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.     

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.     

Simultaneous determination of calcium and magnesium in water using artificial neural network spectro-photometric method

A new analytical method using Back-Propagation(BP) artificial neural networks and spectrophotometry for simultaneous determination of calcium and magnesium in tap water,the Yellow River water and seawater is established.By condition experiment,the optimum analytical conditions for calcium,magnesium and Arsenazo(Ⅲ) color reactions are obtained.Levenberg-Marquart(L-M) algorithm is used for calculation in BP neural network.The topological structure of three-layer BP ANN network architecture is chosen as 11-10-2(nodes).The initial value of gradient coefficient μ is fixed at 0.001 and the increase factor and reduction factor of μ take the default values of the system.The data are processed by computers with our own programs written in MATLAB 7.0.The relative standard deviations of the calculated results for calcium and magnesium are 2.31% and 2.14%,respectively.The results of standard addition method show that the recoveries of calcium and magnesium are 103.6% and 100.8% in the tap water,103.2% and 96.6% in the Yellow River water(Lijin district of Shandong Province),and 98.8%-103.3% and 98.43%-103.4% in seawater from Jiaozhou Bay of Qingdao.It is found that 14 common cations and anions do not interfere with the determination of calcium and magnesium under the optimum experimental conditions.The comparative experiments do not show any obvious difference between the results obtained by this new method and those obtained by the classical complexometric titration method in seawater medium.This method exhibits good reproducibility and high accuracy in the determination of calcium and magnesium and can be used for the simultaneous determination of Ca2+ and Mg2+ in tap water and natural water.     

2011年洪湖和梁子湖受旱程度差异和成因分析

湿地是水陆交错的生态系统,往往受旱灾影响大,故此,对不同湖泊湿地受旱程度差异及其原因分析,可为减灾和湿地保护提供科学依据。本文利用2010年5月和2011年5月2个时相的HJ-1卫星CCD影像,提取洪湖和梁子湖2个时相的湿地水面积,结合统计资料和问卷调查数据,从湿地水面积及空间分布变化、湿地生态系统功能退化、湿地生态系统,对人类福祉影响3方面,分析了两湖湿地受旱程度的差异。并综合气象数据、统计资料和问卷调查数据,从气象因子、区域自然地理环境、湿地类型及特点、区域水文条件和湿地受保护程度5方面探讨两湖受旱程度差异的原因。结果表明：（1）2011年春旱期间,洪湖比梁子湖受旱程度更严重。旱灾时,洪湖水面积减少83.93 km2,减小比例为26%,梁子湖水面积减少13.02 km2,减小比例为4%;两湖的涵养水源、保护生物多样性、物质生产和水质净化功能均受到损害,洪湖较梁子湖更严重;受旱灾影响,洪湖湿地对人类福祉的消极影响大于梁子湖。（2）降雨量、气温和风速等气象因素、湖泊湿地成因类型、区域自然地理环境和水文条件是影响两湖受旱程度差异的主要因素,另外,湿地保护措施的差异及效果也会造成两湖受旱程度不同。     

基于层次分析法的拉市海高原湿地生态系统健康评估

 本文以云南省丽江市拉市海高原湿地生态系统核心区为研究区域,利用区域生态系统功能监测指标和自然环境及人文活动因素指标,对拉市海高原湿地生态系统的健康状况进行空间综合评估分析。首先,根据当前生态系统的状况和高原湿地生态系统的特征,结合湿地保护区开展的系列生态系统功能监测项目及其指标,并充分考虑评估模型的系统性、科学性和实际操作的可行性,构建了拉市海高原湿地生态系统健康评价的指标和指标体系。然后,采用层次分析方法(AHP),融合自然综合指标、人文因素指标在内的生态系统健康综合评价模型,确定6级的生态系统健康评估标准;最后,根据空间评价模型和数据资源,对各个指标权重进行分析计算,得出拉市海高原湿地生态系统的健康状况的空间分布。研究结果表明,拉市海高原湿地的生态系统健康状况良好。按照划定的6级标准,其中,稳定区和较稳定区面积9 358.93hm²,占研究区域总面积的61%,脆弱区和较脆弱区面积为6 058.46hm²,占总面积的39%。整个研究区域内不存在很稳定区和极稳定区,这为高原湿地生态系统的综合管理及发展规划提供了依据。     

Simultaneous determination of iron and manganese in water using artificial neural network catalytic spectrophotometric method

A new analytical method using Back-Propagation (BP) artificial neural network and kinetic spectrophotometry for simultaneous determination of iron and magnesium in tap water, the Yellow River water and seawater is established. By conditional experiments, the optimum analytical conditions and parameters are obtained. Levenberg-Marquart (L-M) algorithm is used for calculation in BP neural network. The topological structure of three-layer BP ANN network architecture is chosen as 15-16-2 (nodes). The initial value of gradient coefficient μ is fixed at 0.001 and the increase factor and reduction factor of μ take the default values of the system. The data are processed by computers with our own programs written in MATLAB 7.0. The relative standard deviation of the calculated results for iron and manganese is 2.30% and 2.67% respectively. The results of standard addition method show that for the tap water, the recoveries of iron and manganese are in the ranges of 98.0%-104.3% and 96.5%-104.5%, and the RSD is in the range of 0.23%-0.98%; for the Yellow River water (Lijin district of Shandong Province), the recoveries of iron and manganese are in the ranges of 96.0%-101.0% and 98.7%-104.2%, and the RSD is in the range of 0.13%-2.52%; for the seawater in Qingdao offshore, the recoveries of iron and manganese are in the ranges of 95.3%-104.8% and 95.3%-104.7%, and the RSD is in the range of 0.14%-2.66%. It is found that 21 common cations and anions do not interfere with the determination of iron and manganese under the optimum experimental conditions. This method exhibits good reproducibility and high accuracy in the determination of iron and manganese and can be used for the simultaneous determination of iron and manganese in tap water and natural water. By using the established ANN- catalytic spectrophotometric method, the iron and manganese concentrations of the surface seawater at 11 sites in Qingdao offshore are determined and the level distribution maps of iron and manganese are drawn.     

洪湖微齿眼子菜种群遗传多样性研究

采用RAPD方法研究了洪湖微齿眼子菜种群的遗传多样性，矛初步探讨了它的遗传结构及影响因素。在97个脚标记中有28条（28．87％）是多态性标记，Shannon信息多样性指数分析表明该种群总的遗传多样性为0.193，说明洪湖微齿眼子菜种群的遗传多样性水平较低。研究发现距离和种群发展历史对洪湖微齿眼子菜种群的遗传结构有重要影响。     

APPLICATION OF ARTIFICIAL NEURAL NETWORK IN COMPLEX SYSTEMS OF RFGIONAL SUSTAINABLE DEVELOPMENT

Meeting the challenge of sustainable development requires substantial advances in understanding the interaction of natural and human systems. The dynamics of regional sustainable development could be addressed in the context of complex system thinking. Three features of complex systems are that they are uncertain, non-linear and self-organizing. Modeling regional development requires a consideration of these features. This paper discusses the feasibility of using the artificial neural networt(ANN) to establish an adjustment prediction model for the complex systems of sustainable development (CSSD). Shanghai Municipality was selected as the research area to set up the model, from which reliable prediction data were produced in order to help regional development planning. A new approach, which could help to manage regional sustainable development, is then explored.     

洪湖微齿眼子菜种群遗传多样性研究

采用RAPD方法研究了洪湖微齿眼子菜种群的遗传多样性,并初步探讨了它的遗传结构及影响因素。在97个RAPD标记中有28条(28.87%)是多态性标记,Shannon信息多样性指数分析表明该种群总的遗传多样性为0.193,说明洪湖微齿眼子菜种群的遗传多样性水平较低。研究发现距离和种群发展历史对洪湖微齿眼子菜种群的遗传结构有重要影响。     

基于BP神经网络的智能入侵检测系统

介绍了BP神经网络的基本知识，设计了基于BP神经网络的智能入侵检测系统。并提出了根据不同的网络协议使用不同神经网络的思想，指出了每个神经网络需要的网络数据，并阐述了训练和测试神经网络的方法。     

基于3S技术的野鸭湖湿地系统景观格局演变研究

野鸭湖湿地自然保护区是北京最大的湿地自然保护区,也是唯一的湿地鸟类自然保护区,因此,选择野鸭湖研究城市湿地景观格局演变特征具有典型意义。以野鸭湖自然保护区为研究对象,分析了1999年、2004年、2009年和2014年4个时期野鸭湖湿地系统景观格局演变过程,并根据景观指数分析其动态变化趋势。研究结果表明:①野鸭湖湿地面积在1999~2004年间逐渐减少,之后逐年恢复,至2014年湿地面积总数已恢复到1999年的水平,但水体的面积仍缩减一半,主要恢复的是草甸湿地和疏林湿地。②野鸭湖湿地景观格局的变化特征主要表现为:景观破碎度增加,景观形状更加复杂,景观多样性和异质性增加而优势度降低。③野鸭湖湿地斑块类型呈现如下特征:耕地连片分布,耕地开垦力度减缓;草甸湿地成片增长且优势度逐渐明显,形状复杂;水体分布聚集,2014年出现恢复现象;疏林湿地面积不断增长,呈现破碎化现象;建筑用地面积也略微增长,但分布分散;滩涂湿地面积最小,形状简单。     

Estimation of reservoir porosity using probabilistic neural network and seismic attributes

Porosity is one of the most important properties of oil and gas reservoirs. The porosity data that come from well log are only available at well points. It is necessary to use other method to estimate reservoir porosity.Seismic data contain abundant lithological information. Because there are inherent correlations between reservoir property and seismic data,it is possible to estimate reservoir porosity by using seismic data and attributes.Probabilistic neural network is a powerful tool to extract mathematical relation between two data sets. It has been used to extract the mathematical relation between porosity and seismic attributes. Firstly,a seismic impedance volume is calculated by seismic inversion. Secondly,several appropriate seismic attributes are extracted by using multi-regression analysis. Then a probabilistic neural network model is trained to obtain a mathematical relation between porosity and seismic attributes. Finally,this trained probabilistic neural network model is implemented to calculate a porosity data volume. This methodology could be utilized to find advantageous areas at the early stage of exploration. It is also helpful for the establishment of a reservoir model at the stage of reservoir development.     

Storm surge disaster evaluation model based on an artificial neural network

Back propagation is employed to forecast the current of a storm with various characteristics of storm surge; the technique is thus important in disaster forecasting. One of the most fuzzy types of information in the prediction of geological calamity is handled employing the information diffusion method. First, a single-step prediction model and neural network prediction model are employed to collect influential information used to predict the extreme tide level. Second, information is obtained using the inf...     

东洞庭湖国家级自然保护区湿地资源评价

湿地是介于陆地与水体之间的过渡地带,是陆地上最大的碳储库和陆地生态系统循环的重要组成部分。但是,由于对湿地资源的严重破坏和不合理利用,湿地面积急剧减少。近年来,湿地资源评价已经倍受关注,湿地资源评价为实现湿地资源可持续利用,保持湿地功能持久性提供科学依据。本文通过对湿地生态系统的研究,从生态效益、社会效益、经济效益角度...     

基于RS与GIS的鄱阳湖典型湿地覆盖变化及生态环境保护

鄱阳湖湿地对长江洪水调蓄、候鸟保护等具有重要意义。在两景Land-Sat影像解译的基础上,选择典型区域,在GIS支持下对湿地土地利用/覆盖变化进行分析,同时对湿地生态资源利用和环境保护的相关问题进行了探讨。结果表明,人类活动是湿地土地利用/覆盖变化的重要驱动力。     

Evolving neural network using variable string genetic algorithm for color infrared aerial image classification

Coastal wetlands are characterized by complex patterns both in their geomorphlc and ecological teatures. Besides field observations, it is necessary to analyze the land cover of wetlands through the color infrared （CIR） aerial photography or remote sensing image. In this paper, we designed an evolving neural network classifier using variable string genetic algorithm （VGA） for the land cover classification of CIR aerial image. With the VGA, the classifier that we designed is able to evolve automatically the appropriate number of hidden nodes for modeling the neural network topology optimally and to find a near-optimal set of connection weights globally. Then, with backpropagation algorithm （BP）, it can find the best connection weights. The VGA-BP classifier, which is derived from hybrid algorithms mentioned above, is demonstrated on CIR images classification effectively. Compared with standard classifiers, such as Bayes maximum-likelihood classifier, VGA classifier and BP-MLP （multi-layer perception） classifier, it has shown that the VGA-BP classifier can have better performance on highly resolution land cover classification.     

Modeling scenic quality of wetland landscape resources in South Dongting Lake, China

This study introduces a method to quantify the scenic value of wetland landscapes and provides the guidance on the allocation of efforts for the purpose of preserving the most important environments.Dongting Lake,a traditional tourist location,is one of the five largest freshwater lakes in China and a Grade 5A National Scenic Region. This study objectively analyzes the scenic resources of South Dongting Lake,and presents a model based on Quantification TheoryⅠto assess wetland scenic landscapes.Employing a multilinear regression relationship between land-scape factors and their associated scenic value,we built up the Wetland Landscape Quantification Model(WLQM), which was used to classify 221 landscapes into five grades.Finally,some suggestions are proposed regarding quantitative assessment and its use for the sustainable development of wetland environments.     

A method of catchments health assessment under value-pressure model and its application in urbanized river network area: A case study in Shanghai,China

Catchments health assessment is fundamental to effective catchments management. Generally, an as-sessment method should be selected to reflect both the purpose of assessment and local characteristics. A trial in Shanghai was conducted to test the method for catchments health assessment in urbanized river network area. Seven indicators that described four dimensions of river, river network, land use and function, and local feature were used to assess catchments values; while possible change rate of urbanization and industrialization in the next 3 years were chosen for catchments pressure assessment in the value-pressure model. Factors related to catchments classi-fication, indicators measurement and protection priority have been considered in the development strategies for catchments health management. The results showed that value-pressure assessment was applicable in urbanized catchments health management, particularly when both human and catchments had multiple demands. As a result of over 30-year rapid urbanization, more than 70% of Shanghai river network area was still in a healthy condition with high catchments values, among them, 39.3% was under high pressure. Poor water quality, simplified river system and weakened local feature of river pattern had largely affected catchments health in Shanghai. Lack of long-term monitoring data would seriously restrict the development and validity of catchments health assessment.     

基于神经网络汉语分词模型的优化

针对目前汉语分词系统中BP算法收敛速度慢等难题，提出利用Levenbery-Marquart算法优化神经网络分词模型。较详细地介绍了所建立的试验系统。并进行了试验分析。优化后的模型可以有效地解决神经网络模型中易陷于局部极小、算法收敛速度慢等缺点。进一步提高该模型在分词领域中的实用性和分词效率，对于中文信息的自动化处理具有重要意义。     

基于模糊C均值的进化神经网络分类模型

将模糊C均值方法与进化算法相结合,提出了一种新的进化神经网络分类模型。实验结果表明,模型与其它标准的分类方法：C4.5、CART、BP神经网络、模糊ARTMAP比较,有更高的分类精度。