基于模型试验的泥石流坡面物源启动预警模型

为进一步探明银洞子沟物源区坡面物源启动机理与降雨的相关性,在进行详细的现场考察与工程勘察工作后选取了典型模型体,并采用20余组人工降雨物理试验方法将降雨强度与坡度设为控制变量,研究了4种坡形、5种雨强条件下,坡面松散物源的失稳机制及破坏模式。试验结果揭示了地表变形与地下物理力学参数变化的定量响应关系,并基于试验发生破坏的临界雨量,建立了银洞子沟传统I-D预警模型。之后提炼可靠预警参数(坡度、深部体积含水率),通过数学回归分析方法并采用Exponential模型,得出IGD、IGM多参数新型预警判别式,实现了传统I-D模型的有效修正,并具一定的可靠性和实用价值。     

测井地质学前世、今生与未来——写在《测井地质学·第二版》出版之时

测井地质学以地质学和测井学的方法理论为指导,综合运用各种测井信息,来解决基础地质和石油地质的地质问题。经过数十年发展,测井地质学在油气勘探开发各个环节得到广泛应用。非常规油气的兴起使得测井地质学正面临多重挑战和全新探索,亟需建立针对非常规油气的测井地质学综合方法理论体系。本文以《测井地质学·第二版》出版为契机,系统归纳了测井地质学的起源及发展历程、主要研究内容和研究方法流程。然后总结了测井资料与地震、地质信息的匹配性,并分析了不同探测特性测井方法纵向分辨率区间特征。在此基础上评述了测井地质学在井旁构造解析、沉积学特征研究、层序地层划分、地应力方向判别及大小计算、井壁裂缝识别与评价、烃源岩评价以及非常规油气“七性关系”综合评价当中的应用。但由于测井资料的负载能力有限性、测井与地质信息属性不对应性以及测井资料本身的多解性,使得测井地质学在测井—地质转换、非常规油气测井评价及其与人工智能融合方面还存在一定的问题。因此加强基础岩石物理研究,挖掘测井曲线中包含的地质信息,并与人工智能相结合,将拓展测井地质学研究的精度和广度,从而使其未来可更好地应用至非常规油气测井评价等实践工作中。     

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。     

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.     

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。