基于ANN的山东省可持续发展水平的区域差异

针对区域可持续发展系统的非线性,采用人工神经网络中较为先进的自组织特征映射网络构建模型.以山东省17个地市2001年的经济、人口、资源、环境、生活和科教状况作为带分样本,用自组织特征映射模型对山东省可持续发展区域差异进行了研究.研究结果表明,山东省可持续发展水平的区域差异可划分为4类,模拟结果比较理想.     

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.     

提高洪水智能预报中洪峰预报精度方法的研究

针对防洪减灾的实际需要,对如何提高智能网络对洪峰的预报精度问题进行了深入系统的研究,提出了峰值放大修正系数和遗传算法优化网络初始权重相结合的改进算法,历史资料的检验结果表明了这些改进策略的有效性和可靠性.     

人工神经网络在预测深基坑周边地表沉降变形中的应用研究

深基坑开挖引起的周边地表变形预测是一个复杂非线性问题,引起地表沉降的影响因素很多,各因素之间呈高度的非线性关系。传统的基坑用边地表沉降变形预测方法存在着一定的局限性,其预测精度有待提高,而人工神经网络是一种多元非线性动力学系统,可以灵活方便地对多成因的复杂未知系统进行高度建模,实现全面考虑各种主要影响因素的深基坑周边地表沉降变形预测。本文介绍了误差反向传播(BP)网络模型的结构、学习过程及其算法的改进,径向基函数(RBF)网络模型的结构及其学习过程;分析了影响深基坑开挖周边土体沉降变形的主要影响因素;以25个基坑工程的地表沉降实测资料为训练样本,建立了11个输入影响因素的BP神经网络模型和RBF神经网络模型,通过对样本的学习训练过程及对5个检验样本的预测精度,说明了人工神经网络用于预测基坑周边地表沉降的可行性和准确性。     

人工甜味剂环境行为研究进展

人工甜味剂是一类人工合成或半合成的蔗糖取代物,由于人们对饮食健康的关注而被越来越广泛地应用于食品和饮料中.但是大多数人工甜味剂在人体内几乎不被代谢就随尿液和粪便直接排到生活污水中.近年,几种典型的甜味剂在水体中广泛被检出且浓度较高,同时研究发现部分甜味剂难以降解,因此人工甜味剂已被视为新型有机污染物而得到关注.本文就人工甜味剂在水处理设施中的行为及其在环境来源和分布的相关研究进行了综述,总结了水处理设施和水环境中甜味剂的污染特征规律,并对今后的研究进行了展望.     

黑麦草在模拟人工湿地中对奶牛场污水高浓度氮的吸收转化

以土壤为基质,黑麦草(Lolium multiflorm)为植被,通过模拟间歇性人工湿地净化系统处理不同质量浓度的奶牛场污水试验,研究黑麦草对高质量浓度畜牧场污水中氮的吸收转化效果.结果表明,黑麦草在间歇性进水的污水质量浓度高达TN(816.8±125.1) mg·L~(-1),NH_4~+-N(443.6±97.9) mg·L~(-1),NO_3~--N(141.5±51.7) mg·L~(-1)都能较好的适应,生长良好.在不同浓度处理的净化系统中,水力停留时间为8d的条件下,NH_4~+-N的去除率为75.9%～88.3%;NO_3~--N的去除率平均为69.3%;TN的去除率74.5%～83.1%.该试验黑麦草对污水中氮的吸收转化在系统对氮净化中的贡献率平均为20.03%.     

三江源区不同建植年代人工草地群落演替与土壤养分变化

研究了了三源区不同建植期人工修复草地在不同演替阶段毒杂草[主要是甘肃马先蒿(Pedicularis kansuensis)]的入侵规律、数量特征,植物群落物种组成、生物苗和草地质最以及土壤养分、微生物活性的变化规律.结果表明,不同建植期人工修复草地植物群落的种类组成、植物功能群组成和群落数量特征存在显著差异.随着演替时间的推移,人工草地群落盖度、高度、物种数、生物最和多样性指数均表现出"V"字型变化规律,杂类草--甘肃马先蒿的数量特征变化尤为明显,在4 a的人工草地群落中开始局部入侵,在5～6 a的人工草地群落中大面积入侵,其入侵速度、入侵面积达到高峰期.土壤的含水量、容重、土壤中有机质、氮素和磷素在演替过程(7 a、9 a草地)中逐渐降低,到一定时期又逐步增加;随着演替的进行,不同建植期人工草地的土壤微牛物生物量碳和酶活性均呈"V"字型,变化.对于退化生态系统的恢复首先是植被恢复,其次是土壤肥力的恢复.土壤有机质等养分的积累、微生物活性的改善不仅能使土壤-植物复合系统的功能得以恢复,同时也能促进物种多样性的形成,有利于人工草地群落稳定性的提高.在试验区尽管植被恢复演替进行得比较缓慢,但从土壤发展的角度看,仍属进展演替.所以,在退化高寒草甸的恢复过程中,若降低和有效控制外界的干扰(如围栏封育),可为退化草地恢复提供繁殖体与土壤环境,实现人工草地逐步向恢复(正向)演替进行.图3表6参34     

人工浮床栽培7种植物在富营养化水体中的生长特性研究

本研究利用人工浮床在富营养化水体栽培了7种植物,成活率都在70%以上,2个月的生长期内生物的生长量都超过20kg鲜重/m2.浮床栽培芦苇的净化效果最好,每平方米的芦苇浮床可以使117.93 m3的水从V类水质净化为Ⅲ类水质(以总磷计),其次分别为荻、水稻、蕹菜、牛筋草、香蒲和美人蕉.对蕹菜和水稻植株的检测表明,分别作为蔬菜和青饲料完全符合国家有关卫生标准.