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61.
滑坡定量预测的非线性理论方法 总被引:19,自引:0,他引:19
滑坡时空预测是当前滑坡研究中的难题,特别是滑坡时间预测工作,其进展缓慢。本文基于非线性科学理论,分析了滑坡活动的分形特征及时间分形预测方法,研究了滑坡空间预测的人工神经网络特征,系统介绍了滑坡时间预测的非线性动力学理论。在此基础上,讨论了滑坡定量预测的发展趋势。 相似文献
62.
地球化学场精细结构解析方案与应用 总被引:2,自引:2,他引:2
为满足勘查地球化学对化探数据处理越来越精细的要求,把人工神经网络和统计分析结合起来形成地球化学场精细结构解析方案。该方案包括对地球化学样品的分类和对各类样品性质的详细研究,目的在于提供便捷的数学和计算机软件工具,以提取化探数据中的深层信息。可以用于:①研究各组样品中元素含量水平和组合特征以解释可能的矿种;②研究元素组合特征与指示元素的关联和区别以揭示可能的成矿作用过程;③研究异常样品的空间组合属性以揭示地球化学场的精细结构;④研究异常样品之间的差异性以缩小找矿靶区;⑤综合分析以确定剥蚀深度和找矿前景。该方案也适用于环境地球化学及矿产资源预测研究。 相似文献
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Determination and application of the weights for landslide susceptibility mapping using an artificial neural network 总被引:38,自引:0,他引:38
The purpose of this study is the development, application, and assessment of probability and artificial neural network methods for assessing landslide susceptibility in a chosen study area. As the basic analysis tool, a Geographic Information System (GIS) was used for spatial data management and manipulation. Landslide locations and landslide-related factors such as slope, curvature, soil texture, soil drainage, effective thickness, wood type, and wood diameter were used for analyzing landslide susceptibility. A probability method was used for calculating the rating of the relative importance of each factor class to landslide occurrence. For calculating the weight of the relative importance of each factor to landslide occurrence, an artificial neural network method was developed. Using these methods, the landslide susceptibility index (LSI) was calculated using the rating and weight, and a landslide susceptibility map was produced using the index. The results of the landslide susceptibility analysis, with and without weights, were confirmed from comparison with the landslide location data. The comparison result with weighting was better than the results without weighting. The calculated weight and rating can be used to landslide susceptibility mapping. 相似文献
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The strain developed due to creep is mainly proportional to the logarithm of the time under load, and is mostly proportional
to the stress and temperature. At higher temperature the creep rate falls slowly with respect to time, and the creep strain
is proportional to a fractional power of time, with the exponent increasing as the temperature increases and reaching a value
approximately one-third at temperatures of about 0.5°C. At these temperatures, the creep increases with stress according to
a power greater than unity and possibly exponentially. It increases with temperature as (−U/kT), where U is an activation energy and k is Boltzman’s constant. There are different methods to determine the creep strain and the energy of Jog (B) including experimental
methods, multivariate regression analysis, and by numerical simulation. These methods are less cumbersome and time consuming.
In the present investigation, artificial neural network technique has been used for prediction of the creep strain and energy
of Jog (B). Two different networks have been tested and validated. Both the networks have four input neurons and one hidden
layer with five neurons, and one output neuron. The data for different rocks at temperatures up to 750°C under conditions
of compressive or tortional stress are taken from the literatures. The training and testing data sets used were 163 and 14,
respectively. To deal with the problem of overfitting of data, Bayesian regulation has been used and network is trained with
suitable training epochs. The coefficients of correlation among the predicted and observed values are found high and they
improve the confidence of the users. The mean absolute percentage error obtained are also very low. 相似文献
70.
Efficient estimation of flood forecast prediction intervals via single‐ and multi‐objective versions of the LUBE method
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Lei Ye Jianzhong Zhou Hoshin V. Gupta Hairong Zhang Xiaofan Zeng Lu Chen 《水文研究》2016,30(15):2703-2716
Prediction intervals (PIs) are commonly used to quantify the accuracy and precision of a forecast. However, traditional ways to construct PIs typically require strong assumptions about data distribution and involve a large computational burden. Here, we improve upon the recent proposed Lower Upper Bound Estimation method and extend it to a multi‐objective framework. The proposed methods are demonstrated using a real‐world flood forecasting case study for the upper Yangtze River Watershed. Results indicate that the proposed methods are able to efficiently construct appropriate PIs, while outperforming other methods including the widely used Generalized Likelihood Uncertainty Estimation approach. Copyright © 2016 John Wiley & Sons, Ltd. 相似文献