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711.
M. Kashif Gill Tirusew Asefa Mariush W. Kemblowski Mac McKee 《Journal of the American Water Resources Association》2006,42(4):1033-1046
ABSTRACT: Herein, a recently developed methodology, Support Vector Machines (SVMs), is presented and applied to the challenge of soil moisture prediction. Support Vector Machines are derived from statistical learning theory and can be used to predict a quantity forward in time based on training that uses past data, hence providing a statistically sound approach to solving inverse problems. The principal strength of SVMs lies in the fact that they employ Structural Risk Minimization (SRM) instead of Empirical Risk Minimization (ERM). The SVMs formulate a quadratic optimization problem that ensures a global optimum, which makes them superior to traditional learning algorithms such as Artificial Neural Networks (ANNs). The resulting model is sparse and not characterized by the “curse of dimensionality.” Soil moisture distribution and variation is helpful in predicting and understanding various hydrologic processes, including weather changes, energy and moisture fluxes, drought, irrigation scheduling, and rainfall/runoff generation. Soil moisture and meteorological data are used to generate SVM predictions for four and seven days ahead. Predictions show good agreement with actual soil moisture measurements. Results from the SVM modeling are compared with predictions obtained from ANN models and show that SVM models performed better for soil moisture forecasting than ANN models. 相似文献
712.
利用事故树对小断面锚网支护方式下,上下隅角的安全问题进行了分析,求出了最小割集和基本事件的结构重要度,提出了防止事故的方案,并在现场应用中取得了良好的效果。 相似文献
713.
714.
Bazzani GM 《Journal of environmental management》2005,77(4):301-314
715.
Philip Heilman Jerry L. Hatfield Martin Adkins Jeffrey Porter Russell Kurth 《Journal of the American Water Resources Association》2004,40(2):333-345
ABSTRACT: Water quality issues in agriculture are growing in importance. A common theme is the provision of better information to decision makers. This study reports the trial of a prototype decision support system by the U.S. Department of Agriculture Natural Resources Conservation Service and the Agricultural Research Service in the NRCS Harrison County Field Office in 1998. Observed data collected at the Deep Loess Research Station (DLRS) near Treynor, Iowa, were extrapolated using a modified GLEAMS field scale simulation model that included a nitrogen leaching component and a crop growth component. An accounting tool was used to convert crop yield estimates into crop budgets. A model interface was built to specify the climate, soil, and topography of the field, as well as the management scenarios for the alternative management systems. For the Deep Loess Hills area of Harrison County, a total of six soil and slope groups, with 66 total combinations of management practices forming management systems, were defined and simulated based on previously calibrated data from DLRS. A multi‐objective decision support system, the Water Quality Decision Support System, or WQDSS, was used to examine the tradeoffs in a comprehensive set of variables affected by alternative management systems with farmers in Harrison County. The study concluded that a multiobjective decision support system should be developed to support conservation planning by the NRCS. Currently, a larger scale effort to improve water quality decision making is underway. 相似文献
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717.
山区城镇泥石流减灾决策支持系统 总被引:11,自引:2,他引:11
山区城镇泥石流减灾决策支持系统是山区城镇泥石流减灾的重要手段之一。该系统由基础数据库、泥石流模型分析和泥石流减灾决策应用三大模块构成 ,其主要功能有降水、泥石流地声和运动监测及信息实时发送和接收、泥石流预报、泥石流危险范围预测与灾情预估、泥石流警报和临灾预案制定。其工作流程为 :降水监测仪将降水信息无线发送到控制中心主机 ,主机对降水信息处理后发送给泥石流预报模型进行泥石流预报 ,并利用泥石流危险性分区模型进行危险范围预测 ,同时进行灾情预估 ;泥石流地声监测仪将地声信号发送给主机 ,主机检测到泥石流地声后发出泥石流警报 ;泥石流运动监测仪将运动信息发送给主机 ,主机根据泥石流运动要素进行危险范围划定和灾情预估 ,最后制定临灾预案。 相似文献
718.
719.
A Knowledge-Based Systems Approach to Design of Spatial Decision Support Systems for Environmental Management 总被引:7,自引:0,他引:7
/ This paper describes a framework for designing spatial decision support systems for environmental management using a knowledge-based systems approach. An architecture for knowledge-based spatial decision supportsystems (KBSDSS) is presented that integrates knowledge-based systems with geographical information systems (GIS) and other problem-solving techniques. A method based on spatial influence diagrams is developed for representation of environmental problems. The spatial influence diagram provides an interface through which knowledge-based systems techniques can be applied to build capabilities for problem formulation, automated design, and execution of a solution process. In addition to the flexibility and developmental advantages of knowledge-based systems, the KBSDSS incorporates expert knowledge to provide assistance for structuring spatial influence diagrams and executing a solution process that automatically integrates the GIS, data base, knowledge base, and different types of models. The framework is illustrated with a system, known as the Islay Land Use Decision Support System (ILUDSS), designed to assist planners in strategic planning of land use for the development of the island of Islay, off the west coast of Scotland.KEY WORDS: Geographical information systems; Spatial decision support systems; Knowledge-based systems; Spatial influence diagrams; Environmental management 相似文献
720.
本文讨论了化学矿业可持续发展的内涵和实现可持续发展战略的最佳技术手段之一,即建立化学矿业可持续发展空间决策支持系统的重要性,提出了化学矿业可持续发展决策支持系统的构建理论依据和方法,探讨了化学矿业可持续发展空间决策支持系统总体设计、系统分析的数学模型以及系统的方法库和模型库的设计。 相似文献