The integrated modeling system STONE for calculating nutrient emissions from agriculture in the Netherlands

For the Netherlands, a nutrient emission modeling system, called STONE, has been developed. It was designed for evaluation at the national and regional scale of the effects of changes in the agricultural sector (e.g. changes in fertilizer recommendations and cropping patterns) and in policy measures (e.g. EU nitrate directive for ground water) for the leaching of nitrogen (N) and phosphorus (P) from agricultural land areas to ground water and surface waters. STONE consists of a chain of models, which are applied subsequently to a large number (6405) of unique units that represent the variation in biophysical conditions in the Netherlands. This paper discusses the main components of the STONE model chain, covering manure excretion and distribution, NH₃ emission and deposition, N and P uptake by crops, transport and immobilization of N and P in soils, and leaching of N and P to surface and ground water. The plausibility of the results from STONE is studied by analyzing the approach and calibration of the different models within STONE and the validity of the models’ results. An overview of weak and strong components within STONE is presented. It was found that computed results on nutrient leaching to ground and surface waters from STONE compare fairly well with observations. A number of aspects that may limit the plausibility of the results generated by STONE are discussed. The models’ capability is illustrated by results from an application. In this study the effects of a number of possible policy measures on fertilizer use within Dutch agriculture are explored for the coming 30 years. The computed future nutrient emissions indicate the efficacy of various policy measures and the location of eutrophication-sensitive areas in the Netherlands.     

Using inductive learning to assess compound feed production in cooperative poultry farms

Production scheduling is one of the most important functions in a production company. As a consequence, in recent decades various methods have been proposed for the modeling and solution of particular scheduling problems. In this context, a special case is that of centralized feed manufacturing plants supplying animal food in a cooperative poultry environment. In this paper, we present the SP4 system, an integrated software environment that combines a statistical method (used to calculate the previous consumption data, mortality indices and feed delivery types), a machine learning method (M5P and IBk models – used to calculate the total amount of feed consumed by type) and an ad hoc algorithm which makes flexible orders for compound feed production forecasting. The data used for this study was provided by a leading Spanish Company (Coren Cooperative) specialized in animal feed production and delivery. Raw data (from the years 2007 and 2008) was built from client orders, company production logs, information about the number of animals at different farms and truck trips to the clients. To ensure that the developed system is able to reproduce acceptable results for the unseeable future, we have evaluated various aggregate measures to forecast error (MSE, MAE, MAPE, ME) during the validation of the models. The results reveal that the proposed system performed well, being able to track the dynamic non-linear trend and seasonality, as well as the numerous interactions between correlated variables.     

Finding an optimum location for biogas plant: a case study for Duzce,Turkey

This study is a case study for modelling and solving a real-life problem. In this study, a practical approximation for finding an optimum location of a foundation was realized with k-means clustering and optimization. Duzce, in the northwest of Turkey, has been researched for the biogas potential to found biogas plant. With this aim, the number of poultry in Duzce has been determined and presented their potential of biogas. Since the number of poultry is quite enough to found a biogas plant, later the location of the poultry farms and their potentials has been determined. Since there are more than 400 poultry farms in Duzce, firstly locations are clustered with classical k-means algorithm. k is specified as 6–8 with an expert knowledge. Later, the nearest location for each cluster center has been attained with simulated annealing with the objective of minimizing the transportation cost. As a result, it has been determined an optimum location for probable biogas plant for Duzce.

     

基于改进AFSA算法的河流突发水污染溯源

污染源的排放时间、排放位置以及排放总量的确定是河流突发性水污染溯源问题的关键.如何快速准确的确定重金属污染源的三个因素, 然后通过GIS得到污染源企业的排查清单是本文的研究重点.本文通过对河流的水文水质的研究, 依据重金属污染物的特性, 确定了一维河流重金属污染物的时空变化的动态解析解.同时构造了污染物时空溯源模型以及污染物排放总量模型, 并利用改进的AFSA算法实现了模型的求解.研究结果表明, 该算法使得模型能够更加快速准确地得到三个参数的结果, 然后将该结果通过本文的方法并且借助GIS技术更快更准确的为相关工作人员提供污染源企业概率排查清单.本文提出的方法和模型对于水污染处理以及保护水环境具有一定的指导意义.     

基于PRM的水体富营养化风险分析建模

从信息的知识发现角度出发,提出基于概率关系模型(PRM)的水体富营养化风险分析建模方法。该建模方法利用多关系数据的存储结构和存储内容对数据进行学习与挖掘,构建具有网络拓扑结构的PRM模型。示例分析结果表明,PRM模型易于解释与分析水体中各种影响因素间的相关性,该建模方法可通过分析历史数据发现水体富营养化的潜在风险,为库区水环境管理与水污染防治提供科学依据。     

Atmospheric signatures in sea-ice concentration estimates from passive microwaves: Modelled and observed

The effects of weather systems on sea-ice concentration retrieval are investigated using an advanced radiative transfer model with input data from 155radiosonde ascents together with satellite and ground based observations in the Weddell Sea in 1992. The results of the model study indicate that, using the SSM/I NASA Team algorithm, cloud liquid water increases estimates of total sea-ice concentration by the same magnitude as water vapour, i.e., up to 10 per cent, depending on surface type (open ocean, first-year ice, multiyear ice), and actual concentration. Estimates of the multiyear ice concentration are reduced by up to 80 per cent by cloud liquid water whereas the water-vapour effect is smaller (up to 6 per cent). The combined effect is less than the sum of the two. Calculations using the SMMR sea-ice algorithm were made for comparison with previous estimates by Pedersen and Maslanik. In this case study, estimates of the multiyear fraction show a smaller reduction by water vapour and a larger reduction by cloud liquid water, whereas the total concentration change is in between the two previous results.The algorithm for the SSM/I radiometer exhibits stronger effects on total ice concentration due to water vapour and cloud liquid water than that for SMMR, and atmospheric effects using the future MIMR radiometer sea-ice algorithm will be in between those from SMMR and SSM/I. Different calculated ice-concentration changes for the SSM/I due to different sets of tiepoints (emissivities) can be of the same order of magnitude as the atmospheric effect of cloud liquid water. Comparison between these modelled effects and satellite-derived concentrations from SSM/I shows good geographical and quantitative agreement in areas with extensive frontal water clouds.     

Modeling historical fecal coliform loadings to large European rivers and resulting in-stream concentrations

Information on fecal coliform (FC) concentrations in European rivers is scarce. The objective of this study was to identify hotspots of water pollution in Europe and provide information on the different FC sources and their contributions to the loads that lead to concentrations in rivers. Model simulations were carried-out with the large-scale water quality model WorldQual to assess the calculated loads regarding to its associated sources and to further estimate the related in-stream concentration. For the year 1995, model results indicated that FC loadings were higher in central Europe with 500 to above 2000 10¹⁰ cfu km⁻² a⁻¹ than in northern and eastern Europe where loadings ranged between 0 and 200 10¹⁰ cfu km⁻² a⁻¹. Major sources of FC loadings are domestic sewage, followed by scattered settlements (private treatment), urban surface runoff and manure application. Concentrations showed similar regional patterns as loadings, with high concentrations in central Europe and low concentrations in northern and eastern Europe.     

地下水空间信息可视化分析技术研究

将虚拟现实与可视化空间分析技术应用于地下水科学管理工作可以弥补传统技术在分析研究、辅助决策中的不足,发现仍有空间数据模型、空间数据库、合适的虚拟现实方法以及高效的可视化空间分析算法等问题有待解决。结合实际研究与应用工作,探讨了地下水及其赋存地质体的空间数据建模与数据库存储技术,基于OpenGL图形库实现了地下水、地质体、相关地表信息、等水位线、地下水流向等的3维可视化,设计并实现了基于纵断面快速生成算法的可视化空间分析功能。以苏锡常地区为研究示范区,开发了"苏锡常地区地下水空间信息可视化虚拟分析系统"软件,在实际地下水科学管理工作中取得了明显效果。     

基于连接模型的局部优化算法在水域污染监测数据融合系统中的应用

针对水域污染监测数据融合系统中存在的困难，讨论了基于“连接”模型的局部优化算法及其应用。该模型采用非“抑制”连接，极大地减少了节点“连接”数和扇出数；各个节点只和相邻节点通过“连接”传递信息，竞争输出，保证了局部最优，同时为实现分布式计算提供了方便。在此模型的基础上本文用局部优化及其改进算法对一个水域污染监测问题进行了仿真研究，理论分析和计算结果表明，局部最优及其改进算法在保证搜索准确性的同时极大地减少了计算量，是解决水域污染监测问题的有力工具。     

基于改进鸟群算法优化聚类的风电场等值建模

为了提高风电场动态等值建模的精确度,采用风力发电机组的12个状态变量作为分群指标,使用改进鸟群算法(Improve Bird Swarm Algorithm,IBSA)搜索最佳聚类中心,通过K-means算法对风电场进行分群处理。在MATLAB/Simulink中搭建详细模型与等值模型,并与传统聚类算法进行对比。实验结果表明,该方法对等值建模的准确度有很大的提高,可以精确地表征风电场的对外特性。     

Optimization-based scheduling for the single-satellite,multi-ground station communication problem

In this paper, we develop models and algorithms for solving the single-satellite, multi-ground station communication scheduling problem, with the objective of maximizing the total amount of data downloaded from space. With the growing number of small satellites gathering large quantities of data in space and seeking to download this data to a capacity-constrained ground station network, effective scheduling is critical to mission success. Our goal in this research is to develop tools that yield high-quality schedules in a timely fashion while accurately modeling on-board satellite energy and data dynamics as well as realistic constraints of the space environment and ground network. We formulate an under-constrained mixed integer program (MIP) to model the problem. We then introduce an iterative algorithm that progressively tightens the constraints of this model to obtain a feasible and thus optimal solution. Computational experiments are conducted on diverse real-world data sets to demonstrate tractability and solution quality. Additional experiments on a broad test bed of contrived problem instances are used to test the boundaries of tractability for applying this approach to other problem domains. Our computational results suggest that our approach is viable for real-world instances, as well as providing a strong foundation for more complex problems with multiple satellites and stochastic conditions.     

采用遥感影像的林地建模研究

提出了一种全新的林地建模方法,对生长着树木的林地获取地面序列影像,利用影像分割实现树木信息的自动识别,通过基于地面区域约束的点云提取算法获得地面高精度密集点云,最后应用三次Beta样条曲线插值模型实现了林地等高线的建模。该方法将摄影测量技术应用于林地建模,解决了无法对有树木遮挡的地面进行建模的难题,提高了林地建模的效率。     

基于OSG的虚拟场景中包围盒碰撞检测的研究

碰撞检测是虚拟场景的核心技术,其效果的好坏直接影响整个虚拟场景的真实感。基于OSG（OpenSceneGraph）三维场景渲染引擎和Multigen Creator三维可视化仿真建模软件,采用包围盒碰撞检测算法,实现了多个静态物体与动态物体的碰撞检测,通过使用射线检测算法可以很好地解决静态物体与地面以及动态物体与地面的碰撞检测。为了满足实时性的要求,提出了多个静态物体与动态物体的碰撞检测优化算法的数学模型。结果表明,算法具有很好的有效性和快速性,能够满足要求。     

Analysis of environmental and economic efficiency using a farm population micro-simulation model

New Zealand's success in raising agricultural productivity has been accompanied by higher input use, leading to adverse effects on the environment. Until recently, analysis of farm performance has tended to ignore such negative externalities. The current emphasis on environmental issues has led dairy farmers to target improvements in both environmental performance and productivity. Therefore, measuring the environmental performance of farms and integrating this information into farm productivity calculations should assist in making informed policy decisions which promote sustainable development. However, this is a challenging process since conventional environmental efficiency measures are usually based on simple input and output flows but nitrogen discharge is a complex process affected by climate, pasture composition, cow physiology and geophysical variability. Furthermore, the outdoor pastoral nature of New Zealand farming means that it is difficult to control input and output flows, particularly of nitrogen. We present a novel approach to measure the environmental and economic efficiency of farms, using the Overseer nutrient budget model and spatially micro-simulated virtual population data. The empirical analysis is based on dairy farms in the Karapiro catchment, where nitrogen discharge from dairy farming is a major source of nonpoint pollution.     

基于匹配跟踪的感知梯度正弦建模方法

匹配跟踪作为一种自适应的信号分解算法,为语音和音频正弦建模提供了一个新的框架.分析了基于匹配跟踪的正弦建模过程以及感知加权匹配跟踪正弦建模算法,并在此基础上提出了感知梯度正弦建模方法.该方法结合匹配跟踪自适应的动态特征,利用心理声学模型计算当前合成信号的动态掩蔽阈值,以此为参考提取残差信号中感觉最明显的信号分量,从而最大限度地增加合成信号中的感知信息.在模型精度不高的情况下,该方法也能得到合成质量比较高的语音.实验表明,该方法更好地利用了人耳的听觉特性,建模结果更为合理、有效.客观的信噪比和主观试听测试都显示了所提出算法的合理性与优越性.     

限定TIN与CSG集成仿真模型生成算法研究

为了有效地组织和管理三维GIS空间数据，实现对三维空间实体高效、完整地表达，提出了限定不规则三角网（TIN）与构造实体几何（CSG）集成的仿真建模算法。采用TIN模型描述地形，CSG模型描述建筑物，通过抽取建筑物地面轮廓线作为地形三角剖分时的限定约束条件，将两种模型有机集成在一起。同时，实现了两类模型可视化渲染操作的同步进行。仿真试验结果表明，算法在保证模拟精度的前提下，能有效地简化建模过程，在一定程度上降低了可视化渲染计算的复杂度。     

一种新的基于体元剖分的软组织切割算法

切割是虚拟手术系统中需要表现的真实动作之一,真实感和实时性是衡量切割效果的关键．因此有必要研究一种新的基于体元剖分的软组织切割算法．该算法不同于以往的体元剖分算法,它将整个剖分过程分两步来完成：首先通过退化处理过程将不完全切割的四面体单元分解成多个完全切割的四面体单元;然后将切割情况进行分类,根据不同的切割情况分别进行相应的基元分解．实验表明,研究的新算法用于四面体单元的有限元模型,具有良好的切割处理效果．     

A hybrid neural network and ARIMA model for water quality time series prediction

Accurate predictions of time series data have motivated the researchers to develop innovative models for water resources management. Time series data often contain both linear and nonlinear patterns. Therefore, neither ARIMA nor neural networks can be adequate in modeling and predicting time series data. The ARIMA model cannot deal with nonlinear relationships while the neural network model alone is not able to handle both linear and nonlinear patterns equally well. In the present study, a hybrid ARIMA and neural network model is proposed that is capable of exploiting the strengths of traditional time series approaches and artificial neural networks. The proposed approach consists of an ARIMA methodology and feed-forward, backpropagation network structure with an optimized conjugated training algorithm. The hybrid approach for time series prediction is tested using 108-month observations of water quality data, including water temperature, boron and dissolved oxygen, during 1996–2004 at Büyük Menderes river, Turkey. Specifically, the results from the hybrid model provide a robust modeling framework capable of capturing the nonlinear nature of the complex time series and thus producing more accurate predictions. The correlation coefficients between the hybrid model predicted values and observed data for boron, dissolved oxygen and water temperature are 0.902, 0.893, and 0.909, respectively, which are satisfactory in common model applications. Predicted water quality data from the hybrid model are compared with those from the ARIMA methodology and neural network architecture using the accuracy measures. Owing to its ability in recognizing time series patterns and nonlinear characteristics, the hybrid model provides much better accuracy over the ARIMA and neural network models for water quality predictions.     

A fast SAR image segmentation method based on improved chicken swarm optimization algorithm

Severe speckle noise existed in synthetic aperture radar (SAR) image presents a challenge to image segmentation. Though some traditional segmentation methods for SAR image have some success, most of them fail to consider segmentation effects and segmentation speed at the same time. In this paper, we propose a novel method of SAR image fast segmentation which is based on an improved chicken swarm optimization algorithm. In this method, the positions of the whole chicken swarm are firstly initialized in a narrowed foraging space. Secondly, the grey entropy model is selected as the fitness function of the improved chicken swarm optimization algorithm. Hence, the optimal threshold value is located gradually and quickly by virtue of the foraging behaviors of chicken swarm with a hierarchal order. Experimental results show that our method is superior to some segmentation methods based on genetic algorithm, artificial fish swarm algorithm in convergence, stability and segmentation effects.     

Modified genetic programming combining with particle swarm optimization and performance criterion in solar cell fabrication

This study describes the design and development of the novel model for the process optimization of solar cell fabrication. The model performance can affect the result of the physical experiment in the solar cell fabrication because the high accuracy model can provide the closer result to the output efficiency of the physical experiment. In this study, genetic programming (GP) based modeling technique was developed for the process simulation. GP is a global modeling technique, so it is suitable for process data modeling. This study describes the modified GP algorithm to solve the constant terminal problem. In the traditional GP, the constant term can be randomly selected within the fixed range when the structure is changed. Therefore, the variation ratio of the constant is too low to fit the model well. In this study, the novel GP is proposed. The method includes particle swarm optimization (PSO) to optimize the constant term in the terminals. PSO is a strong searching algorithm without a high computation cost. Actually, through the simulation results, the modeling performance and speed can be improved by the proposed GP. Because by the proposed modeling method, the structure and parameters of the model can be optimized simultaneously, the proposed method can be used as the new global modeling approach.