Precipitation forecasting by large-scale climate indices and machine learning techniques

Global warming is one of the most complicated challenges of our time causing considerable tension on our societies and on the environment. The impacts of global warming are felt unprecedentedly in a wide variety of ways from shifting weather patterns that threatens food production, to rising sea levels that deteriorates the risk of catastrophic flooding. Among all aspects related to global warming, there is a growing concern on water resource management. This field is targeted at preventing future water crisis threatening human beings. The very first stage in such management is to recognize the prospective climate parameters influencing the future water resource conditions. Numerous prediction models, methods and tools, in this case, have been developed and applied so far. In line with trend, the current study intends to compare three optimization algorithms on the platform of a multilayer perceptron (MLP) network to explore any meaningful connection between large-scale climate indices (LSCIs) and precipitation in the capital of Iran, a country which is located in an arid and semi-arid region and suffers from severe water scarcity caused by mismanagement over years and intensified by global warming. This situation has propelled a great deal of population to immigrate towards more developed cities within the country especially towards Tehran. Therefore, the current and future environmental conditions of this city especially its water supply conditions are of great importance. To tackle this complication an outlook for the future precipitation should be provided and appropriate forecasting trajectories compatible with this region's characteristics should be developed. To this end, the present study investigates three training methods namely backpropagation (BP), genetic algorithms (GAs), and particle swarm optimization (PSO) algorithms on a MLP platform. Two frameworks distinguished by their input compositions are denoted in this study: Concurrent Model Framework (CMF) and Integrated Model Framework (IMF). Through these two frameworks, 13 cases are generated: 12 cases within CMF, each of which contains all selected LSCIs in the same lead-times, and one case within IMF that is constituted from the combination of the most correlated LSCIs with Tehran precipitation in each lead-time. Following the evaluation of all model performances through related statistical tests, Taylor diagram is implemented to make comparison among the final selected models in all three optimization algorithms, the best of which is found to be MLP-PSO in IMF.     

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子午岭林区油松密度管理图的编制

利用先进的计算工具，编制了“子午岭油松人工林密度管理图”，经检验达到了精度要求。该较为子午岭林区人工油松林的抚育间伐，生长量预测，造林设计等经营活动提供了科学依据。     

木材横纹压缩大变形应力－应变关系的定量表征

在１００—１３００ｋｇ／ｍ３的密度变异范围选取１７种阔叶树试树，采用典型的６种木材压缩加工条件（气干·２０℃：饱水·２０℃：饱水·１００℃３种条件与约束条件相组合），进行横纹大变形压缩试验和数字化数据采集，经数据分析处理，建立了定量描述木材横纹压缩大变形全领域应力－应交关系的数学表达式。该式的适用性对本试验范围内的各树种、压缩工艺条件均为有效，计算值与实测值十分吻合。     

闽东柳杉人工林林分密度控制图的研究

利用闽东地区的柳杉人工林标准地材料研制林分密度控制图 ,经检验精度满足要求。文中还提出一个新的密度效应模型及估计最大密度线参数的方法 ,在生产上有实际应用价值     

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大规模定制(简称MC)是应对目前市场激烈竞争的一种有效方式,它对企业的柔性能力提出了更高的要求。本文基于资源和能力角度,建立了面向MC提升企业柔性的整体模型,通过分析企业需要向MC转变的程度以及企业已具备的柔性与企业的理想柔性之间的差距,确定企业提升柔性的途径,并利用系统动力学方法给出了相应的因果关系结构图。     

A model of decision-making and information flows for information-intensive agriculture

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