自然灾害灾情划分指标研究

本文阐述了灾情分析中的两个基本方面，即损失的规模和损失的深度，提出了灾级和灾度的概念，对于影响灾级和灾度的主要指标也进行了分析。并就时间和空间变化对灾级划分界线的影响作了论述。     

数据库语言在震害预测中的应用

震害预测的软件程序一般都是基于结构分析的原理，大都采用ＦＯＲＴＲＡＮ语言来编写，而震害预测工作中又大量用到分组和排序，这又是数据库语言的特长。本文介绍了在震害预测工作中应用数据库语言的一些体会，主要讨论了两种语言的接口问题。     

CALCULATION OF NONLINEAR CONFIDENCE AND PREDICTION INTERVALS FOR GROUND-WATER FLOW MODELS1

ABSTRACT: A method is derived to efficiently compute nonlinear confidence and prediction intervals on any function of parameters derived as output from a mathematical model of a physical system. The method is applied to the problem of obtaining confidence and prediction intervals for manually-calibrated ground-water flow models. To obtain confidence and prediction intervals resulting from uncertainties in parameters, the calibrated model and information on extreme ranges and ordering of the model parameters within one or more independent groups are required. If random errors in the dependent variable are present in addition to uncertainties in parameters, then calculation of prediction intervals also requires information on the extreme range of error expected. A simple Monte Carlo method is used to compute the quantiles necessary to establish probability levels for the confidence and prediction intervals. Application of the method to a hypothetical example showed that includsion of random errors in the dependent variable in addition to uncertainties in parameters can considerably widen the prediction intervals.     

RUNOFF PREDICTION USING REMOTE SENSING IMAGERY1

ABSTRACT: Percent imperviousness is an important parameter in modeling the urban rainfall-runoff process and is usually determined using manual methods such as random sampling or conventional accounting methods. In this study two computerized methods are used for estimating the percent imperviousness of urban watersheds using high altitude remote sensing imagery. These methods include the Laser Image Processing Scanner and the Video-Tape Camera system. Imperviousness is directly estimated in the former method while in the latter it is estimated as a function of the statistics of the responses on emulsions of the imagery. The percent imperviousness computed by utilizing remote sensing imagery was used with the conceptual models of rainfall-runoff models. The models were applied to four urban watersheds and the runoff prediction results indicate that imperviousness determined by using remote sensing imagery was as accurate as that obtained by the manual methods, and that the use of remote sensing imagery requires significantly less time and money.     

CN算法应用于我国23次强震的预测研究

本文用CN算法研究了我国近期大陆东部和西部南北带及邻近区域共23次强震前的TIP,即强震发生概率增长时间。结果表明,18次强震发生在被判定的强震发生概率增长时间的TIP内。TIP警戒约占总研究时空域的30％,获得了较好的中期地震预测效果。表明CN算法可作为强震中期预测的手段之一。     

灾度指数及其意义

本文分析了现行支度判别方法的不足，指出评价灾度时必须充分考虑承灾体对灾害的反应情况，并且根据判别准则的可比性原则，提出了灾度指数的概念，并以此做为灾度评价因子，旨在促进灾度评估工作的定量化、科学化，为防灾减灾服务。     

由三个地震模式讨论1995年日本阪神大震的预报问题

本文由我们已往提出的三个地震模式讨论了１９９５年日本阪神大震（Ｍ７．２）的预报问题。这些模式是立交模式、组合模式和调制模式。此外还用柯里奥利力（Ｃｏｒｒｉｏｌｉ）的效应讨论了主震３年后最强余震的预报。     

Predicting RUSLE (Revised Universal Soil Loss Equation) Monthly Erosivity Index from Readily Available Rainfall Data in Mediterranean Area

Summary Seasonal rainerosivity is important in the structure and dynamics of Mediterranean ecosystems. The present paper contributes to the quantitative assessment of RUSLE's monthly erosion index in a data-scarce Mediterranean region. Therefore, a regionalized relationship for estimating monthly erosion index (EI_30-month) from only three rainfall parameters has been obtained. Knowledge of the seasonal and annual distribution of erosivity index, permit soil and water conservationists to make improved designs for erosion control, water harvesting or small hydraulic structures. Although a few long data sets were used in the analysis, validation with established monthly erosivity index values from other Italian locations, suggest that the model presented (r² = 0.973) is robust. It is recommended to monthly erosivity estimates when experimental data-scarce rainfall become available.     

Potential and Shortcomings of Numerical Weather Prediction Models in Providing Meteorological Data for Urban Air Pollution Forecasting

The last decade progress in numericalweather prediction (NWP) modelling and studies of urbanatmospheric processes for providing meteorological data forurban air pollution forecasting is analysed on examples ofseveral European meteorological centres. Modern nested NWP models are utilising land-use databasesdown to 1 km resolution or finer, and are approaching thenecessary horizontal and vertical resolution suitable forcity scale. The recent scientific developments in the fieldof urban atmospheric physics and the growing availabilityof high-resolution urban surface characteristics datapromise further improvements of the capability of NWPmodels for this aim. A strategy to improve NWP data forthe urban air pollution forecasting is suggested.     

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