Prediction of Impacts Caused by South-to-North Water Diversion on Underground Water Level in Shijiazhuang

After implementation of the South-to-North Water Diversion Project which has attracted worldwide attentions, new surface water sources would be added and, accordingly, mining of groundwater for industrial and living water would be reduced to ease the mining pressure of the underground water and abate the decline of the water level in the depression centers of Shijiazhuang and the whole intake area. Numerical model was established in this paper to respectively calculate the underground water level in 2020 based on three schemes including mining status of underground water, urban water resource planning of South-to-North water diversion in Shijiazhuang and Hutuo River infiltration with artificial regulation and storage. It is estimated that after implementation of the South-to-North Water Diversion Project, the buried depth of groundwater of depression centers in Shijiazhuang in 2020 will increase by 28.5~41.09 m comparing with that without water supply through South-to-North water diversion     

新疆石河子农区土壤含盐量定量反演及其空间格局分析

盐渍化严重威胁着土地持续耕作和粮食生产安全,土壤含盐量的快速反演和及时监测,对新疆石河子农区农业可持续发展有着重大意义。以Landsat-8 OLI遥感影像与野外采样测定的72个样点土壤含盐量为数据基础,引入土壤亮度指数、土壤湿度指数、土壤盐盖度指数和土壤辐射水平指数等构建的特征遥感指数定量反演新疆石河子农垦区土壤含盐量,并对其空间布局进行了分析。研究表明：① 利用遥感影像像元特征遥感指数值反演地面对应土壤含盐量的精度较好,反演方法可行,反演精度为66%;② 反演得到土壤含盐量空间分布图在空间上等级明显,层次分明,呈现出“一带一区”的格局,“一带”是玛纳斯流域形成的盐渍化程度较高的条带,“一区”是内在土质形成的严重盐渍化区域;③ 在新疆石河子农区中,35.40%的棉田适合耕作,64.60%的棉田耕地需要进行改良,农区土壤盐渍化治理仍十分紧迫。     

基于人工智能(AI)的地质灾害防控体系建设

近年来,人工智能(AI)技术得到快速发展,并日益融入到经济社会各个领域,成为当代创新发展的新标志,智能防灾减灾将成为未来发展的趋势和研究的热点。在回顾AI发展现状与趋势的基础上,系统梳理出以往地质灾害风险防控的数据依据和传统技术方法,分析了可能采用的潜在AI方法,初步搭建了基于AI的地质灾害风险防控体系建设方案。研究表明,AI技术为地质灾害风险防控提供了新的技术途径,但目前尚无可照搬或可移植的成熟技术或解决方案。智能防灾减灾体系包括早期识别、风险评估、风险防控等3个主要环节,其中最重要的环节是早期识别,传统方法与AI技术融合的关键参数为斜坡失稳概率或泥石流发生概率;根据所依据的数据资料将早期识别方法归纳为图像识别、形变识别、位移识别、内因识别、诱因识别和综合识别等6种方法;提出了从数据层、方法层和应用层3个层次构建基于大数据智能混合优化的地质灾害风险防控平台。认为数据驱动的智能模型与理论驱动的物理模型融合是地质灾害风险防控发展的趋势。     

TRANSPORT OF GRAVELS WITH DIFFERENT RANGES OF GRAIN SIZES IN A STEEP CHANNEL

An understanding of the transport mechanism of gravel-bed rivers is very important for the river management and engineering works. The main objective of this study was to conduct a series of laboratory experiment in a steep flume to investigate the particle segregation and the transport rate of nonuniform gravel. Median sizes of 15 mm and 7.5 mm, and gradation coefficients of 1.5 and 2.0 were selected for the particle size distributions of nonuniform gravel. In addition to the 36 sets of data collected in this study, 635 sets of existing data for gravel with both nonuniform and nearly uniform sizes were analyzed. According to the results of the sieve analysis and the related theory, hiding functions for both particle size distributions of this study were derived. An attempt was made to develop an Einstein-type transport relationship for nonuniform gravel using dimensionless parameters with mean size as a representative particle size. A modified Schoklitsch-type sediment transport equation with a critical unit flow discharge was also developed to reasonably predict the transport rate of gravels. In addition, an artificial neural network (ANN) model with a back-propagation network (BPN) algorithm was also applied in this study.     

Studies on Construction Pre-control of a Connection Aisle Between Two Neighbouring Tunnels in Shanghai by Means of 3D FEM,Neural Networks and Fuzzy Logic

The ground freezing construction technique is one of the most effective and widely applied site construction methods in soft soil areas, like Shanghai. Some elevation-inclined refrigeration pipes are arranged for the artificial freezing excavation of the Pudong-side first-storey connection aisle, which is designed to connect two adjacent tunnel lanes of Shanghai East-Fuxing-Road tunnel project. No advanced research results could be found for computing the temperature field of tunnels and aisles frozen with inclined refrigeration pipes. Anyhow the computation of the relevant temperature field is of high importance for the safe and economical excavation of the above-mentioned aisle. In this paper, a method for computing the aisle temperature field using 3D FEM is given, and the computation accuracy is verified by contrasting the computed and site measured results. The back propagation neural networks are also applied to the temperature prediction using self-developed Neural Network-Expert System software, the predicted results are also very satisfactory. The mechanism during freezing and aisle excavation will be discussed on the basis of 3D FEM simulation. The authors believe that studying the parameter-sensitivity of temperature field is very important for the optimum selection of parameter values. So, in this paper, the parameter-sensitivity of temperature field is also discussed. In order to obtain the optimum frozen wall thickness, the relation between the frozen wall thickness and the initial freezing brine temperature is studied. At the end, an excavation pre-control plan is proposed by means of fuzzy logic theory for improving the excavation safety. The research result of the current paper is very helpful for projects that will be excavated by freezing construction technique.     

基于布谷鸟算法的遥感影像智能分类

提出了一种新的基于布谷鸟算法的智能式遥感分类方法。采用布谷鸟智能优化算法,自动搜索遥感影像各波段的最优阈值分割点,并定义各波段最优阈值分割点和影像分类目标类别的连线为布谷鸟的最佳解,构造以If-Then形式表达的遥感分类规则。将所提的基于布谷鸟算法的影像分类方法应用于ALOS影像分类中,并与蜂群智能遥感分类方法和See5.0决策树方法进行了对比分析。结果表明,布谷鸟智能遥感分类的总体精度和Kappa系数均比蜂群智能遥感分类和See5.0决策树方法更高,该智能遥感分类方法具有更好的分类效果。     

关于加强地质情报研究工作的建议

科技情报工作是指在广泛搜集各种情报来源的基础上,运用类比、分析、综合、判断、逻辑推理等方法,鉴别各种情报信息,洞察当前科学技术的变化和发展,研究这些变化与发展的条件、特点和意义,分析这些发展的内在规律并预测其未来发展的趋势,判断其目前所达到的水平和未来可能达到的水平。加强地质科技情报工作是当代地质工作发展的客观需要。当前,中国的地质情报工作必须坚持3个服务方向,即为领导决策服务、为科技进步服务和为生产一线服务。建议中国地质调查局组建地质情报研究中心,选择重点领域开展情报研究,力争建立地质情报研究工作的新模式。     

洪水灾害风险管理广义熵智能分析的理论框架

基于洪水灾害风险管理的背景分析,提出用广义分布函数及其广义熵理论统一描述、物理解析洪水灾害风险管理系统的各种不确定性信息。基于洪水灾害风险形成机制和风险管理理论与水利科学、信息科学、智能科学综合集成途径,提出由洪水灾害孕灾环境和致灾因子危险性广义熵智能分析、承灾体易损性广义熵智能分析、承灾体灾情广义熵智能分析和风险决策广义熵智能分析组成的洪水灾害风险管理广义熵智能分析的初步理论框架及其主要研究内容,在其它灾害风险管理中具有一定的参考应用价值。     

基于环境因子AI-GIS方法的天然滑坡危险性预测--以香港大屿山岛为例

利用人工神经元网络(AI)及地理信息系统(GIS)技术,将多元空间信息分析与非线性理论相结合,建立了基于环境因子的区域滑坡非线性预测模型.选择天然滑坡比较发育的香港大屿山岛中部作为试验研究区,定量分析了滑坡与各种环境因子的空间关系,进行了因子筛选,据此对区域天然滑坡进行了危险性分区.结果表明,该方法可获取较高的预测精度,是完全可行的.该方法的应用将为区域滑坡危险性区划及滑坡灾害的防治提供重要依据.