吐鲁番山区人工增水作业效果的统计分析

应用统计检验和回归分析两种数理统计方法，对吐鲁番1997－2000年山区人工增水作业效果进行了分析。结果得出作业影响区内作业年份5－6月平均流量显著增加。     

Structural damage detection using the optimal weights of the approximating artificial neural networks

This work presents a novel neural network‐based approach to detect structural damage. The proposed approach comprises two steps. The first step, system identification, involves using neural system identification networks (NSINs) to identify the undamaged and damaged states of a structural system. The partial derivatives of the outputs with respect to the inputs of the NSIN, which identifies the system in a certain undamaged or damaged state, have a negligible variation with different system errors. This loosely defined unique property enables these partial derivatives to quantitatively indicate system damage from the model parameters. The second step, structural damage detection, involves using the neural damage detection network (NDDN) to detect the location and extent of the structural damage. The input to the NDDN is taken as the aforementioned partial derivatives of NSIN, and the output of the NDDN identifies the damage level for each member in the structure. Moreover, SDOF and MDOF examples are presented to demonstrate the feasibility of using the proposed method for damage detection of linear structures. Copyright © 2001 John Wiley & Sons, Ltd.     

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.     

Simulation of artificial random field considering the temporal-spatial variation

Introduction For the seismic design of special structures such as nuclear power station, marine platform, long-span bridge and dam, generally the time-history response analysis of the structure under seismic excitation is imperative, which was coded in most seismic design codes. The earthquake records suitable for the seismic situation and site condition are necessary to be used as the seismic input in the dynamic analysis of structures. As a result of the limited observational condition of st…     

Application of genetic BP network to discriminating earthquakes and explosions

Introduction Artificial Neural Network (ANN) is an important branch of artificial intelligence. It is proposed on the foundation of the study on modern neural science, is a man-made network that can implement some functions based on the mans comprehensive understanding for cerebral neural network (HAN, WANG, 1997). ANN is a mathematical model of simplified human brain neural network and is used to simulate the structures and functions of human brain neural network. ANN is a complex netw…     

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.     

湘桂黔滇藏红色岩溶风化壳发育模式

基于对湘，桂、黔，滇，藏等地岩溶区红色风化壳的野外和室内研究，从表生地貌学，粘土矿物学和地球化学角度分析红色石灰土性质与地貌演化的关系，提出红色岩溶风化壳发育的二阶段模式；1）地貌夷平-风化物质积累阶段，在地貌演化过程中溶蚀残余物质不断积累，最后在夷平面上形成厚层连续的泥质风化壳，夷平面的地貌水文条件有利于粘粒的形成和保存，但限制了富铝化作用的有效进行，造就了岩溶风化壳粘粒含量高，富铝化程度低的特点，这与研究区23个红色岩溶风化壳剖面化学，粒度特征和粘土矿物组合特点一致。2）地貌切割-风化壳淋溶阶段，原始夷平面上的风化壳大多呈灰色，只有在构造隆升和地表微切割导致地下水位降低，淋溶条件开始改善的情况下，风化壳才有可能枞根本上转为红色。     

沙坡头人工植被固沙区天然降水的入渗和分配研究

土壤、植被冠层与大气界面间(SVAT)物质传输过程日益成为水文学研究最感兴趣的领域,降水量的迁移与转换是非灌溉区SVAT主要的物质传输过程.干旱半干旱区稀疏灌丛蒸散量占降水量的90%以上,因此对降水入渗与水分在土壤内植物根际区再分配的研究显得十分重要.试验于2001年8月17日至9月30日在中国科学院沙坡头试验站进行,主要观测人工植被荒漠灌木柠条(Caragana korshinskii) 灌丛固定沙丘降水入渗与再分配过程.结果表明:在7次不连续降水过程中,土壤入渗深度与降水强度呈简单线性相关关系,土壤入渗速率约为降水强度的10倍.当次降水过程中降水强度小于0.46 mmh-1时,土壤入渗速率约为0 cm*h-1,此时的降水对沙区土壤基本上没有水分补给作用.受荒漠灌木柠条根系吸水作用的影响,其根系密集剖面深度40～140 cm内降水水分入渗积累不明显.降水入渗速率及入渗深度受土壤剖面初始含水率多寡而变化,干燥土壤剖面有助于提高瞬时入渗速率.降水以后随着时间的推移,人工固沙区微环境内空气温度、湿度等气象条件适宜,柠条生长进入相对旺盛阶段,其根系密集层140 cm深度处土壤含水率在总体上下降的过程中,表现出昼消夜长的趋势,试验期间翌日 8:00时土壤含水率值略高于前一日20:00时水分值 0.1%～0.3%.     

胶茶人工群落在改善山地土壤生态环境上的作用

经过多年的实践和研究结果表明，橡胶(Hevea brasiliensis)—茶树(Camellia sinensis)两层人工群落结构，比单一橡胶种植模式有着明显的改善山地土壤生态环境的特点。胶—茶人工群落对减少水土流失，改善土壤微生物状况，增强土壤地表呼吸强度，促进团聚体形成和提高土壤肥力以及增加单产等方面，均有显著的效果。因此，胶—茶人工群落为合理利用我国西部热带山地资源，开辟了一条新的经营种植模式。     

湘北红壤坡地雨水过程的水土流失及其影响

模拟主要利用方式构建的垫面为主导因子,采用径流场实测方法,通过4a(1998～2001年)实地观测试验,剖析不同下垫面对雨水地表径流及其过程的影响。研究表明:降雨(时间分布及其强度)是影响地表径流特征的主导因素;年降雨量与地表径流量和系统水土流失量呈正相关。不同垫面地表径流产量有显著性差异;雨水径流过程的土壤和养分流失有相似的表现。在假定雨水年份的基础上,应用不同雨水年份出现概率指标,推算出:湘北红壤坡地现今利用状况下,雨水产地表径流过程的年均水土流失量及其养分(有机质、N、P、K)损失量;和可最大开发利用时,可能带来的水土流失与养分损失。提出"利用坡地集雨利用优势,构建与单元生态系统水循环平衡相适应的坡地农林复合生态系统,通过水平衡生态建设来维系生态系统的水分平衡"观点。