基于GIS的青海高寒区矿山地质环境影响程度模糊评价

青海省的矿产资源主要分布于青藏高原高寒区,处于生态环境脆弱地区,具有独特的地质环境条件,生态环境保护与矿业的开发之间矛盾突出。本文以青海省东部大通河上游的江仓煤矿四井田为例,将影响矿山地质环境质量的主要矿山地质环境地质要素概括为自然因子和人为因子二大类。为尽可能实现多因素的影响和评价因子的量化,将评价因子划分为三级,建立了矿山地质环境影响程度评价指标体系。在GIS支持下,建立影响程度评价因子数据库。利用GIS空间栅格叠加分析功能,采用三级模糊综合评判方法对矿山地质环境影响程度进行了综合评价,编制了矿山地质环境影响程度评价图。基于GIS的矿山地质环境影响程度模糊评价方法将GIS技术和模糊数学理论引入矿山地质环境影响程度评价研究之中,达到了定性、定量以及定位相结合,从而可为受损的矿山地质环境实施科学保护与治理提供更加精确的信息。     

长期天气预报中有关预报因子的若干探讨

目前行之有效的长期天气预报方法都是数理统计方法。本文以江淮流域夏季旱涝的长期预报为例，对有关预报因子的几个问题作了探索。挑选有实际天气学意义、各自独立、单相关系数较高的物理因子作预报因子，更有利于提高预报准确率，预报因子的时间尺度以月、季平均值最为适宜，而以三个旬滑动平均值的效果最佳，建立各种预报量的初选、精选因子库，更适用于计算机技术制作长期预报。     

和田河流域棉蚜大发生原医及防治与气象服务

从和田河流域棉蚜越冬基数、气候条件、作物布局及发育期、防治措施、品种特点等方面，分析了和田河流域棉蚜大发生的原因，结果表明气候条件与棉蚜大发生关系十分密切，作物布局及发育期和防治措施也是棉蚜泛滥成灾的主要因素，提出了棉蚜防治的各项技术措施与气象服务。     

Spatio-temporal characteristics of small-scale wave–current ripples on the Ameland ebb-tidal delta

Ebb-tidal deltas are highly dynamic environments affected by both waves and currents that approach the coast under various angles. Among other bedforms of various scales, these hydrodynamics create small-scale bedforms (ripples), which increase the bed roughness and will therefore affect hydrodynamics and sediment transport. In morphodynamic models, sediment transport predictions depend on the roughness height, but the accuracy of these predictors has not been tested for field conditions with strongly mixed (wave–current dominated) forcing. In this study, small-scale bedforms were observed in the field with a 3D Profiling Sonar at five locations on the Ameland ebb-tidal delta, the Netherlands. Hydrodynamic conditions ranged from wave dominated to current dominated, but were mixed most of the time. Small-scale ripples were found on all studied parts of the delta, superimposed on megaripples. Even though a large range of hydrodynamic conditions was encountered, the spatio-temporal variations in small-scale ripple dimensions were relatively small (height 0.015 m, length 0.11 m). Also, the ripples were always highly three-dimensional. These small dimensions are probably caused by the fact that the bed consists of relatively fine sediment. Five bedform height predictors were tested, but they all overestimated the ripple heights, partly because they were not created for small grain sizes. Furthermore, the predictors all have a strong dependence on wave- and current-related velocities, whereas the ripple heights measured here were only related to the near-bed orbital velocity. Therefore, ripple heights and lengths in wave–current-dominated, fine-grained coastal areas ( mm) may be best estimated by constant values rather than values dependent on the hydrodynamics. In the case of the Ameland ebb-tidal delta, these values were found to be m and m. ©2019 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.     

A comparative study of extensive machine learning models for predicting long-term monthly rainfall with an ensemble of climatic and meteorological predictors

Rainfall prediction is of vital importance in water resources management. Accurate long-term rainfall prediction remains an open and challenging problem. Machine learning techniques, as an increasingly popular approach, provide an attractive alternative to traditional methods. The main objective of this study was to improve the prediction accuracy of machine learning-based methods for monthly rainfall, and to improve the understanding of the role of large-scale climatic variables and local meteorological variables in rainfall prediction. One regression model autoregressive integrated moving average model (ARIMA) and five state-of-the-art machine learning algorithms, including artificial neural networks, support vector machine, random forest (RF), gradient boosting regression, and dual-stage attention-based recurrent neural network, were implemented for monthly rainfall prediction over 25 stations in the East China region. The results showed that the ML models outperformed ARIMA model, and RF relatively outperformed other models. Local meteorological variables, humidity, and sunshine duration, were the most important predictors in improving prediction accuracy. 4-month lagged Western North Pacific Monsoon had higher importance than other large-scale climatic variables. The overall output of rainfall prediction was scalable and could be readily generalized to other regions.     

Preparation and Characterization of a New Layered Double Hydroxide, Mg–Fe–Ce

正1 Introduction In the present paper,MgCl2·6H2O,FeCl3·6H2O,and CeCl3·6H2O were used as raw materials in the precipitationhydrothermal method to synthesize MgF eC e hydrotalcite.The effects of the Fe:Ce molar ratio on the composition,crystal structure,and thermal stability of hydrotalcite are examined.Energy-dispersive X-ray spectroscopy(EDS),X-     

Forecasting the western Pacific subtropical high index during typhoon activity using a hybrid deep learning model

Seasonal location and intensity changes in the western Pacific subtropical high(WPSH) are important factors dominating the synoptic weather and the distribution and magnitude of precipitation in the rain belt over East Asia. Therefore, this article delves into the forecast of the western Pacific subtropical high index during typhoon activity by adopting a hybrid deep learning model. Firstly, the predictors, which are the inputs of the model, are analysed based on three characteristics: the first...     

煤系高岭土合成4沸石试验研究

本试验所用原料为大同怀仁和徐州夹河煤系高岭土,这两种原料的ＳｉＯ２和Ａｌ２Ｏ３分子比均接近于２,其成分能满足合成４Ａ沸石的要求。本试验首先进行了原料的焙烧活化及漂白的试验研究,解决了原料的白度问题。分析探讨了合成４Ａ沸石过程中成分、碱度、温度、时间及搅拌等对合成试验的影响,并通过对比试验获得了最佳合成条件,得到了合格的４Ａ沸石产品。     

基于EC细网格产品在乌鲁木齐机场低能见度预测中的释用

利用2015—2018年乌鲁木齐机场航空例行天气报告(METAR报)、ECMWF(European Centre for Medium-Range Weather Forecasting)细网格数值预报产品对影响能见度的主要因子进行分析,提取与低能见度相关性高的物理量作为预报因子,采用SVM方法,分别基于Poly、RBF核函数建立乌鲁木齐机场未来21 h能见度预报模型。结果表明:(1)基于预报因子区间分类的SVM模型物理意义明确,试验结果较好;以RBF为函数建立的SVM模型(SVM-RBF)预报能力更好,其训练样本预测的TS评分0.84,准确率89.20%。(2)SVM-RBF模型的检验样本中,预报准确样本的预报误差整体偏小;在漏报样本中则有能见度越低、预报误差越大的特点,模型的振荡性明显。(3)结合NCEP/NCAR再分析资料研究SVM-RBF模型对天气过程的预报表现,发现模型对于特定天气形势下引发的低能见度天气,预报误差较小且预报提前量较大。     

Environmental determinants of leech assemblage patterns in lotic and lenitic habitats

Leeches (Clitellata: Hirudinida) are abundant predators or ecto-parasites inhabiting various freshwater habitats; however many biotic and abiotic drivers of their assemblage patterns have been deduced rather than directly tested. To study species richness and composition changes in leech assemblages, 109 sites of running and stagnant water bodies were sampled in three regions of the Czech Republic in 2007–2010, together with several explanatory variables that are known or expected to be important predictors of leech distribution. In total, 17 species of leeches were recorded, varying between 0–7 and 0–9 species in lotic and lenitic sites, respectively. These differences in species richness of lotic and lenitic sites were highly significant, contrary to the abundances, which varied between 0–283 and 0–295 individuals. The main change in species composition was controlled by water temperature and morphological characteristics (e.g. substrate and cover of macrophytes), mostly reflecting the differences between lotic and lenitic habitats. We found the density of benthos (i.e. prey availability) to be the best predictor of species composition in both lotic and lenitic sites, together with the percentage of canopy cover. However, the other significant predictors (i.e. the substrate and water conductivity found to be significant in lotic sites, and the mean annual temperature and PO₄^3? in lenitic sites), differed between these habitats. Other than mean annual temperature and water temperature, which had different effects on species richness in lotic and lenitic sites, there were no other differences between lotic and lenitic sites in terms of how species richness and abundance responded to all other analyzed predictors. Our results stress the importance of prey availability and canopy for leech distribution patterns. Differences in the significant predictors of leech assemblage patterns between lotic and lenitic sites raise fundamental questions about the underlying mechanisms and ecological constraints to leech distribution in these main types of aquatic systems.