黄河中游区水,沙变化趋势及其预测

本文用灰色系统分析方法对黄河中游区干、支流主要测站水、沙资料进行研究、评估,阐明了水、沙多年变化趋势,论证了水、沙多年变化的原因,并对几个测站试作水、沙长期预测。     

几种幼鱼曲线游泳能力的比较研究

于１９８９年３－５月，利用视觉运动反应方法测定了黑鲷、黄鳍鲷、鲻鱼、鲤鱼和白鲢５种幼鱼的曲线游泳能力。结果表明，幼鱼游泳速度随环形水道半径增加而加快，但加快的速率逐渐减缓。随游泳时间的延长，游泳速度线性下降，实测耐久时间，黑鲷为６６．７ｍｉｎ；黄鳍鲷为６６．８ｍｉｎ；鲻鱼为１０４．４ｍｉｎ；鲤鱼为７０．３ｍｉｎ；白鲢为５８．８ｍｉｎ。游泳能力指数，黑鲷为２９０．６；黄鳍鲷为１４７．３；鲻鱼为４８５     

RUNOFF-ENERGY FACTORS FORMUSLE SEDIMENT-YIELD MODEL FOR SURFACE MINES

1INTRODUCTIONSediment-yieldestimatesarerequiredforsurfacec0al-minepermitsunderFederalandStatelaws-Estimatesareneededfordeterminingtheimpactsofminingandreclamationduringmine0Peration,fordiversi0nandsmallimpoundmentdesigns,andforsizingsedimentpondstocontrolsedimenttransporteddownstfeam0faproposedminesite.Technologiesavailablef0rsediment-yieldestimationsincludetheUniversalSoilLossEquati0n(USLE,WischmierandSmith,l978);theWaterErosionPredictionPr0ject(WEPP,FlanaganandLivingst0n,l995),…     

天津市经济增长与环境污染水平关系

摘要：以经济增长与环境污染水平计量模型——环境库兹涅茨曲线为理论基础,分析天津市经济增长与环境污染之间的关系,其环境库兹涅茨曲线与传统的环境库兹涅茨曲线形态不同,出现波动性特征,呈“U型+倒U型”。其中第一阶段为1992年前,工业结构以轻工业为主,随着工业废水治理力度的提高,污染恶化趋势有所遏制,1992-1993年为“U”型环境库兹涅茨曲线的低谷;第二阶段为1992年-1998年,工业结构以重工业为主,固体废弃物与气体污染程度逐渐增加,环境污染水平有所提高,1997-1998年出现“倒U”型曲线的高峰;第三阶段为1998年后,随着综合治理措施的不断加强,城市环境质量总体处于好转态势,曲线开始出现下降趋势。分析表明,引起上述变化的原因与产业结构的变化、环境政策取向、环境保护投资力度等因素有明显关系。     

基于复杂性科学的绿洲城镇化演进理论探讨

基于复杂性科学,引入涌现、协同、“吸引盆”等概念和模型,从涌现生成、协同维生、临界相变三个维度构建绿洲城镇化演进的理论框架。对绿洲城镇化的生成、演化和转型的过程与机理进行系统剖析,并据此对诺瑟姆曲线进行新的解读。研究表明：① 绿洲城镇是以人为核心的绿洲地域系统在多时空维度上的结构和功能的整体涌现现象,涌现形成的条件包括生成主体、自组织与受限生成机制、环境策略;② 竞争与协同机制维持了绿洲城镇化系统的生命活力与进化稳定,是系统演化的根本动力,系统不同层级具有不同的协同维生机制,根据协同学和耗散结构理论构建的绿洲城镇化演进的协同度和有序度可以识别系统演化方向;③ 绿洲城镇化过程实质上也是系统的自组织临界相变,该过程是一个渐变与突变、有序与混沌、稳态与非稳态的辩证统一;④ 时空间的正负反馈机制使绿洲城镇化复杂性与有序性不断提高,且不断向自组织临界态靠近,城镇化系统外在表现为活力与多样性增强。     

Hilbert曲线层级演进关系

网格编码代数是当前全球空间网格研究中的难点,也是推动全球空间网格从数据组织管理框架发展成为新一代空间分析模型的理论基础。Hilbert曲线是设计全球空间网格单元编码的重要工具。本文以Hilbert曲线序列码作为八叉树立体网格单元的唯一编码,由Hilbert曲线数学性质推导得到网格单元Hilbert码的层级演进关系表,进而设计了用于网格编码代数运算的若干操作算子,为构建全球空间网格分析理论与方法提供参考。     

Multi-hazard susceptibility mapping based on Convolutional Neural Networks

Multi-hazard susceptibility prediction is an important component of disasters risk management plan. An effective multi-hazard risk mitigation strategy includes assessing individual hazards as well as their interactions. However, with the rapid development of artificial intelligence technology, multi-hazard susceptibility prediction techniques based on machine learning has encountered a huge bottleneck. In order to effectively solve this problem, this study proposes a multi-hazard susceptibility mapping framework using the classical deep learning algorithm of Convolutional Neural Networks (CNN). First, we use historical flash flood, debris flow and landslide locations based on Google Earth images, extensive field surveys, topography, hydrology, and environmental data sets to train and validate the proposed CNN method. Next, the proposed CNN method is assessed in comparison to conventional logistic regression and k-nearest neighbor methods using several objective criteria, i.e., coefficient of determination, overall accuracy, mean absolute error and the root mean square error. Experimental results show that the CNN method outperforms the conventional machine learning algorithms in predicting probability of flash floods, debris flows and landslides. Finally, the susceptibility maps of the three hazards based on CNN are combined to create a multi-hazard susceptibility map. It can be observed from the map that 62.43% of the study area are prone to hazards, while 37.57% of the study area are harmless. In hazard-prone areas, 16.14%, 4.94% and 30.66% of the study area are susceptible to flash floods, debris flows and landslides, respectively. In terms of concurrent hazards, 0.28%, 7.11% and 3.13% of the study area are susceptible to the joint occurrence of flash floods and debris flow, debris flow and landslides, and flash floods and landslides, respectively, whereas, 0.18% of the study area is subject to all the three hazards. The results of this study can benefit engineers, disaster managers and local government officials involved in sustainable land management and disaster risk mitigation.