Cellular automata spatial temporal data model and forecasting approach

The standard cellular automata(CA) model is expanded to meet requests of space time dynamic simulation and forecast under the platform of geographic information system(GIS). Taking power load forecasting of the electric power industry as the specific application, the relations between dynamic model of the land use and power load space are established. The data and attribute data interactive discrete in spatial temporal data management have been solved. The CA theory is practically used to simulate the process of urban land use dynamic development, to forecast future land use types of each small area, to establish spatial load forecasting model. It breaks through the localization of all kinds of forecasting methods of traditional space time separation power prediction. The effectiveness of the prediction method is verified by example.     

加工番茄产量组合预测模型研究

为了实现加工番茄产量的精准预测,通过采用方差倒数法来确定权重,将一元线性回归模型、灰色预测模型和指数平滑模型加以组合,根据2001—2009年新疆某加工番茄种植及产量数据,建立了加工番茄产量组合预测模型。以预测2010年和2011年番茄产量为例,结果表明此组合预测模型与单一预测模型相比更加准确可靠。该研究为加工番茄产业决策支持系统的建立奠定了理论基础。     

Determination to Weighting Coefficient of Combination Forecast Based on Rough Set Theory

The problem of determination to weighting coefficient is a key and difficulty for combination forecast. A method of determining weighting coefficient based on rough set theory is showed in this paper. Determining weighting coefficient is translated into estimating significance of attributes among rough set. A relation data model about combination forecast is established. Knowledge systems are built through making attribute value into eigenvalue. Under data moving, the weighting coefficients of a combination forecast model are computed by analyzing the dependence and significance of forecasting method for the predicted object. The proposed approach overcomes the subjectivity of traditional determination to weighting coefficient, avoids computing linear or nonlinear extremum problem and makes combination forecast more objective. The validity of the proposed approach is verified with a case.     

GIS技术在农业规划畜牧场选址中的应用

畜牧场选址是一项受多方面因素制约的工作。在利用GIS技术以前,畜牧场选址工作是需要多部门共同协作,进行实地调查,得到可以建设畜牧场的地块。不仅费力费时,而且无法准确评估防疫等各个方面的影响因素。笔者在概述地理信息系统在农业各领域应用的基础上,以吉林省永吉县中国-新加坡食品控制区为例,重点体现了GIS技术在畜牧场选址中的应用。利用GIS技术将规划区域的各种地物信息、交通信息、地形信息等有机结合,将图片资料以图形化方式转换为空间数据,并与其他非空间数据、资料等有机结合。结果表明：通过GIS技术可以使畜牧业规划在区域布局上更为合理;产业选择上更为科学、提高重点项目的可操作性,提升规划的可行性。在畜牧产业中,提高畜牧场选址的工作效率和准确度,同时更准确的了解规划区域的畜牧生产潜力,确定养殖规模和不同畜种发展方向。     

Intelligent Extracting Method for Feature Mode of Electric Days Load

Generally there have a number of bad data in the electric load data and it affects the precision of load forecasting,so it is necessary for extracting the feature mode of days load data,then cleaning the load data before it is used to forecasting electric load or performing power system analysis.Inspired by soft clustering thought,a intelligent feature mode of days load data extracting method is proposed based on the mutual offset of fuzzy c-means clustering arithmetic and Kohonen self organization feature map neural network.With the merits of not only high extracting precision and convergent speed but also dynamic calculation capability,the method proposed can supply load forecasting or system analysis procedure with due data.Test results using actual data of Chengqu power supply bureau in Chongqing demonstrate the effectivity and feasibility of the method.     

Design and Development of Analysis and Forecast System for the Distribution of Car

Analysis and forecast for market have become important methods of making decision in most enterprises. It is the base of analysis and forecast to effectively store and make full use of the data in the distribution. Technology of data warehouse and OLAP provides the strong function of storing and integrating data, and mathematics methods provides a series of statistics and analysis tools. Combine IT and mathematics methods in the field of analysis and forecast for the data of distribution to put forward a design and implementation plan of application system in supporting decision based on data warehouse. The design exhibits the processes how to build data warehouse and multi-dimensional data, recommends the methods of analyzing and forecasting, and shows a good integration of advanced information technology and decision science.     

基于ARCGIS ENGINE的干旱监测预测系统

利用Visual Basic 6开发环境和ARCGIS Engine开发工具包,以EOS/MODIS等气象卫星资料和地面常规观测资料为主要信息源,建立干旱监测预测系统,发挥GIS强大的空间信息可视化管理和分析功能,实现大面积干旱灾害的快速监测及预警,为政府和有关部门提供一种强有力的决策支持方案。     

Load Forecasting Based on BP ANN for Economical Operation of Electric Boiler with Heat Reservoir Under Time-sharing price

Along with the generalization of DSM and time-sharing price system, the use of electric boiler with heat reservoir becomes more and more extensive. Load forecasting of heat supply system is an important base in the study of economical operation of electric boiler with heat reservoir under time-sharing price. The BP ANN modeling of the load forecasting for a 1200 kW electric boiler is discussed. The result of hourly heat load forecasting accords well with the real heat load.     

The Impact of Real‐Time Information on Choices During the Commute Trip: Evidence from a Travel Simulator

ABSTRACT In the area of travel behavior, route choice when facing congestion has been widely researched. However, there are other behavioral alternatives based on people's activities and adaptive decision‐making strategies that have been overlooked. This paper focuses on how and what kind of information is used when considering a subset of these alternatives. Alternatives examined are the final choice of people's decision‐making process when faced with unexpected events during the commute trip in the presence of real‐time information collected using GABRIEL (Gis Activity‐Based tRavel sImuLator) in Columbus, OH. An empirical analysis of the data set, which includes a limited set of alternatives (six in total) is presented. Bivariate analysis and multinomial logit models are used to identify variables that influence the choice. Results show that people are willing to experiment with other alternatives if provided the information to support their choice.     

农业信息技术在小麦育种中的应用研究

小麦作为我国三大粮食作物之一,其市场需求量不断增加。随着气候等因素变化,小麦产量及品质将受到严峻考验,因此需要更为优良的新品种出现。现阶段我国小麦育种仍主要以传统育种技术为主,而凭借育种家经验,从海量育种群体中筛选获得高产、多抗、优质新品种的难度越来越大。因此,有效提升育种效率变得极为迫切。随着农业信息技术在育种中应用的迅猛发展,无损预测预报与进程信息化管理等高效育种成为可能。本文从育种进程管理、数据采集、综合评判决策、种质资源信息化管理方面介绍国内外信息技术在小麦育种中的研究进展,分析信息化育种的需求和存在的主要问题,并对其应用前景进行展望。     

Processing Fault Data Method in Load Forecasting Based on Wavelet Analysis

The reliability and reality of load historical data is the foundation of load forecasting.But,the impact load in running power system,and the disturb data in collecting load data through the SCADA may cause much fault data in load historical data. Focusing on solving this problem, a method through adjusting amplitade of its wavele modulus maxima and processing the wavelet decomposed detail signal by soft threshold based on wavelet analysis and singularity theory, then fault date can be eliminated,so that,the real historical imformation and regulation data can be gained by load forecasting.     

Fuzzy inference in the inter-wire crosstalk prediction

This paper presents the application of fuzzy inference on the issue of predicting crosstalk between interconnection wires. Effective electromagnetic interference parameters are selected as predictors from the pre-survey data, and the data are classified according to their statistical properties, and then fuzzy implication sentences are merged to determine the relationship between fuzzy implication set. Compared with the artificial neural network forecasting method,fuzzy inference forecasting method can not only make full use of existing experience and knowledge of experts, but also extract antomation summary and the new inference rules. The result of the example shows that the method is feasible.     

Data Analysis Method for Neural Network Based on Short-term Load Forecasting

In neural network based short-term load forecasting, complexity and redundancy of input data have a negative effect on network training efficiency and forecasting precision. Focusing on solving this problem, a multiple method of data processing is developed. Firstly a method called input variable contribution analysis is applied, which divides input variables into primary variables and minor variables according to their contribution to network output. Minor variables are tossed out. Then principal component analysis is applied to primary variables to eliminate linear correlation among them, thus reduce the variable dimension. Based on this method, the main components are gotten, and then simplified network structure is designed. The result shows that after data processing, the training time is reduced noticeably and forecasting precision is enhanced.     

宁夏灌区日光温室内气温变化规律与最低气温预测模式

为了提高日光温室的气象服务水平和能力,利用引黄灌区的日光温室内气温和当地气象台站的常规气象观测资料,开展了温室内气温的变化特征和温室内最低气温预测方法研究。结果表明,不同天气条件下的温室内气温日变化规律存在明显差异;温室内的日最高气温时间分布较为集中,主要出现在每日的12:00—15:00,而日最低气温主要出现在每日的7:00—9:00;温室内、外的最低气温呈现明显的线性关系,据此建立了温室内最低气温的线性回归方程,历史回代检验表明方程的预报绝对误差绝大多数在3℃以内,精度较好。研究结果可为温室的气象保障服务提供技术支持。     

ECMWF细网格产品在一次大到暴雨天气预报中的释用

利用常规气象观测、区域自动站加密、ECWMF细网格模式产品(0.25°×0.25°)资料,针对2017年7月23-24日发生在青海省东部地区一次大到暴雨天气过程进行分析,得出ECWMF细网格资料地面至中高层湿区的叠加区域及水汽通量散度的汇合区域对大到暴雨落区的预报具有较好的指示意义。对于大雨或暴雨以上量级的强降水,单一模式预报量级均偏小,落区偏西,而综合多种模式的相关产品能给预报提供更好的参考,本地化QPF客观产品对大到暴雨预报具有较好的参考价值。     

Application of Combination Grey Model in Power Load Forecasting

Based on the gray forecast theory, this paper studies the principle and deficiency in power load forecasting by the basic grey model and other improved models, and introduces a new method -the combination grey model to forecast the long-medium power load. Based on an example, the basic grey model, other improved models and combination grey model are used to forecast power load and results of all models are analyzed and compared. The calculation results show that forecasting power load by grey theory is credible and simple. For this type of complex problems such as forecasting the long-medium power load, the combination grey model is specially useful because of it's high precision and facility. The method can be used as one of the tools of forecasting the long-medium power load.     

A Fuzzy Regression Method for Long-Term and Medium-Term Load Forecasting

In view of the observation data fuzziness and load pattern fuzziness,a new fuzzy regression prediction method was presented for long-term and medium-term load forecasting. With the established fuzzy regression model, the future load value can be forecasted based on the fuzzy historical observation data. The validity of the proposed method was verified with the numerical example of a practical system.     

3个时期达里诺尔国家级湿地自然保护区土地利用动态研究

受气候暖干化和人类活动影响,蒙古高原干旱区内陆湿地土地利用变化显著,及时掌握湿地土地利用变化,可为湿地自然保护区土地资源管理提供参考依据。[方法]本研究以1994、2005和2016年达里诺尔国家级自然保护区Landsat-TM/OLI影像为数据源,采用决策树分类、转移矩阵和动态度等方法对其进行动态分析。[结果]结果表明：研究区土地利用类型分类总体精度较高,主要以草地、林地、湖泊为主,沼泽、沙地、滩地、裸地所占比例较小。1994—2005年238.93 km2地物发生变化,占总面积18.81%,其中,林地和草地分别增加了19.25%和10.94%;沙地和裸地分别减少了66.63%和47.19%。2005—2016年,191.72 km2地物发生变化,占总面积15.09%,其中,沙地和滩地分别增加了19.73%和12.64%;裸地、沼泽和林地分别减少了59.51%、21.36%和15.72%。[结论]研究区地物覆盖类型变化明显,1994—2016年草地、林地、滩地面积增加,湖泊、沼泽、裸地面积减少。土地利用类型主要转移的方向为湖泊类型转化为滩地和沼泽,沼泽和裸地类型转化为草地,总体上湿地功能具有退化趋势。     

基于L-THIA和GIS的辽河源头区小流域非点源污染负荷变化分析

农业非点源污染是导致流域水质恶化的重要原因之一。笔者选择了辽河源区域作为研究目标,重点研究辽河流域的小型水系,使用所处位置的土地状况以及日降雨量数据,基于长期水文影响评价模型L-THIA,以GIS空间分析技术为平台,对研究区的总氮、硝态氮、总磷、COD和可溶磷5种非点源污染物的污染输出负荷进行估算,并分析了其空间分布特征。结果表明：总氮、硝态氮和总磷3种污染物的单位网格污染物输出负荷的空间分布较为一致,可溶磷和COD污染输出负荷分布各有特点。区内的农用地域污染一般都是农田抑或是村里闲置的土地。经研究发现,越是干旱的区域会使得调查数据分布影响越大,对总氮、硝态氮和总磷的贡献率分别达到了82.57%、80.02%和92.92%,村镇用地对可溶磷的污染负荷分布影响最大,而水田对COD污染输出负荷作用明显。根据不同污染物输出负荷并结合土地利用现状,采用GIS空间叠加和融合技术,将研究区划分为4个非点源污染控制区,分区结果能够为非点源污染控制提供科学依据。     

基于生态系统过程耦合的农业管理信息系统的分析与设计

对农业管理信息系统与生态过程模拟模型耦合进行了有益的探索,提出构建与生态过程耦合、基于空间信息的农业管理信息系统的设想,为农业管理和生态过程模拟模型在空间区域上的应用提出了一种有效、直观的解决方案。通过系统分析、系统设计及其功能实现技术的研究,采用SuperMap作为开发平台,利用二次开发语言开发了与生态过程耦合的农业管理信息系统(TES_AGRDSS)。该系统充分利用了地理信息系统（GIS）的强大功能,将各种信息进行综合、抽取,实现了单个模型内部的属性数据和空间数据的双向连接,以及不同模拟模型之间数据的双向连接与耦合,使得生态模拟模型在区域上的应用、生态过程模型与农业管理模型的耦合成为可能,为土地利用的管理与决策提供了强有力的帮助。