特大火灾定量与定性风险管理技术应用探讨

定量化风险管理技术有其无可比拟的优势，但无法对风险因素中的人为因素进行管理。本文通过分析量化管理的缺陷和定性化管理的优势，提出应根据具体情况，采用定量化与定性化管理相结合的管理方式，并提出定性化管理的具体方法。     

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.     

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.     

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。     

模拟降雨径流作用下红壤坡面侵蚀水动力学机制

土壤侵蚀过程受控于侵蚀外营力和土壤抗侵蚀性能,深入理解坡面流水动力学特性及其侵蚀动力是研究土壤侵蚀动力学机制的基础。利用可变坡土槽,通过不同雨强(60、90和120 mm/h)和径流冲刷(10、15和20 L/min)组合模拟试验,研究了第四纪黏土红壤坡面水流的水动力学特征参数及其与土壤侵蚀量间的关系。结果显示:降雨和径流冲刷影响了坡面产流产沙过程和坡面流水力学特性,其中平均流速v、平均水深h、雷诺数Re和水流功率ω均随降雨强度和上方来水流量的增加而增大,相对水深曼宁糙率n/h则减小,其水力学他参数(弗如德数Fr、阻力系数f和水流剪切力τ)变化规律不明显。坡面水流平均速度取值范围为0.21~0.45 m/s,平均水深取值范围为5.6~9.4 mm,在试验条件下红壤坡面侵蚀水流流态大部分均处于"紊流-急流区"。不论从径流角度看或是从泥沙角度分析,由相对水深和曼宁糙率系数两种水动力因子共同组成的复合水动力特征参数-相对水深曼宁糙率,是表征不同上方来水流量和降雨强度条件下第四纪粘土红壤坡面侵蚀特征的水动力参数。     

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。     

TRMM 3B42卫星降水数据在赣江流域径流模拟中的应用

以赣江流域为研究区,基于观测降水和TRMM准实时数据(3B42RTV6、3B42RTV7)和分析数据(3B42V6、3B42V7),驱动VIC水文模型,开展卫星降水产品在赣江流域的水文模拟,评估TRMM降水产品在水文模拟中的应用能力。结果表明:(1)在赣江流域,3B42V7估算的降水与实测降水的对比结果最好,3B42RTV6的估算精度最低,3B42RTV7较3B42RTV6在赣江流域的降水估算精度提升非常明显;(2)在径流模拟方面,3B42V6和3B42V7在日尺度上尽管对洪峰的模拟有所偏差,但模拟结果仍能反映径流变化特征,在月尺度上模拟结果精度较高,纳什系数均在0.9以上,并且二者在4、5月的径流模拟结果较好,7、8月的模拟结果较差,而3B42RTV6对径流的模拟能力较差,日径流量和月径流量均呈现明显低估,3B42RTV7对径流的模拟结果比3B42RTV6有明显改善,可以满足实时水文预报的需求。