我国南方冰雪灾害的特征与城市救灾对策研究

在对2008年初中国南方所发生的冰雪灾害的损失、影响和成因进行分析的基础上,指出该次灾害具有受灾范围的全面性、灾害过程的发展性和救灾行动的艰难性等特征,阐明冰雪灾害在城市会造成停电停水、交通瘫痪和通讯中断等严重灾情,进而引发断油断粮、物价飞涨和人心恐慌等综合性灾害。通过冰雪灾害对城市救灾行动的总结,指出冰雪灾害给城市政府的深刻启示在于城市规模的急速扩张必须同时加强防灾救灾的软硬件建设,并提出城市必须采取持久开展防灾救灾知识宜传教育、大力加强防灾救灾基础设施建设、科学制订各类灾害的专业性应急预案和努力构建协同联动救灾机制等对策,有效提升城市应对灾害的能力,减少灾害造成的损失。     

Reflections on the use of Bayesian belief networks for adaptive management

A broad range of tools are available for integrated water resource management (IWRM). In the EU research project NeWater, a hypothesis exists that IWRM cannot be realised unless current management regimes undergo a transition toward adaptive management (AM). This includes a structured process of learning, dealing with complexity, uncertainty etc. We assume that it is no longer enough for managers and tool researchers to understand the complexity and uncertainty of the outer natural system-the environment. It is just as important, to understand what goes on in the complex and uncertain participatory processes between the water managers, different stakeholders, authorities and researchers when a specific tool and process is used for environmental management. The paper revisits a case study carried out 2001-2004 where the tool Bayesian networks (BNs) was tested for groundwater management with full stakeholder involvement. With the participation of two researchers (the authors) and two water managers previously involved in the case study, a qualitative interview was prepared and carried out in June 2006. The aim of this ex-post evaluation was to capture and explore the water managers' experience with Bayesian belief networks when used for integrated and adaptive water management and provide a narrative approach for tool enhancement.     

基于BP模型对城市交通噪声的数据处理和预测

城市交通噪声的预测和评价技术是城市交通可持续发展的重要研究内容,直接为城市交通规划中环境容量分析和环境影响评价服务。本文通过实测的大量数据,运用神经网络中的BP模型及其算法建立车辆数、道路宽度和交通噪声之间的关系,对城市道路交通噪声数据进行处理和预测。     

基于MSPA与MCR模型的余江县生态网络构建基于MSPA与MCR模型的余江县生态网络构建

在我国社会经济快速发展的背景下,城市化进程的不断推进导致城市生态斑块日益减少,生态环境问题正在逐年增加,构建生态网络是改善城市生态环境与服务功能的有效方法之一。以余江县为研究区,采用形态学空间格局分析(MSPA)方法和景观指数法,提取对生态网络具有重要意义的生态源地,并基于MCR模型构建综合阻力面,采用最小成本路径方法生成潜在廊道,再基于重力模型、中介中心度等对关键廊道及踏脚石斑块进行识别并提取,从而构建研究区潜在生态网络。结果表明：MSPA方法能够与MCR模型有机的结合,通过定量的分析识别出研究区潜在生态廊道,并根据斑块的中介作用选定踏脚石斑块,明确研究区景观要素的保护优先度,将景观中的潜在生态源地及廊道作为生态网络构建的主要依据,能够更加科学地为余江县生态网络的构建提供指导,同时也可为其他区域提供参考。     

Comparison of the Performance of Statistical Models in Forecasting Monthly Total Dissolved Solids in the Rio Grande1

Abudu, S., J.P. King, Z. Sheng, 2011. Comparison of the Performance of Statistical Models in Forecasting Monthly Total Dissolved Solids in the Rio Grande. Journal of the American Water Resources Association (JAWRA) 48(1): 10‐23. DOI: 10.1111/j.1752‐1688.2011.00587.x Abstract: This paper presents the application of autoregressive integrated moving average (ARIMA), transfer function‐noise (TFN), and artificial neural networks (ANNs) modeling approaches in forecasting monthly total dissolved solids (TDS) of water in the Rio Grande at El Paso, Texas. Predictability analysis was performed between the precipitation, temperature, streamflow rates at the site, releases from upstream reservoirs, and monthly TDS using cross‐correlation statistical tests. The chi‐square test results indicated that the average monthly temperature and precipitation did not show significant predictability on monthly TDS series. The performances of one‐ to three‐month‐ahead model forecasts for the testing period of 1984‐1994 showed that the TFN model that incorporated the streamflow rates at the site and Caballo Reservoir release improved monthly TDS forecasts slightly better than the ARIMA models. Except for one‐month‐ahead forecasts, the ANN models using the streamflow rates at the site as inputs resulted in no significant improvements over the TFN models at two‐month‐ahead and three‐month‐ahead forecasts. For three‐month‐ahead forecasts, the simple ARIMA showed similar performance compared to all other models. The results of this study suggested that simple deseasonalized ARIMA models could be used in one‐ to three‐month‐ahead TDS forecasting at the study site with a simple, explicit model structure and similar model performance as the TFN and ANN models for better water management in the Basin.     

The relationships between air pollutants, meteorological parameters and concentration of airborne fungal spores

Fungal spores are an important component of bioaerosol and also considered to act as indicator of the level of atmospheric bio-pollution. Therefore, better understanding of these phenomena demands a detailed survey of airborne particles.The objective of this study was to examine the dependence of two the most important allergenic taxa of airborne fungi - Alternaria and Cladosporium - on meteorological parameters and air pollutant concentrations during three consecutive years (2006-2008). This study is also an attempt to create artificial neural network (ANN) forecasting models useful in the prediction of aeroallergen abundance.There were statistically significant relationships between spore concentration and environmental parameters as well as pollutants, confirmed by the Spearman’s correlation rank analysis and high performance of the ANN models obtained. The concentrations of Cladosporium and Alternaria spores can be predicted with quite good accuracy from meteorological conditions and air pollution recorded three days earlier.     

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.     

PREDICTING MEASUREMENT ERROR OF AREAL MEAN PRECIPITATION DURING EXTREME EVENTS1

ABSTRACT: The areal mean precipitation (AMP) over a catchment is normally calculated using point measurements at rainfall gages. Error in AMP estimates occurs when an insufficient number of gages are used to sample precipitation which is highly variable in space. AMP error is investigated using historic, severe rainfalls with a set of hypothetical catchments and raingage networks. The potential magnitude of error is estimated for typical gage network densities and arrangements. Possible sources of error are evaluated, and a method is proposed for predicting the magnitude of error using data that are commonly available for severe, historic rainfall.     

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.