Risk degree of debris flow applying neural networks

A number of methods for prediction of debris flows have been studied. However, the successful prediction ratios of debris flows cannot always maintain a stable and reliable level. The objective of this study is to present a stable and reliable analytical model for risk degree predictions of debris flows. This study proposes an Artificial Neural Networks (ANN) model that was constructed by seven significant factors using back-propagation (BP) algorithm. These seven factors include (1) length of creek, (2) average slope, (3) effective watershed area, (4) shape coefficient, (5) median size of soil grain, (6) effective cumulative rainfall, and (7) effective rainfall intensity. A total of 171 potential cases of debris flows collected in eastern Taiwan were fed into the ANN model for training and testing. The average ratio of successful prediction reaching 99.12% demonstrates that the presented ANN model with seven significant factors can provide a highly stable and reliable result for the prediction of debris flows in hazard mitigation and guarding systems.     

Creation of an artificial carbonate sand

This paper deals with the efficacy of creating artificial carbonate sand from crushed chalk to model the natural carbonate sand. Direct drained shear using 100 mm shear box and compressibility tests using 150 mm Rowe cell have been performed on artificial carbonate sand in order to determine the shear stress-strain and compressibility characteristics of the artificial material. These are compared with data from natural carbonate sands given in the literature. The results obtained demonstrate that shear stress-strain and compressibility characteristics of the artificial material closely mirror that of natural carbonate soils. The artificial carbonate sand shows generally similar characteristics, which are in the range of the natural carbonate material.     

A neural network approach for attenuation relationships: An application using strong ground motion data from Turkey

This paper presents an application of neural network approach for the prediction of peak ground acceleration (PGA) using the strong motion data from Turkey, as a soft computing technique to remove uncertainties in attenuation equations. A training algorithm based on the Fletcher–Reeves conjugate gradient back-propagation was developed and employed for three sample sets of strong ground motion. The input variables in the constructed artificial neural network (ANN) model were the magnitude, the source-to-site distance and the site conditions, and the output was the PGA. The generalization capability of ANN algorithms was tested with the same training data. To demonstrate the authenticity of this approach, the network predictions were compared with the ones from regressions for the corresponding attenuation equations. The results indicated that the fitting between the predicted PGA values by the networks and the observed ones yielded high correlation coefficients (R²). In addition, comparisons of the correlations by the ANN and the regression method showed that the ANN approach performed better than the regression. Even though the developed ANN models suffered from optimal configuration about the generalization capability, they can be conservatively used to well understand the influence of input parameters for the PGA predictions.     

人工神经网络在徐州岩溶地面塌陷评价中的应用

岩溶地面塌陷的影响因素很多,发展过程也复杂。在众多的对岩溶地面塌陷的评价方法中,神经网络具有自学习、自适应与高度非线性映射的特点,是一种非常有效的评价手段。在徐州岩溶石地面塌陷的评价中,成功地运用了人工神经网络技术,它具有的强大非线性映射能力,能够建立评价因子和评价对象之间的关系,正确选取评价因子,避免主观判断取值,从而得出可靠的预测模型和岩溶塌陷危险性分区图。     

单多普勒雷达在山区火箭人工增雨中的应用

利用单多普勒雷达资料产品对2003年8月3日和8月9日鄂西山区两次火箭增雨的作业条件和效果进行分析。分析表明:依据多普勒雷达资料,利用山区迎风坡地形对云系的抬升作用,适时开展火箭作业进行人工催化,可以达到显著的增雨效果。     

空间决策支持系统在商业智能中的应用研究

介绍了空间决策支持系统和商业智能的基本概念。分析了对商业决策有影响的空间因素，并借鉴Huff模型对网点吸引力进行了表达。描述了商业空间决策支持系统的总体设计。     

石漠化的成因机理与防治对策

从石漠化的研究现状出发，依据我国西南岩溶石山地区石漠化特征，着重分析其主要控制介质为岩性、构造、地貌、气候、岩溶、地下水、植被等自然介质因子与森林砍伐、高坡开垦等人为介质因子．石漠化是以岩溶山区脆弱的岩溶生态环境为基础，以内、外地质作用和人类恶性生态地质作用为驱动力，以资源退化和恶化为本质，基岩不断裸露，生态环境不断恶化，周而复始的恶性循环过程．提出对石漠化的研究重点是成固机理研究，对石漠化的防治应着重于对石漠化的驱动力及其主异因子的防治，阐述了治山必须先治水等防治对策与建议．     

人工智能和专家系统在地学中的应用综述

本文所指的地学是以地理学为主包括地质、气象、水文等以地球表层为研究对象的学科,其研究对象具有多因素相互作用的复杂性。本文在回顾了地理学／地学研究方法发展过程的基础上,分析了人工智能、专家系统、地理信息系统的相互关系,综述了专家系统在地学中的应用实例,最后分析了地学专家系统的发展趋势。     

人工神经网络在遥感数字图像分类处理中的应用

近年来,随着人工神经网络系统理论的发展,神经网络技术日益成为遥感数字图像分类处理的有效手段,并有逐步取代最大似然法的趋势。本文重点讨论国外遥感图像分类处理研究中应用效果显着或应用前景广阔的一些神经网络方法。最后讨论了有关上述方法在遥感数据分类处理中实际应用的一些原则性问题。     

伸臂式结构地震剪力图和弯矩图的理论包络线

本文以人工工振型作为理论工具、推演出伸臂式结构地震剪力地震弯矩的主、次上界的简练公式。不难理解,上界地震剪力图和弯矩图就分别相当于无数地震剪力图的弯矩图的包络线。文中还推演出主、次上界与等效静力解之间的一个十分简明的理论关系。