气候变化与人类活动对石家庄市藁城区地下水位埋深的影响分析

为了探寻石家庄市藁城区地下水埋深动态变化规律,以藁城区2001—2018年的年降水量、地下水人工开采量等数据为基础,对藁城区地下水位埋深进行研究。首先采用P-Ⅲ型曲线法确定降水序列的丰、平、枯年份,分析不同降水量情况下地下水位埋深变化规律;其次,利用地下水开采潜力系数法和灰色关联度法对人工开采量和地下水位埋深的关系进行研究。结果表明：1）藁城区地下水位埋深在2001—2016年逐渐增大,在2016—2018年趋于减小,2016年为转折点;在空间上藁城区地下水位埋深呈现出北部埋深小、南部埋深大的特征,北部水位埋深较同期南部水位埋深要浅5~10 m。2）降水是驱动藁城区地下水位埋深变化的重要因素,枯水年水位埋深变幅在0.8~1.5 m之间,平水年水位埋深变幅在0.3~1.2 m之间,丰水年水位埋深变幅在0.3~1.1 m之间。主灌期（3—6月）的地下水位埋深增加速率均为cm/d级,非主灌期（7—10月）的地下水位埋深减少速率均为mm/d级。3）人工开采是驱动藁城区地下水位埋深变化的主导因素,其中农业开采量占人工开采量的80%。综上认为,藁城区一直处于严重超采状态,地下水累计超采量每增加1亿m³,地下水位埋深增加0.45 m。     

人工智能在冰雹识别及临近预报中的初步应用

基于广东10部S波段多普勒天气雷达的三维拼图资料,利用机器学习技术开发了一种冰雹识别和临近预报的人工智能算法。算法设计时以雷达回波反射率的垂直和水平扫描数据为基础训练集,将冰雹云的雷达反射率扫描数据作为正样本,将其他雷达反射率扫描数据作为负样本,通过贝叶斯分类法对正、负样本数据集进行机器学习,训练人工智能识别冰雹云内在规律的能力。训练时以广东省2008-2013和2015-2016年的数据作为训练集,使用了2014年广东省12次冰雹过程的数据做检验。对比检验的结果表明,人工智能法比传统的概念模型法击中率高9个百分点。研究结果表明了人工智能对冰雹这类非线性强天气过程具有较强的识别能力。      

基于图像处理的自动野外救援系统设计

针对野外救援的不及时性,结合人工智能等技术的应用,提出了一种新的自动野外救援系统.人类在野外和海上活动中受困后,除了依靠自身能力和装备进行求生之外,外部的有效及时救援是提高生存几率的关键.自动野外救援系统,由自动报警装置、手动报警装置实现报警,触发整个系统的运行.自动救援装置可以通过定位模块实现定位,通过执行器移动到随身装置附近实现救援,随身装置还可以发出信标导引信号;自动救援装置还可以通过传感器检测信标,通过相位检测技术等计算待救者的坐标并规划路径,实现更精准的定位与救援.     

Impact of artificial subsurface drainage on groundwater travel times and baseflow discharge in an agricultural watershed,Iowa (USA)

Pollutant delivery through artificial subsurface drainage networks to streams is an important transport mechanism, yet the impact of drainage tiles on groundwater hydrology at the watershed scale has not been well documented. In this study, we developed a two‐dimensional, steady‐state groundwater flow model for a representative Iowa agricultural watershed to simulate the impact of tile drainage density and incision depth on groundwater travel times and proportion of baseflow contributed by tile drains. Varying tile drainage density from 0 to 0.0038 m^?1, while maintaining a constant tile incision depth at 1.2 m, resulted in the mean groundwater travel time to decrease exponentially from 40 years to 19 years and increased the tile contribution to baseflow from 0% to an upper bound of 37%. In contrast, varying tile depths from 0.3 to 2.7 m, while maintaining a constant tile drainage density of 0.0038 m^?1, caused mean travel times to decrease linearly from 22 to 18 years and increased the tile contribution to baseflow from 30% to 54% in a near‐linear manner. The decrease in the mean travel time was attributed to decrease in the saturated thickness of the aquifer with increasing drainage density and incision depth. Study results indicate that tile drainage affects fundamental watershed characteristics and should be taken into consideration when evaluating water and nitrate export from agricultural regions. Copyright © 2011 John Wiley & Sons, Ltd.     

马氏珠母贝种苗规模化繁育新技术及工艺

对马氏珠母贝人工育苗换水、投附着基和饵料等关键环节进行了研究。结果表明:(1)不换水组的D形幼虫及壳顶幼虫的存活率,稚贝育成率以及D形幼虫、壳顶幼虫及稚贝壳长日生长率比换水组分别提高了15.3%、259.6%、186.5%、33.3%、34.2%、12.4%,且差异显著;(2)第1、2次投附着板组的稚贝壳长日生长率均比一次性投附着板组快,第3次投附着板组的壳长日生长率比其他所有组均慢,且差异均显著。多次投附着板组的同一批次稚贝均匀度均比一次性投附着板组好,且多次投附着板组比一次性投附着板组的稚贝育成率提高了32.5%,稚贝存活率提高了19.3%,采苗量提高了35%;(3)投喂虾塘水组稚贝存活率、育成率及壳长日生长率比投喂50%自溶酵母+50%小球藻组分别提高了28.1%、47.2%、35.9%,而投喂这两种不同饵料的稚贝阴干后的存活率差异不显著。研究表明,通过封闭不换水育苗、多次投附着板及投喂虾塘水中的生态饵料的方法可以高效地培育出健康的马氏珠母贝种苗。     

Are artificial neural network techniques relevant for the estimation of longitudinal dispersion coefficient in rivers? / Les techniques de réseaux de neurones artificiels sont-elles pertinentes pour estimer le coefficient de dispersion longitudinale en rivières?

Abstract

Abstract Accurate application of the longitudinal dispersion model requires that specially designed experimental studies are performed in the river reach under consideration. Such studies are usually very expensive, so in order to quantify the longitudinal dispersion coefficient, as an alternative approach, various researchers have proposed numerous empirical formulae based on hydraulic and morphometric characteristics. The results are presented of the application of artificial neural networks as a parameter estimation technique. Five different cases were considered with the network trained for different arrangements of input nodes, such as channel depth, channel width, cross-sectionally averaged water velocity, shear velocity and sinuosity index. In the case where the sinuosity index is included as an input node, the results turned out to be better than those presented by other authors.     

Neuro-fuzzy models employing wavelet analysis for suspended sediment concentration prediction in rivers

Abstract

The study of sediment load is important for its implications to the environment and water resources engineering. Four models were considered in the study of suspended sediment concentration prediction: artificial neural networks (ANNs), neuro-fuzzy model (NF), conjunction of wavelet analysis and neuro-fuzzy (WNF) model, and the conventional sediment rating curve (SRC) method. Using data from a US Geological Survey gauging station, the suspended sediment concentration predicted by the WNF model was in satisfactory agreement with the measured data. Also the proposed WNF model generated reasonable predictions for the extreme values. The cumulative suspended sediment load estimated by this model was much higher than that predicted by the other models, and is close to the observed data. However, in the current modelling, the ANN, NF and SRC models underestimated sediment load. The WNF model was successful in reproducing the hysteresis phenomenon, but the SRC method was not able to model this behaviour. In general, the results showed that the NF model performed better than the ANN and SRC models.

Citation Mirbagheri, S. A., Nourani, V., Rajaee, T. & Alikhani, A. (2010) Neuro-fuzzy models employing wavelet analysis for suspended sediment concentration prediction in rivers. Hydrol. Sci. J. 55(7), 1175–1189.     

Modelling combined open channel flow by artificial neural networks

Combined open channel flow is encountered in many hydraulic engineering structures and processes, such as irrigation ditches and wastewater treatment facilities. Extensive experimental studies have conducted to investigate combined flow characteristics. Nevertheless, there is no simple relationship that can fully describe the velocity profiles in a turbulent flow. The artificial neural network (ANN) has great computational capability for solving various complex problems, such as function approximation. The main objective of this study is to evaluate the applicability of the ANN for simulating velocity profiles, velocity contours and estimating the discharges accordingly. The velocity profiles measured by an acoustic doppler velocimeter in the open channel of the Chihtan purification plant, Taipei, with different discharges at fixed measuring section and different depths are presented. The total number of data sets is 640 and the data sets are split into two subsets, i.e. training and validation sets. The backpropagation algorithm is used to construct the neural network. The results demonstrate that the velocity profiles can be modelled by the ANN, and the ANN constructed can nicely fit the velocity profiles and can precisely predict the discharges for the conditions investigated. Copyright © 2005 John Wiley & Sons, Ltd.     

铁路工程地质选线咨询系统

本文考虑了铁路线位选择的特点，从地质的角度出发，将铁路地质选线中涉及的主要问题（滑坡、泥石流、风沙、岩溶）归纳、总结建立知识库，通过推理为铁路总体选线提供地质依据，以减少工程失误，节约工程造价，保证工期。本文重点介绍该系统的组成部分及各个部分的具本设计思想和设计技巧。     

矿物分类和识别的人工神经网络模型

本文提出矿物分类和识别的人工神经网络模型，并选取一组标样－我国沉积碳酸盐型锰矿中菱锰矿作为研究对象，识别效率达１００％。结果表明，该模型性能良好，可望成为矿物识别的有效手段。