Tree-Ring δD as an Indicator of Asian Monsoon Intensity

Oxygen and hydrogen isotopic compositions of meteoric water are known to correlate with surface air temperature, except in tropical areas. This relationship has been described using a number of terms corresponding to specific observations, such as latitude, altitude and seasonal effects. However, these temperature effects do not seem to apply to precipitation in monsoonal areas of Asia. Questions have been raised as to whether the isotopic composition of meteoric water can be used to reconstruct paleomonsoon intensity. Tree rings of two modern spruce trees (Picea meyeri) and a 10,000-yr-old timber (Picea jezoensis) were analyzed for hydrogen isotopic composition. On average, the older tree is depleted in deuterium by 45‰ compared to the modern trees. We attribute this isotopic depletion to the strength of summer monsoons, which were more intense in the early Holocene than at present. Although this study is not definitive, it suggests that paleomonsoon intensity can be reconstructed by direct or proxy methods that yield the oxygen or hydrogen isotopic composition of meteoric water.     

Integrated evaluation of urban groundwater hydrogeochemistry in context of fractal behaviour of groundwater level fluctuations

The present study was carried out as part of a complex survey of urban groundwater quality and quantity in Szeged, southeast Hungary. The concentrations of 12 inorganic contaminants in 28 shallow groundwater monitoring wells were determined over a 2-year period (2010–2012). The evaluation of concentrations indicates remarkable contamination all over the city. Discriminant analysis (DA) was used to evaluate the spatial changes of groundwater quality. The groundwater levels were measured over a 14-year period (2000–2013). The fractal properties of water level fluctuations time series characterizing the groundwater system and Tisza River were investigated using rescaled range (R/S) analysis. The resulting Hurst exponents clearly showed the persistency and thus long memory effects of both the groundwater and the river flow. Comparison of the results of DA with the results of R/S analysis thus implies that the geological conditions and the changing groundwater quantities are not related to groundwater quality.

EDITOR A. Castellarin

ASSOCIATE EDITOR A. Fiori     

M5'模型树算法和无人机航拍影像在震后崩塌滑坡分析中的应用——以鲁甸M_S6.5地震为例

利用高分辨率无人机航拍影像,结合基本地质资料,分析了影响2014年8月3日鲁甸M_S6.5地震震后崩塌滑坡分布的主要因素,使用M5'模型树算法建立了崩塌滑坡密度与其影响因子间的分段线性模型,并检验了该模型的预测性能。结果表明,地震诱发的崩塌滑坡分布受断层距、岩土体结构强度、坡度、植被条件等的影响,其中,断层距、岩土体结构强度及坡度等为主要影响因素;崩塌滑坡易发生在结构破裂区及坡度为38°~50°的区域,其分布密度随断层距的增加而减小;利用M5'模型树算法建立的模型体现出崩塌滑坡分布与其影响因子间复杂的非线性关系,模型检验结果显示,理论模型与实际关联函数间的相关系数达到0.88,因此,可利用该模型预测地震诱发的崩塌滑坡的分布。     

基于植被物候特征的互花米草提取方法研究——以长三角湿地为例

互花米草自1979年引进我国以来迅速增长,呈现外来物种入侵势态,严重影响了滨海湿地生态系统平衡。通过遥感手段监测可以获取互花米草的时空扩展规律,为互花米草治理提供参考和依据。本文选取长三角地区3个主要湿地区域为研究区,依托Google Earth Engine (GEE)平台选取了2014—2019年Landsat 8 OLI时间序列数据,提出了一种基于物候特征的互花米草提取方法。首先,通过归一化差异湿度指数(NDMI)、归一化差异植被指数(NDVI)和归一化差异水体指数(NDWI)提取互花米草生长的高湿度区域;然后,通过植被指数构建表征植被物候特征的时间序列曲线,确定互花米草与其他植被的物候特征差异时相;最后,基于物候特征差异时相数据构建决策树提取互花米草。通过与现有的互花米草决策树提取方法和支持向量机(SVM)方法对比发现,本文方法在3个研究区的提取结果均为最优提取结果,表明本文的方法对提取互花米草具有较好的适用性。     

Analysis of the Characteristics of the D.P.R. Korean Seismic Event on September 23, 2017

A M3. 4 seismic event occurred in the Punggye-ri nuclear test site,D. P. R Korea at 16∶29 p.m. on September 23,2017. We analyzed the waveforms of this event recorded by the China Earthquake Networks Center. In conclusion,this event is complex,including an explosion with a successive earthquake. Although the beginning of the event is an explosion,fault dislocation plays the main role in the event.     

基于模糊评价理论的海洋工程采办过程风险分析方法研究

在具体研究了现代项目风险分析和风险管理常用方法基础上,将其中的事故树法、智爆法及风险指数致命度分析法相结合,利用模糊评价理论,建立了海洋工程采办过程风险分析方法。应用该方法对“曹妃甸11-1／2(CFD11-1／2)”项目的采办过程进行风险分析,得到了采办过程中的风险事故树及风险指数排序,并对风险指数较大的风险点提出建议及改进措施,进行风险规避,对工程实践起到了很好的指导作用。     

BVRI photometry of possible standard stars in NGC 188

CCD photometry of possible standard stars, in Cousins B, V, R and I, in a field in the region of NGC 188 is presented.     

How do machine learning techniques help in increasing accuracy of landslide susceptibility maps?

Landslides are abundant in mountainous regions.They are responsible for substantial damages and losses in those areas.The A1 Highway,which is an important road in Algeria,was sometimes constructed in mountainous and/or semi-mountainous areas.Previous studies of landslide susceptibility mapping conducted near this road using statistical and expert methods have yielded ordinary results.In this research,we are interested in how do machine learning techniques help in increasing accuracy of landslide susceptibility maps in the vicinity of the A1 Highway corridor.To do this,an important section at Ain Bouziane(NE,Algeria) is chosen as a case study to evaluate the landslide susceptibility using three different machine learning methods,namely,random forest(RF),support vector machine(SVM),and boosted regression tree(BRT).First,an inventory map and nine input factors were prepared for landslide susceptibility mapping(LSM) analyses.The three models were constructed to find the most susceptible areas to this phenomenon.The results were assessed by calculating the receiver operating characteristic(ROC) curve,the standard error(Std.error),and the confidence interval(CI) at 95%.The RF model reached the highest predictive accuracy(AUC=97.2%) comparatively to the other models.The outcomes of this research proved that the obtained machine learning models had the ability to predict future landslide locations in this important road section.In addition,their application gives an improvement of the accuracy of LSMs near the road corridor.The machine learning models may become an important prediction tool that will identify landslide alleviation actions.     

Polynomial chaos expansion and response surface method for nonlinear modelling of reference evapotranspiration

The feasibility of polynomial chaos expansion (PCE) and response surface method (RSM) models is investigated for modelling reference evapotranspiration (ET₀). The modelling results of the proposed models are validated against the M5 model tree and multi-layer perceptron neural network (MLPNN) methods. Two meteorological stations, Isparta and Antalya, in the Mediterranean region of Turkey, are inspected. Various input combinations of daily air temperature, solar radiation, wind speed and relative humidity are constructed as input attributes for the ET₀. Generally, the modelling accuracy is increased by increasing the number of inputs. Including wind speed in the model inputs considerably increases their accuracy in modelling ET₀. Mean absolute error (MAE), root mean square error (RMSE), agreement index (d) and Nash-Sutcliffe efficiency (NSE) are used as comparison criteria. The PCE is the most accurate model in estimating daily ET₀, giving the lowest MAE (0.036 and 0.037 mm) and RMSE (0.047 and 0.050 mm) and the highest d (0.9998 and 0.9999) and NSE (0.9992 and 0.9996) with the four-input PCE models for Isparta and Antalya, respectively.     

Evaporation modelling by heuristic regression approaches using only temperature data

Accurate estimation of pan evaporation (E_pan) is very important in water resources management, irrigation scheduling and water budget of lakes. This study investigates the accuracy of two heuristic regression approaches, multivariate adaptive regression splines (MARS) and M5 model tree (M5Tree) in estimating pan evaporation using only temperature data as input. Monthly minimum temperature, maximum temperature and E_pan data from three Turkish stations were used, with month number (periodicity information) added as input to see its effect on estimation accuracy. The models were compared with the calibrated Hargreaves-Samani (CHS), Stephens-Stewart (SS) and multiple linear regression methods. Three different train-test splitting strategies (50%–50%, 60%–40% and 75%–25%) were employed for better evaluation of the applied methods. The results show that the MARS method generally estimated monthly E_pan with higher accuracy compared to the M5Tree, CHS and SS methods. When extraterrestrial radiation, calculated from Julian date and latitude information, was used as input to the SS instead of solar radiation, satisfactory estimates were obtained. A positive effect on model accuracy was observed when involving periodicity information in inputs and increasing training data length.