Simultaneous determination of three herbicides by differential pulse voltammetry and chemometrics

A novel differential pulse voltammetry method (DPV) was researched and developed for the simultaneous determination of Pendimethalin, Dinoseb and sodium 5-nitroguaiacolate (5NG) with the aid of chemometrics. The voltammograms of these three compounds overlapped significantly, and to facilitate the simultaneous determination of the three analytes, chemometrics methods were applied. These included classical least squares (CLS), principal component regression (PCR), partial least squares (PLS) and radial basis function-artificial neural networks (RBF-ANN). A separately prepared verification data set was used to confirm the calibrations, which were built from the original and first derivative data matrices of the voltammograms. On the basis relative prediction errors and recoveries of the analytes, the RBF-ANN and the DPLS (D – first derivative spectra) models performed best and are particularly recommended for application. The DPLS calibration model was applied satisfactorily for the prediction of the three analytes from market vegetables and lake water samples.     

GIS网络服务区分析在重庆市主城区夜间施工噪声投诉处置中应用

通过对重庆市主城区夜间施工噪声投诉处置现状分析,以主城区道路交通网络、环境监察机构分布等数据专题建模,采用G IS的网络服务区功能的分析结果,提出车辆调度合理化建议方案,为环境监察部门的夜间施工噪声投诉处置车辆部署调度提供辅助技术支持。     

JUSTIFICATION FOR A REDUCTION IN THE CREST-STAGE GAGE PROGRAM IN LOUISIANA1

The crest-stage gage program in Louisiana was evaluated to determine if the data were adequate for use in developing regional flood-frequency equations and to determine if any crest-stage gage stations could be discontinued. An abundance of data at many crest-stage gage stations and a lack of data for urban areas and flat-slope areas indicated a need for a shift in the number, type, and locations of gages. Correlations and comparisons of annual peak discharges and watershed characteristics of 96 existing stations resulted in the elimination of 72 stations and the addition of one new station, reducing the total network to 25 stations that could be used for future flood-frequency analyses. The adequacy of the reduced network for development and verification of regional flood-frequency equations was tested by comparing a set of regional flood-frequency equations developed using data from the full network with a set developed using data from the reduced network. The results indicate that the crest-stage gage network can be reduced to 25 stations and still provide adequate information for future flood-frequency analyses.     

Large-scale constructed wetlands for nutrient removal from stormwater runoff: An everglades restoration project

The South Florida Water Management District (SFWMD) constructed a wetland south of Lake Okeechobee to begin the process of removing nutrients (especially phosphorus) from agricultural stormwater runoff entering the Everglades. The project, called the Everglades Nutrient Removal (ENR) project, is a prototype for larger, similarly constructed wetlands that the SFWMD will build as part of the Everglades restoration program. This innovative project is believed to be one of the largest agricultural stormwater cleanup projects in the United States, if not in the world. This publication describes the ENR project's design, construction, and proposed operation, as well as the proposed research program to be implemented over the next few years.     

APPLICATION OF GREY MODEL AND ARTIFICIAL NEURAL NETWORKS TO FLOOD FORECASTING1

The main focus of this study was to compare the Grey model and several artificial neural network (ANN) models for real time flood forecasting, including a comparison of the models for various lead times (ranging from one to six hours). For hydrological applications, the Grey model has the advantage that it can easily be used in forecasting without assuming that forecast storm events exhibit the same stochastic characteristics as the storm events themselves. The major advantage of an ANN in rainfall‐runoff modeling is that there is no requirement for any prior assumptions regarding the processes involved. The Grey model and three ANN models were applied to a 2,509 km² watershed in the Republic of Korea to compare the results for real time flood forecasting with from one to six hours of lead time. The fifth‐order Grey model and the ANN models with the optimal network architectures, represented by ANN1004 (34 input nodes, 21 hidden nodes, and 1 output node), ANN1010 (40 input nodes, 25 hidden nodes, and 1 output node), and ANN1004T (14 input nodes, 21 hidden nodes, and 1 output node), were adopted to evaluate the effects of time lags and differences between area mean and point rainfall. The Grey model and the ANN models, which provided reliable forecasts with one to six hours of lead time, were calibrated and their datasets validated. The results showed that the Grey model and the ANN1010 model achieved the highest level of performance in forecasting runoff for one to six lead hours. The ANN model architectures (ANN1004 and ANN1010) that used point rainfall data performed better than the model that used mean rainfall data (ANN1004T) in the real time forecasting. The selected models thus appear to be a useful tool for flood forecasting in Korea.     

计算机局域网络保密措施

随着计算机的普及 ,局域网络越来越广为利用 ,但也出现了相应的失密问题。本文从信息及实体两个方面 ,论述了局域网络的保密措施。     

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     

煤体注水湿润半径预测的神经网络模型

合理的注水半径一直是煤体注水防尘技术中难以确定的参数。笔者基于对影响煤体注水半径因素的分析和神经网络理论的原理之上 ,设计网络模型为 3层 ,输入层为 7个节点 ,应用BP网络算法 ,建立了煤体注水湿润半径的预测模型 ,并对其参数进行了讨论。然后 ,用平顶山矿务局和水城矿务局 13个矿 19个回采工作面的统计资料对BP网络进行自适应学习 ,并取η =0 .9,α =0 .82 ,控制网络总误差E≤ 10 6。经过 2 12 34次迭代后 ,网络趋于稳定。用训练好的网络对平顶山矿务局的某矿的 3层煤的注水湿润半径进行预测 ,预测结果与实测值很接近。其误差分别为 0 .5 %,0 .6 %和 0 .7%。     

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.