which was considered the host of uncultivated diazotrophic cyanobacterium group A (UCYN-A), appeared only in the offshore area. Similarly, in Stramenopiles, Pseudo-nitzschia spp. and MAST-1D, respectively, dominated in the Kuroshio-influenced and offshore areas. While Alveolata was the most abundant group, the distributions of all lineages were similar. The association between picoeukaryote succession and hydrographic change is yet to be fully understood. Our results will assist future studies on the community composition of picoplankton and their relationship with marine ecology in the region.     

Biomarker-based Seawater Temperatures of Winter Sinking Particles and Core-top Sediment in the Ulleung Basin of the East Sea

Ocean Science Journal - This study evaluates the application of biomarkerbased temperature proxy data (alkenone with its resultant $$U_{37}^{K'}$$ index and glycerol dialkyl glycerol tetraether...     

南海北部海面风速概率分布特征

利用南海北部的浮标、石油平台观测的海面风资料,分析了0~200 km范围内,不同离岸距离站点的风速的概率分布特征。观测结果指出,各站平均风速一般最大值出现在冬季,最小值出现在夏季,具有明显的季节变化特征,并且平均风速随着离岸距离的增大也逐渐增大。对于离岸距离较近的区域（100 km以内）,海面风基本符合双参数的Weibull分布,但对于100 km以外的海面风速概率分布与Weibull分布存在明显差异,随着离岸距离的增大,平均风速和风速标准偏差也相应增大。风速平均值与风速标准偏差的比值较小时,Weibull分布的偏斜度基本为正值,当比值较大时偏斜度转为负值。随着离岸距离的增大,出现与Weibull分布不一致的情况越来越多,且与对应的Weibull分布相比,其偏斜度越小,风速概率分布越不能用Weibull分布进行较好拟合。     

Intelligent modelling of clay compressibility using hybrid meta-heuristic and machine learning algorithms

Compression index Ccis an essential parameter in geotechnical design for which the effectiveness of correlation is still a challenge.This paper suggests a novel modelling approach using machine learning(ML)technique.The performance of five commonly used machine learning(ML)algorithms,i.e.back-propagation neural network(BPNN),extreme learning machine(ELM),support vector machine(SVM),random forest(RF)and evolutionary polynomial regression(EPR)in predicting Cc is comprehensively investigated.A database with a total number of 311 datasets including three input variables,i.e.initial void ratio e0,liquid limit water content wL,plasticity index Ip,and one output variable Cc is first established.Genetic algorithm(GA)is used to optimize the hyper-parameters in five ML algorithms,and the average prediction error for the 10-fold cross-validation(CV)sets is set as thefitness function in the GA for enhancing the robustness of ML models.The results indicate that ML models outperform empirical prediction formulations with lower prediction error.RF yields the lowest error followed by BPNN,ELM,EPR and SVM.If the ranges of input variables in the database are large enough,BPNN and RF models are recommended to predict Cc.Furthermore,if the distribution of input variables is continuous,RF model is the best one.Otherwise,EPR model is recommended if the ranges of input variables are small.The predicted correlations between input and output variables using five ML models show great agreement with the physical explanation.     

两种海浪模式对中国黄海海域浪高模拟能力的比较

本文利用第三代海浪数值模式WAVEWATCH和SWAN模式,分别对黄海区域进行了理想模拟计算和实际浪场的模拟计算,结果表明:理想状态下两种模式的模拟结果在大小和空间分布上都不相同,但在大小及其变化趋势士相差不大;在实际模拟中,将两种模式的模拟结果与实际观测值相比,发现SWAN模式模拟结果较WAVEWATCH模式好,只是在高风速的模拟情况下,SWAN模式模拟结果偏大,而WAVEWATCH模式模拟结果偏小.     

Analytical influence functions for displacement discontinuity and point force elements

The analytical influence functions for the elements of a hybridized displacement discontinuity and indirect boundary element method to model fracture propagation are presented. The influence functions are exact and in closed form and, thus, have significant advantages over their numerical counterparts.     

Characteristics of vertical ozone distribution in the lower troposphere in the Yangtze River Delta at Lin’an in the spring of 2001

Deterioration of air quality as a result of rapid eco- nomic development in the Yangtze River Delta (YRD) has attracted much scientific and public attention[1―7]. To explore the air quality of a region, measurement of pollutants in locations reflecting the “background” atmospheric conditions of the region is essential. The Regional Baseline Air Quality Monitoring Station of the Chinese Academy of Meteorological Sciences (CAMS) at Lin’an is located in such an area in Zhe- jiang Pro…