水利大数据研究综述

为了推动水利大数据的发展及其在水利管理中的应用,对水利大数据相关的研究进行总结和分析,并对未来的研究方向进行展望。在回顾水利大数据的产生背景和发展历程的基础上,界定了水利大数据的概念及内涵;构建了水利大数据的研究框架,并对水利大数据的存储与共享方法、分析平台、挖掘算法、数据可视化和应用等进行了探讨。结合水利大数据研究发展和应用需求,从数据集成方法、分析挖掘算法、数据安全和综合决策分析平台等方面对水利大数据研究领域的发展趋势进行阐述。     

瑞安市基础控制网改造与原有控制网精度分析

通过瑞安市基础控制网的改造实践,对各地控制网改造的方法进行了探讨,并对原有测绘成果转换到新坐标基准中进行了分析、探讨.     

弧形防波堤波浪力试验研究

在圆筒形防波堤和V形防波堤的基础上,结合离岸堤后形成V形和半圆形连续出现的韵律海岸的地貌平面形态特点,提出一种前墙为连续的半圆筒形防波堤形式。通过在波浪水槽内进行规则波物理模型试验,探究这一新型防波堤的波压力分布规律及波高、周期、水深等因素对波压力的影响。将试验结果与海港水文公式和合田良实公式计算的理论值对比分析,给出了以合田良实公式的折减系数来拟合新型弧形防波堤波浪总水平力的计算公式。结果表明:新型弧形防波堤上的波压力随波高、周期的增大而增大,其水平波浪总力比同等尺度直墙少10%左右。     

Evaluation of Rockburst Hazard in Deep Coalmines with Large Protective Island Coal Pillars

Natural Resources Research - As the cover depth of coalmines in China increases, large protective coal pillars are widely used to support the overlying strata during mining. After the longwall...     

用接收函数方法研究上海地震台阵下地壳结构

利用上海地震台阵16个台站记录的远震资料,采用接收函数线性反演方法,对台阵下的地壳速度结构进行研究,获得了研究区域内地壳厚度和地壳速度的分布特征。研究结果表明,研究区域Moho面深度约为33±2 km,Moho面深度基本不变,地幔顶部S波速度约4.4 km/s,地壳内没有发现明显的低速层。     

云南旱灾风险管理框架及对策研究

基于云南省近年来大范围持久性干旱的反思,分析云南省旱灾管理现状,借鉴国内外成功的旱灾风险管理实践经验,提出了推行云南省旱灾风险管理的必要性和可行性.并以风险管理理论为基础,将旱灾风险识别、风险评估、损失评估、风险决策与控制4个步骤为主线贯穿旱灾风险管理过程,构建了云南省旱灾风险管理框架.通过鱼骨图分析,从自然环境、社会经济2个类别识别判断影响云南省旱灾风险的10个因子.从风险管理理念、制度体系、预警与调度系统、科技支撑水平、应急水源和应急服务及灾后补偿保障6个方面提出了云南省旱灾风险管理行动对策,为云南省建立长效的抗旱减灾管理机制提供了重要建议.     

Clustering stream profiles to understand the geomorphological features and evolution of the Yangtze River by using DEMs

Stream morphology is an important indicator for revealing the geomorphological features and evolution of the Yangtze River.Existing studies on the morphology of the Yang-tze River focus on planar features.However,the vertical features are also important.Vertical features mainly control the flow ability and erosion intensity.Furthermore,traditional studies often focus on a few stream profiles in the Yangtze River.However,stream profiles are linked together by runoff nodes,thus affecting the geomorphological evolution of the Yangtze River naturally.In this study,a clustering method of stream profiles in the Yangtze River is proposed by plotting all profiles together.Then,a stream evolution index is used to investigate the geomorphological features of the stream profile clusters to reveal the evolution of the Yangtze River.Based on the stream profile clusters,the erosion base of the Yangtze River generally changes from steep to gentle from the upper reaches to the lower reaches,and the evolution degree of the stream changes from low to high.The asymmetric distribution of knickpoints in the Hanshui River Basin supports the view that the boundary of the eastward growth of the Tibetan Plateau has reached the vicinity of the Daba Mountains.     

Prediction of $$\hbox {PM}_{2.5}$$PM2.5 concentration based on weighted bagging and image contrast-sensitive features

Stochastic Environmental Research and Risk Assessment - In this paper, we propose an effective method of $$\hbox {PM}_{2.5}$$ prediction based on image contrast-sensitive features and weighted...     

地震短期综合预测的一种思路

本文介绍了一种地震短期综合预测思路,即以虚报率较低的短期异常作为“引信”,在出现1个或数个“引信”异常后,再依据其它异常综合判定发震地点和震级,其优点是据此做出的综合预测意见的成功率较高。本文以2012年江苏高邮-宝应4.9级地震和2016年江苏射阳4.4级地震的震前实时跟踪过程为例,详细介绍了该思路的应用,该综合预测思路对指导今后震情跟踪工作有实际意义。     

Strength of ensemble learning in multiclass classification of rockburst intensity

Rockbust is a violent expulsion of rock due to the extreme release of strain energy stored in surrounding rock mass, leading to considerable damages to underground strucures and equipment, and threatening workers' safety. As the operational depth of engineering projects increases, a larger number of factors influence the mechanism of rockburst. Therefore, accurate classification of rockburst intensity cannot be achieved based on conventional criteria. It is urgent to develop new models with high accuracy and ease to implement in practice. This study proposed an ensemble machine learning method by aggregating seven individual classifiers including back propagation neural network, support vector machine, decision tree, k-nearest neighbours, logistic regression, multiple linear regression and Naïve Bayes. In addition, we proposed nine data imputation methods to replace the missing values in the compiled database including 188 rockburst instances. Five-fold cross validation and the beetle antennae search algorithm are used to tune hyperparameters and voting weights of the individual classifiers. The results show that the rockburst classification accuracy obtained by the classifier ensemble has increased by 15.4% compared with the best individual classifier on the test set. The predictor importance obtained by the classifier ensemble shows that the elastic energy index is the most sensitive input variable for rockburst intensity classification. This robust ensemble method can be extended to solve other classification problems in underground engineering projects.