微波技术在环境保护领域的应用

综述了当前微波技术在环境保护领域的应用和研究状况，着重介绍了微波技术在废气处理，废水处理，固体废弃物处理，环境监测等方面的应用，讨论了应用存在的一些问题，并展望了微波技术在环境保护领域的应用前景。     

基于神经网络的温度预测

室内温度与诸多影响因素之间的非线性、复杂性等关系 ,给建模、预测带来了难度 ,引入了人工神经网络 ;利用人工神经网络的非线性、并行计算和自学习特性进行建模 ,实现了对温度模拟     

京沪高速铁路地震预警系统的方案及关键参数研究

京沪高速铁路及沿线区域跨越 4个主要的地震带 ,带内地震活动活跃 ,地震是对高速列车行车安全危害最大的自然灾害。在对京沪高速铁路及沿线区域地震危险性调查的基础上 ,对地震预警系统的构成、监测设备的设置方案及报警模式等一些关键问题进行了研究。通过对京沪高速铁路各类典型构筑物的大量的地震响应统计分析 ,给出了机械式地震仪的预警水平 ,并提出了适于P波检测的M -R判别标准 ,为京沪高速铁路安全监控系统中地震预警系统的建设提供理论依据。     

提高交通安全——实现智能汽车的研究

主要介绍了通过智能汽车的研究 ,提高城市交通安全的必要性 ;综述了发达国家关于智能汽车研究的现状与趋势 ,并通过国外智能汽车发展的趋势 ;进一步论证智能汽车的研究是汽车工业发展的方向 ,也是提高交通安全的重要手段之一。此外 ,论文提出利用模式识别技术的智能汽车的结构框架 ;指出神经网络方法应用与模式识别技术结合的优势。最后 ,论文在阐述我国需要发展智能汽车的同时 ,提出应结合中国国情相应研究相关理论 ,为今后的实施奠定基础的近期目标 ,并提出展望     

滨海平原咸水入侵动态监测网构建研究——以潍河下游地区为例

作为进行咸水入侵动态监测的基础性工作，对潍河下游地区的供需水平衡及地下水位、水质和地层电阻率在咸淡水界面上变化规律进行了研究分析；同时，探索了监测工作的地理工程技术途径，并据此构建了3个层次的监测体系：（1）宏观区域供需水平衡分析与地下水位负值区变化监测，（2）中观咸水入侵发展变化的物探监测，（3）微观地下水位、水质变化监测；最后，为了对日益增多的咸水入侵动态数据资料进行有效的管理，设计了一个基于GIS的、以数据库管理应用为核心的信息系统。     

SOIL SALINITY PREDICTION USING ARTIFICIAL NEURAL NETWORKS1

ABSTRACT: This study explores the applicability of Artificial Neural Networks (ANNs) for predicting salt build‐up in the crop root zone. ANN models were developed with salinity data from field lysimeters subirrigated with brackish water. Different ANN architectures were explored by varying the number of processing elements (PEs) (from 1 to 30) for replicate data from a 0.4 m water table, 0.8 m water table, and both 0.4 and 0.8 m water table lysimeters. Different ANN models were developed by using individual replicate treatment values as well as the mean value for each treatment. For replicate data, the models with twenty, seven, and six PEs were found to be the best for the water tables at 0.4 m, 0.8 m and both water tables combined, respectively. The correlation coefficients between observed salinity and ANN predicted salinity of the test data with these models were 0.89, 0.91, and 0.89, respectively. The performance of the ANNs developed using mean salinity values of the replicates was found to be similar to those with replicate data. Not only was there agreement between observed and ANN predicted salinity values, the results clearly indicated the potential use of ANN models for predicting salt build‐up in soil profile at a specific site.     

FLOOD STAGE FORECASTING WITH SUPPORT VECTOR MACHINES1

ABSTRACT: Machine learning techniques are finding more and more applications in the field of forecasting. A novel regression technique, called Support Vector Machine (SVM), based on the statistical learning theory is explored in this study. SVM is based on the principle of Structural Risk Minimization as opposed to the principle of Empirical Risk Minimization espoused by conventional regression techniques. The flood data at Dhaka, Bangladesh, are used in this study to demonstrate the forecasting capabilities of SVM. The result is compared with that of Artificial Neural Network (ANN) based model for one‐lead day to seven‐lead day forecasting. The improvements in maximum predicted water level errors by SVM over ANN for four‐lead day to seven‐lead day are 9.6 cm, 22.6 cm, 4.9 cm and 15.7 cm, respectively. The result shows that the prediction accuracy of SVM is at least as good as and in some cases (particularly at higher lead days) actually better than that of ANN, yet it offers advantages over many of the limitations of ANN, for example in arriving at ANN's optimal network architecture and choosing useful training set. Thus, SVM appears to be a very promising prediction tool.     

A RIFFLE STABILITY INDEX TO EVALUATE SEDIMENT LOADING TO STREAMS1

ABSTRACT: Riffles in moderately entrenched stream reaches with gradients of 2 percent to 4 percent that have received excessive sediment from upstream have a distinctly different and higher proportion of smaller mobile particles than riffles in systems that are in dynamic equilibrium. The mobile fraction on the riffle can be estimated by comparing the relative abundance of various particle sizes present on the riffle with the dominant large particles on an adjacent bar. Riffle particles smaller than the dominant large particles on the bar are interpreted as mobile. The mobile percentile of particles on the riffle is termed “Riffle Stability Index” (RSI) and provides a useful estimate of the degree of increased sediment supply to riffles in mountain streams. The RSI addresses situations in which increases in gravel bedload from headwaters activities is depositing material on riffles and filling pools, and it reflects qualitative differences between reference and managed watersheds. The RSI correlates well with other measures of stream channel physical condition, such as V and the results of fish habitat surveys. Thus, it can be used as an indicator of stream reach and watershed condition and also of aquatic habitat quality.     

论代表性的涵义及工业废水重点源采样方案的设计原则

数据代表性是指监测结果在表征被监测对象特性时所能达到的真实程度;样品代表性是指局部体现整体时所能达到的真实程度。加密监测是重点原调查的质量基础,不能简化行事。监督性监测中,通过测定流量比例混合样来获取总量控制所必需的漉量加权平均值浓度数据,并以此来减小工作量。为满足浓度控制需要,时间—浓度确定型废水的峰浓度样应单独测定。     

智能型地理信息系统（AIGIS）在城市抗震防灾规划中的应用

本文简要介绍地理信息系统GIS的发展概况、主要功能以及在国内外的应用现状.对现有的GIS软件技术加以改进,引进人工神经元网络和模糊综合评判技术,发展了一种智能型的GIS,在我国若干城市的抗震设防区划工作中应用,取得良好的效果。