农田土壤湿度的人工神经网络预测诊断系统

采用人工神经网络（ＡＮＮ）方法，研制了农田土壤湿度预测，诊断分析系统，该系统根据不同的土壤湿度特点和影响因子，可自动提供时间序列拓展的人工神经网络预测模型和人工神经网络的混合预测模型进行土壤湿度的动态预测。并提供不同降水量的土壤湿水分渗透深度诊断分析，系统采用中文菜单显示的人机对话方式操作，使具有准动力学特性，多种可调参数和网络结构的ＡＮＮ方法变得简便易于操作。实际使用表明，该系统对土壤湿度的预测     

GPS掩星技术和电离层反演

系统地介绍了GPS无线电掩星技术的发展现状、系统组成和一些关键技术；论述了利用GPS掩星数据进行电离层反演的理论、模型及相应的计算流程．并结合GPS／MET和CHAMP卫星的实测资料，计算了电子密度剖面，与用其他方法所得结果的对比表明．GPS掩星电离层观测具有精度高、覆盖范围大等特点；最后讨论了GPS掩星技术应用于地震前兆监测的机理和前景。     

德州市深层地下水人工回灌试验浅探

德州市德城区由于长期开采深层地下水,引起了水位区域性大幅度下降,形成了大面积地下水降落漏斗,并产生了地面沉降等不良地质环境问题。为保护德州市地质环境和深层地下水资源,于2002年开展了深层地下水人工回灌试验。根据回灌试验成果和深层地下水目前开采强度推算,利用德城区现有部分(65%)深机井在丰水季节进行人工回灌,单井回灌量保持1200m3/d,每年保持90d的回灌时间,回灌量可达到1080×104m3/a,可增可采资源量675×104m3/a。因此开展人工回灌可以有效地补给地下水源,解决深层地下水资源枯竭问题。     

Hybrids between olive flounder Paralichthys olivaceus and stone flounder Kareius bicoloratus: karyotype, allozyme and RAPD analyses

The hybrid between olive flounder Paralichthys olivaceus and stone flounder Kareius bicoloratus was produced by artificial insemination of olive flounder eggs with stone flounder Sperm. Sinistral and dextral are two types of hybrid progeny after metamorphosis. Karyotypes of both hybrid flounders are the same as those of the two parental species. Of the 22 loci examined from 12 allozymes,12 confirmed hybridization of the paternal and matemal loci in hybrids and no difference was found in allozyme patterns of sinistral and dextral hybrid fishes. RAPD patterns of these specimens were also studied with 38 primers selected from 104 tested. Among them, the PCR products of 30 primers showed hybridization of the paternal and maternal bands. Genetic variation between hybrids and their parental stocks was analyzed by RAPD using 10 of the above 38 primers. The average heterozygosity and genetic distance were calculated. The results suggested that the filial generation could inherit a little more genetic materials from paternal fish than that from maternal fish.     

Ecosystem health assessment of Honghu Lake Wetland of China using artificial neural network approach

Honghu Lake, located in the southeast of Hubei Province, China, has suffered a severe disturbance during the past few decades. To restore the ecosystem, the Honghu Lake Wetland Protection and Restoration Demonstration Project (HLWPRDP) has been implemented since 2004. A back propagation (BP) artificial neural network (ANN) approach was applied to evaluatinig the ecosystem health of the Honghu Lake wetland. And the effectiveness of the HLWPRDP was also assessed by comparing the ecosystem health before and after the project. Particularly, 12 ecosystem health indices were used as evaluation parameters to establish a set of three-layer BP ANNs. The output is one layer of ecosystem health index. After training and testing the BP ANNs, an optimal model of BP ANNs was selected to assess the ecosystem health of the Honghu Lake wetland. The result indicates that four stages can be identified based on the change of the ecosystem health from 1990 to 2008 and the ecosystem health index ranges from morbidity before the implementation of HLWPRDP (in 2002) to middle health after the implementation of the HLWPRDP (in 2005). It demonstrates that the HLWPRDP is effective and the BP ANN could be used as a tool for the assessment of ecosystem health.     

SIMULATION AND PREDICTION OF DEBRIS FLOW USING ARTIFICIAL NEURAL NETWORK

Debris flow is one of the most destructive phenomena of natural hazards. Recently, major natural haz-ard, claiming human lives and assets, is due to debris flow in the world. Several practical methods for forecasting de-bris flow have been proposed, however, the accuracy of these methods is not high enough for practical use because of the stochastic and non-linear characteristics of debris flow. Artificial neural network has proven to be feasible and use-fill in developing models for nonlinear systems. On the other hand, predicting the future behavior based on a time se-ries of collected historical data is also an important tool in many scientific applications. In this study we present a three-layer feed-forward neural network model to forecast surge of debris flow according to the time series data collect-ed in the Jiangjia Ravine, situated in north part of Yunnan Province of China. The simulation and prediction of debris flow using the proposed approach shows this model is feasible, however, further studies are needed.     

CLOBAL CHARM OF THE CHANGJIANG RIVER DELTA

1SPLENDOROFHISTORICALCULTURE InChina,theChangjiang(Yangtze)RiverDelta①gen-erallymeansanadvancedproductivityandculture,whichaffectsdeeplynotonlythewholeChinabutal-sotheAsian-Pacificregion,evenEuropeandAfrica. 1.1CradlefortheCommunicationandPropagationofCivilization ThedevelopmentcourseoftheChangjiangRiverDelta'scultureisdevious,buttheNeolithicHemuduCultureexcavatedinYuyaoCityofZhejiangProvinceindicatestheadvancedpaddyriceculture;MajiabangCulture5000aB.P.andEastBridgeRuin…     

水污染物浓度的神经网络预测模式及效果检验

污染物浓度预测是环境保护的重要内容,将神经网络用于水中有机污染物浓度的预测并对效果进行检验.结果表明,预测值与观测值符合较好.     

于DEM光照晕渲模拟的梯田自动提取方法

梯田作为黄土高原最为典型的人工地貌之一,有重要的农业生产和水土保持价值。传统的梯田自动提取仅限于梯田所在区域的范围划定,未能对梯田田坎线实现有效的自动化提取。鉴于此,本文提出了一种基于光照晕渲模拟的梯田快速提取方法。首先,对无人机航测生成的1 m分辨率的数字高程模型（DEM）进行4个方向的光照晕渲模拟,并相加取平均值;然后,通过适当的阈值对均值图像进行二值化,并掩膜掉沟谷等非梯田区域;最后,基于二值化图像自动矢量化得到梯田田坎线,并通过适当的长度阈值进行碎线过滤提高提取精度。本文以陕西省长武县王东沟流域为实验样区进行了实验,结果表明,该方法提取结果的准确率为89.09%,具有较好的提取精度。此外,对该方法涉及到的参数进行了讨论,表明光照模拟的方向角采用2个正交的对称方向对、高度角采用田坎坡度的反正切值、二值化阈值采用t=180-σ的经验公式,可以满足黄土高原的梯田自动快速提取。     

基于A-star算法的矿井事故救援研究

针对井下不同形状巷道几何空间特征,确立以空间点、线、面为基本图元,以巷道中心线作为三维模型构建的基础框架,建立巷道三维模型。采用启发式路径搜索A-star算法,实现了应急救援路线智能快速选择。实践证明,该方法对于矿井事故定位和救援具有一定的实用性。