新疆中低产田发展人工草地的初步研究

从新疆天然草地的利用，农业面临的问题出发，说明在绿洲内发展人工草地的必要性。针对全疆中低产田特点，将其分成7种基本类型。按类型总结出相应适生的饲草种类。通过人工种植这些饲草，达到合理利用改良耕地的目的，进而带动整个农业产业结构的调整，建立起适宜新疆农业发展的草地农业模式。     

AN EVALUATION OF THE AVAILABLE TECHNIQUES FOR ESTIMATING MISSING FECAL COLIFORM DATA1

ABSTRACT: This paper presents the findings of a study aimed at evaluating the available techniques for estimating missing fecal coliform (FC) data on a temporal basis. The techniques investigated include: linear and nonlinear regression analysis and interpolation functions, and the use of artificial neural networks (ANNs). In all, seven interpolation, two regression, and one ANN model structures were investigated. This paper also investigates the validity of a hypothesis that estimating missing FC data by developing different models using different data corresponding to different dynamics associated with different trends in the FC data may result in a better model performance. The FC data (counts/100 ml) derived from the North Fork of the Kentucky River in Kentucky were employed to calibrate and validate various models. The performance of various models was evaluated using a wide variety of standard statistical measures. The results obtained in this study are able to demonstrate that the ANNs can be preferred over the conventional techniques in estimating missing FC data in a watershed. The regression technique was not found suitable in estimating missing FC data on a temporal basis. Further, it has been found that it is possible to achieve a better model performance by first decomposing the whole data set into different categories corresponding to different dynamics and then developing separate models for separate categories rather than developing a single model for the composite data set.     

BASIN SCALE WATER MANAGEMENT AND FORECASTING USING ARTIFICIAL NEURAL NETWORKS1

ABSTRACT: Water scarcity in the Sevier River Basin in south‐central Utah has led water managers to seek advanced techniques for identifying optimal forecasting and management measures. To more efficiently use the limited quantity of water in the basin, better methods for control and forecasting are imperative. Basin scale management requires advanced forecasts of the availability of water. Information about long term water availability is important for decision making in terms of how much land to plant and what crops to grow; advanced daily predictions of streamflows and hydraulic characteristics of irrigation canals are of importance for managing water delivery and reservoir releases; and hourly forecasts of flows in tributary streams to account for diurnal fluctuations are vital to more precisely meet the day‐to‐day expectations of downstream farmers. A priori streamflow information and exogenous climate data have been used to predict future streamflows and required reservoir releases at different timescales. Data on snow water equivalent, sea surface temperatures, temperature, total solar radiation, and precipitation are fused by applying artificial neural networks to enhance long term and real time basin scale water management information. This approach has not previously been used in water resources management at the basin‐scale and could be valuable to water users in semi‐arid areas to more efficiently utilize and manage scarce water resources.     

Efficacy of created and restored nesting sites for the conservation of colonial Laridae in the South of France

By rapidly modifying key habitat components, habitat restoration is at risk of producing attractive cues for animals without providing habitats of sufficient quality. As such, individual fitness components, such as reproduction, could be reduced and restored habitats could become ecological traps. This risk notably appears by using artificial constructions in restoration projects, yet few studies have evaluated their efficacy in a robust way. We investigated this by analyzing 154 islets that were created or restored to improve the conservation status of 7 colonial Laridae species in the South of France. From 2007 to 2016, we compared occupancy dynamics and breeding parameters of these species between the restored sites and 846 unmanaged nesting sites. We also explored species’ preference for different nesting site characteristics and their respective effect on breeding parameters. Restored nesting sites were 2–9 times as attractive as unmanaged sites for all species except the Black-headed Gull (Chroicocephalus ridibundus). Colonization probability was up to 100 times higher in sites already used by other species the previous year and increased with distance to the shore until >0.2 when distance was over 250 m. Abandonment probability was 29–70% lower when breeding was successful the previous year in all species except the Sandwich Tern (Thalasseus sandvicensis). Productivity and breeding success probability were 2 times higher on managed sites. Distance from the shore was an important attractive characteristic of artificial nesting sites in all species. Other nesting site characteristics had species-specific effects on colonization, abandonment, and breeding success. Our results indicate that managed nesting sites are successful conservation tools for colonial Laridae in the Mediterranean and do not act as ecological traps. Our study showed that testing the ecological trap hypothesis is a robust way to evaluate the success of restoration projects of breeding habitats.     

罕见冬季降雨中人工增雨微物理过程分析研究

用Micaps实时资料、CPAS卫星反演分析资料、NCEP再分析资料、ECMWF再分析资料,多普勒雷达PUP产品资料,对2015年12月1日夜间至2日凌晨沈阳地区罕见冬季降雨过程,进行了天气学分析、及大气微物理人工增雨作业条件分析,并结合沈阳地区人工增雨作业指标,得出本次降雨过程在厄尔尼诺正在发生的背景下,为常见的高空槽转东北冷涡型,水汽、动力等微物理作业条件也适合于开展人工增雨作业,并给出了相应的作业指导参数,为沈阳地区今后在冬季降雨天气过程中开展人工增雨作业积累了宝贵经验.     

ABC算法优化SVR的磨损故障预测模型

目的为了提高故障预测的精度,针对支持向量回归SVR(Support vector machine for regression,SVR)参数选择困难的问题,提出一种采用人工蜂群(artificial bee colony,ABC)算法优化支持向量回归(SVR)的故障预测模型(ABC-SVR)。方法该模型先对样本数据进行重构,然后将故障预测误差(适应度)作为优化目标,通过ABC算法寻优找到最优的SVR参数,建立故障预测模型。最后通过实例仿真验证模型的优越性。结果采用ABC算法优化的SVR故障预测模型进行时间序列预测,能够较好地跟踪发动机滑油金属元素浓度的变化过程,并且能够提前2个取样时间预测异常情况的出现。结论 ABC-SVR模型有效解决了SVR参数选择难题,能够更加准确地表现故障变化规律,提高了故障预测精度。     

海州湾海洋牧场人工鱼礁投放对营养盐的影响

为了解海州湾海洋牧场人工鱼礁投放对营养盐的影响,基于2008—2015年间共24个航次水质调查资料,分析了海州湾海洋牧场鱼礁区和对照区活性磷酸盐(PO_4~(3-)-P)、活性硅酸盐(SiO_3~(2-)-Si)和溶解无机氮(DIN)的浓度、组成及营养盐结构的变化.同时,比较了鱼礁区和对照区营养盐的变化,初步分析了人工鱼礁投放量与营养盐的相关性,尝试探索人工鱼礁投放对营养盐浓度与结构的影响.结果表明,鱼礁区和对照区PO_4~(3-)-P、SiO_3~(2-)-Si浓度均呈现出秋季夏季春季的特点,且鱼礁区高于对照区.DIN在鱼礁区和对照区也呈现出同样的季节变化特点,但鱼礁区的变化幅度显著大于对照区.海洋牧场人工鱼礁的投放使鱼礁区PO_4~(3-)-P和DIN浓度增加.受热力学平衡的影响,对照区DIN浓度也出现了增加,而PO_4~(3-)-P受人工鱼礁投放的影响小.鱼礁区和对照区SiO_3~(2-)-Si受人工鱼礁投放影响均不明显.另外,海洋牧场DIN组成也发生了改变,2012年前NO_3~--N为DIN主要组成,之后为NH_4~+-N.营养盐结构分析显示,海州湾海洋牧场营养盐对浮游植物主要表现为磷限制.     

基于神经网络的洪水预报研究

人工神经网络通过神经元之间的相互作用来完成整个网络的信息处理,具有自学习和自适应等一系列优点,因而用它来进行洪水预报是可行的.对洪水预报问题,初步建立了基于神经网络的洪水预报系统,给出了应用实例.     

黄河三角洲人工草场群落土壤盐分动态集成预报模型及其应用

提出了以ＧＭ（１,１）模型拟合发展的趋势、傅里叶变换撮周期分量ＡＲ（ｐ）模型模拟随机过程的集成预报模型,并用于黄河三角洲人工草场群落土壤盐分的定量研究中,经理论和应用检验证明：该模型能以较高的精度模拟或预报土壤盐分在较长时期内的动态变化过程,方法简单,计算工作量小,并优于传统的单一预报模型。     

毛竹林各组分能量估算模型的研究

在建瓯设置40块毛竹林标准地,分别测定了毛竹单株各部分干重与能量,建立了各部分生物量模型,并在此基础上,运用人工神经网络方法对毛竹林各组分能量进行估测.结果表明毛竹林各组分秆、枝叶和地下部分的平均能量依次为4.23225×10