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.     

西昌高原鳅人工繁殖技术研究

对西昌高原鳅(Triplophysa xichangensis)人工繁殖中的催产药物及其剂量、受精方式、孵化方法和仔鱼水花培育方法等一系列关键技术进行了详细研究。结果表明,对性腺发育至Ⅳ+~Ⅳ++期的雌鱼分二次注射LRH-A2 0.1μg/g(鱼体重)和HCG 20 IU/g(鱼体重),雄鱼剂量减半,注射量0.1 mL,能很好地促进雌雄鱼发情并产卵排精,在水温19±0.5℃时的效应时间为30.5~31 h;在一个特制的塑料盆中完成了亲鱼培育、打针催产、发情、自然产卵受精、孵化以及仔鱼培育等全部人工繁殖过程,受精率、孵化率和仔鱼水花培育20 d的成活率平均分别为97.2%、85%和85%。本研究成果可应用于西昌高原鳅规模化繁育,对长江上游鱼类生物多样性保护有着积极意义。     

基于人工神经网络的气体泄爆最大超压预测研究

为解决传统经验公式在预测气体泄爆中最大超压出现时的较大偏差或过于保守的问题，提出使用人工神经网络预测气体泄爆最大超压。基于124组实验数据，采用BP与RBF神经网络，通过优化算法计算与迭代循环对泄爆样本中的影响因素进行降维与选择，并确定2类神经网络本身在学习与计算气体泄爆样本时的相关参数。结果表明:PCA（主成分分析法）在当前样本条件下的降维效果较差，而通过迭代对比确认气体泄爆样本中的5类特征全部保留时神经网络的训练模拟效果最好；通过对124组实验数据进行随机挑选训练集与测试集的训练模拟结果发现，神经网络对气体泄爆中最大超压的预测效果较好；通过对比Molkov提出的和经Fakandu等改进的NFPA 68经验公式以及2类神经网络的预测结果表明，神经网络相比于传统气体泄爆经验公式具有明显优势。     

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。     

不同雨强条件下太湖流域典型蔬菜地土壤磷素的径流特征

以太湖流域典型区域无锡市近郊区鸿声镇的蔬菜地为研究对象,采用人工模拟降雨的方法,通过野外径流小区试验,研究了不同雨强对菜地土壤磷素径流流失的影响.结果表明,初始产流时间随雨强的增大呈幂函数减小(R²=0.99),径流量在雨强较小时,缓慢上升,但随着雨强的增大急剧上升,在雨强0.83、1.17和1.67 mm·min^-1时,总磷(TP)和颗粒态磷(PP)都表现为初始流失浓度较高,随降雨历时延长略有下降,最终趋于稳定,而在大雨强2.50 mm·min^-1时,TP和PP呈现波浪式起伏,没有明显的变化趋势;在整个降雨-径流过程中,溶解态磷(DP)变化比较平缓,占TP的比例为20%～32%,而PP占TP的比例为68%～80%,其变化规律与TP相一致,由此可见,PP是土壤磷素流失的主要形态;通过对比不同雨强下不同形态磷素的流失率,发现TP的流失率,大雨强2.50 mm·min^-1是小雨强0.83 mm·min^-1的20倍,而DP的流失率,却是33倍,这表明随着雨强的增加,加速土壤PP流失的同时,也大大促进了DP的流失,主要原因是降雨前表施磷肥,使得磷肥中大量的无机态磷溶解释放到水环境中,增加了DP的流失,从而会加重受纳水体富营养化的程度.     

Anaerobic tapered fluidized bed reactor for starch wastewater treatment and modeling using multilayer perceptron neural network

Anaerobic treatability of synthetic sago wastewater was investigated in a laboratory anaerobic tapered fluidized bed reactor(ATFBR) with a mesoporous granular activated carbon(GAC)as a support material.The experimental protocol was defined to examine the effect of the maximum organic loading rate(OLR),hydraulic retention time(HRT),the efficiency of the reactor and to report on its steady- state performance.The reactor was subjected to a steady-state operation over a range of OLR up to 85.44 kg COD/(m~3.d).The COD removal efficiency was found to be 92% in the reactor while the biogas produced in the digester reached 25.38 m~3/(m~3·d) of the reactor. With the increase of OLR from 83.7 kg COD/(m~3.d),the COD removal efficiency decreased.Also an artificial neural network(ANN) model using multilayer perceptron(MLP)has been developed for a system of two input variable and five output dependent variables. For the training of the input-output data,the experimental values obtained have been used.The output parameters predicted have been found to be much closer to the corresponding experimental ones and the model was validated for 30% of the untrained data.The mean square error(MSE)was found to be only 0.0146.     

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.