Identification of physical processes inherent in artificial neural network rainfall runoff models

The emergence of artificial neural network (ANN) technology has provided many promising results in the field of hydrology and water resources simulation. However, one of the major criticisms of ANN hydrologic models is that they do not consider/explain the underlying physical processes in a watershed, resulting in them being labelled as black‐box models. This paper discusses a research study conducted in order to examine whether or not the physical processes in a watershed are inherent in a trained ANN rainfall‐runoff model. The investigation is based on analysing definite statistical measures of strength of relationship between the disintegrated hidden neuron responses of an ANN model and its input variables, as well as various deterministic components of a conceptual rainfall‐runoff model. The approach is illustrated by presenting a case study for the Kentucky River watershed. The results suggest that the distributed structure of the ANN is able to capture certain physical behaviour of the rainfall‐runoff process. The results demonstrate that the hidden neurons in the ANN rainfall‐runoff model approximate various components of the hydrologic system, such as infiltration, base flow, and delayed and quick surface flow, etc., and represent the rising limb and different portions of the falling limb of a flow hydrograph. Copyright © 2004 John Wiley & Sons, Ltd.     

Comments on ‘A comparative study of ANN and neuro-fuzzy for the prediction of dynamic constant of rockmass’

Singh et al (2005) examined the potential of the ANN and neuro-fuzzy systems application for the prediction of dynamic constant of rockmass. However, the model proposed by them has some drawbacks according to fuzzy logic principles. This discussion will focus on the main fuzzy logic principles which authors and potential readers should take into consideration.     

基于EMD与神经网络的机械故障诊断技术

经验模式分解 (EMD)是分析非线性、非平稳信号的有力工具 ,它将信号分解为突出了原信号的不同时间尺度的局部特征信息的内在模函数 (IMF)分量。本文通过将各 IMF分量输入到 BP网络中进行训练学习和故障诊断 ,比直接输入原信号可以提高 BP网络对故障诊断的准确率 ,而且减少了训练时间。     

Modelling evaporation using an artificial neural network algorithm

This paper investigates the prediction of Class A pan evaporation using the artificial neural network (ANN) technique. The ANN back propagation algorithm has been evaluated for its applicability for predicting evaporation from minimum climatic data. Four combinations of input data were considered and the resulting values of evaporation were analysed and compared with those of existing models. The results from this study suggest that the neural computing technique could be employed successfully in modelling the evaporation process from the available climatic data set. However, an analysis of the residuals from the ANN models developed revealed that the models showed significant error in predictions during the validation, implying loss of generalization properties of ANN models unless trained carefully. The study indicated that evaporation values could be reasonably estimated using temperature data only through the ANN technique. This would be of much use in instances where data availability is limited. Copyright © 2002 John Wiley & Sons, Ltd.     

ARTIFICIAL NEURAL NETWORK CORRECTION OF ROUGH ERRORS OF OBSERVATIONS*

In the context of tower measured radiation datasets.following the correction principle meeting a diagnostic equation in data quality control and in terms of a technique for model construction on data and ANN (artificial neural network) retrieval for BP correction of radiation measurements with rough errors available,a BP model is presented.Evidence suggests that the developed model works well and is superior to a convenient multivariate linear regression model,indicating its wide applications.     

人工神经网络在岩体质量分级中的应用

结合四川省金沙江某水电站工程实例，应用BP人工神经网络方法建立3层BP网络模型，选取岩石单轴抗压强度等6个影响因素为输入变量，对坝基复杂岩体进行质量分级。通过机算机Visual C 语言编程实现神经网络模型，进行网络的学习和运算。以神经网络合理结构分析方法选取合理结构，确定合理隐层单元的数量，提高网络测试的精度。对测试结果的分析发现，经过优化的BP网络模型经多次学习后，测试精度提高，结果可靠，取得较好的实际应用效果。     

人工土层冻结法加固在盾构出洞施工中的应用

软土地区盾构出洞施工中洞口土体易失稳、渗水,上海明珠线二期工程浦东大道站至张扬路站区间,隧道在盾构出洞施工中,为确保地面建筑及地下管线的安全及正常使用,首次采用了人工土层冻结加固,取得了良好的效果;本文介绍了该工程出洞口土体加固的方案选择、关键技术处理及实际取得的效果,并探讨了人工土层冻结加固在含水松软土层的地下工程中的应用前景.     

基于人工神经网络的航磁信息关联方法的研究

应用人工神经网络方法 ,以航磁数据为样本 ,建立了航磁信息关联的BP人工神经网络模型。对山东省 1∶5 0万航磁水平一阶导数轴向图进行计算机自动关联叠置、提取航磁解释构造轴线的研究。经与垂向二阶导数对比分析 ,证实了该方法给出的模型具有较强的非线性、自适应和学习能力。     

重庆地区夏季一次降水过程及增雨潜力的数值模拟分析

利用国家气象中心GRAPES人工增雨云系模式,选取2008年7月4日重庆地区一次降水过程进行数值模拟,分析了重庆地区降雨天气的水汽分布、云系宏微观分布、云中微物理转化和增雨潜力等特征。结果表明：本次降水大气过程中,重庆地区水汽含量极为丰富,水汽分布与地形分布呈明显的对应关系,低层水汽输送较大,整层水汽通量较高,有明显水汽辐合,云中液态水对地面降水影响很大。西南气流和地形共同作用为重庆地区液态水的形成提供了有利条件,在东北部山区迎风坡处大量水汽累积抬升,易形成丰富的液态水。重庆东北部地区水汽向云水转化较强,过冷液态水含量丰富,冰晶含量少,0℃层附近水汽垂直通量较大,降水效率较低,有较大的增雨潜力。