边坡变形分析与预报的模糊人工神经网络方法

边坡变形受地质、水文、气候、突发因素（如暴雨、地震）、人工活动（如开挖或防护）等各种因素的影响,这些因素与变形之间一般不是确定的定量关系。本文介绍了不同影响因素与位移量的模糊关系及位移量的模糊近似推论,在此基础上应用模糊人工神经网络进行位移量预报,并以一个边坡监测资料为例说明了具体的应用方法。     

新一代的地震预报专家系统NGESEP中的推理子系统

本文介绍了第一代专家系统和第二代专家系统的特点以及新一代地震预报专家系统中的推理子系统。该系统根据地震预报知识的特点设计了推理机,解决了推理子系统中的一系列问题。     

由我国历史飞蝗北界记录得到的古气候推断

本文利用我国古代有关飞蝗的文献记录,整理出近1000年来飞蝗记录地域北界变动资料,根据飞蝗的生态习性,推断出飞蝗发生在我国北纬41°以北地区的年份的气温条件指出1162～1177年、1265～1280年和1763～1773年是我国东北地区气候温暖的时段,这分别为南宋气候、中世纪温暖期和小冰期的回暖期提出新的佐证。     

现场或震后趋势预测中的几个不精确推理问题

在地震预报的实践中经常会遇到下面的情况,即如何估计在经过了一定的预报时间后尚未发生预期地震的条件下在余下的预报时间段内发生预期地震的信度问题。文中结合地震发生是个小概率事件这一特点,运用MYCIN模型推得了估计这一信度的实用公式,同时还对所遇到的其它几个不精确推理问题进行了论述。     

关于岩土样本统计分析的几点讨论

结合岩土单元体的天然成因及人为设定的基本特性,阐述了岩土样本离散的必然性及其离散程度与岩土单元体分划范围的相关性,通过对变异系数的适用性及岩土样本概况性的分析,提出采用均同指数评价样本分布的离散程度和按相对容量标准控制岩土样本数量等观点,建议切实针对岩土母体的分布形态推断标准值,同时对小样本条件下标准值的确定方式进行了分析和建议。     

Time series forecasting of river flow using an integrated approach of wavelet multi-resolution analysis and evolutionary data-driven models. A case study: Sebaou River (Algeria)

The complexity of hydrological processes and lack of data for modeling require the use of specific tools for non-linear natural phenomenon. In this paper, an effort has been made to develop a conjunction model – wavelet transformation, data-driven models, and genetic algorithm (GA) – for forecasting the daily flow of a river in northern Algeria using the time series of runoff. This catchment has a semi-arid climate and strong variability in runoff. The original time series was decomposed into multi-frequency time series by wavelet transform algorithm and used as inputs to artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models. Several factors must be optimized to determine the best model structures. Wavelet-based data-driven models using a GA are designed to optimize model structure. The performances of wavelet-based data-driven models (i.e. WANFIS and WANN) were superior to those of conventional models. WANFIS (RMSE = 12.15 m³/s, EC = 87.32%, R = .934) and WANN (RMSE = 15.73 m³/s, EC = 78.83%, R = .888) models improved the performances of ANFIS (RMSE = 23.13 m³/s, EC = 54.11%, R = .748) and ANN (RMSE = 22.43 m³/s, EC = 56.90%, R = .755) during the test period.     

双黑洞系统的不同起源及其参数分布

双黑洞组成的近密双星系统并合是激光干涉仪引力波天文台等地基引力波探测器的主要探测对象。随着探测器灵敏度的提高,大量该类信号的探测将成为进一步研究黑洞物理的有效工具。但是目前对双黑洞系统的起源机制和内禀参数分布等物理问题的研究还不够深入,例如由引力波探测得到的黑洞质量分布与X射线双星观测的结果存在较大差异,还未有很好的理论模型可解释该结果。目前普遍认为双黑洞系统主要有两种起源:大质量双星演化机制和动力学起源机制。基于这两类起源的双黑洞系统在质量、自旋分布等方面存在差异。因此可在贝叶斯理论框架下,利用引力波信号携带的波源质量和自旋等信息,推断波源起源,计算不同起源的双黑洞系统所占比例,以及检验质量自旋等参数分布的差异。     

Systematic increase in model complexity helps to identify dominant streamflow mechanisms in two small forested basins

This study shows how the use of increasing model complexity allows us to hypothesize about dominant streamflow mechanisms in two small Brazilian forested basins. Nine different structures from SUPERFLEX, an objective framework to systematically increase hydrological model complexity, were tested and we extended the flexible modelling methodology to error models as well. We show that applying a rigorous methodology in a model evaluation framework, with residual analysis and control of model complexity, is essential for testing a model as a hypothesis for dominant hydrological controls. Our results indicate that the model architecture was more important than the increase in the number of model parameters. Better performing models were those with a parallel structure, which confirms our a priori belief about the dominant runoff mechanisms of the studied catchments, characterized by a rapid response to rainfall, but also a constant river discharge fed by water storage on the thick soil layer.