Consideration of fuzziness: Is it necessary in modelling fish habitat preference of Japanese medaka (Oryzias latipes)?

The present study aims to clarify the necessity and effectiveness of considering fuzziness in modelling fish habitat preference, and the advantages which would be achieved by considering it. For this purpose, genetic algorithm (GA) optimized habitat preference models under three different levels of fuzzification were compared with regard to prediction ability of the habitat use of Japanese medaka (Oryzias latipes) dwelling in agricultural canals in Japan. Field surveys were conducted in agricultural canals in Japan to establish a relationship between fish habitat preference and physical environments of water depth, current velocity, lateral cover ratio and percent vegetation coverage. The habitat preference models employed for testing the fuzzy-based approach were category model, fuzzy habitat preference model, and fuzzy habitat preference model with fuzzy inputs. All the models were developed at 50 different initial conditions. The effectiveness of the fuzzification in fish habitat modelling was assessed by comparing mean square error and standard deviation of the models, and fluctuation in habitat preference curves evaluated by each model. As a result, the effect of fuzzification appeared as smoother curves and was found to reduce fluctuation in habitat preference curves in proportion to the level of fuzzification. The smooth curves would be appropriate for expressing uncertainty in habitat preference of the fish, by which fuzzy habitat preference model with fuzzy input achieve the best prediction ability among the models. In conclusion, the present study revealed that there are two advantages of fuzzification: reducing fluctuations in habitat preference evaluation and improving prediction ability of the model. Therefore, the consideration of fuzziness would be appropriate for representing fish habitat preference under natural conditions.     

How might we model an ecosystem?

Predicting ecosystem effects is of crucial importance in a world at threat from natural and human-mediated change. Here we propose an ecologically defensible representation of an ecosystem that facilitates predictive modelling. The representation has its roots in the early trophic and energetic theory of ecosystem dynamics and more recent functional ecology and network theory. Using the arable ecosystem of the UK as an example, we show that the representation allows simplification from the many interacting plant and invertebrate species, typically present in arable fields, to a more tractable number of trophic-functional types. Our compound hypothesis is that “trophic-functional types of plants and invertebrates can be used to explain the structure, diversity and dynamics of arable ecosystems”. The trophic-functional types act as containers for individuals, within an individual-based model, sharing similar trophic behaviour and traits of biomass transformation. Biomass, or energy, flows between the types and this allows the key ecological properties of individual abundance and body mass, at each trophic height, to be followed through simulations. Our preliminary simulation results suggest that the model shows great promise. The simulation output for simple ecosystems, populated with realistic parameter values, is consistent with current laboratory observations and provides exciting indications that it could reproduce field scale phenomena. The model also produces output that links the individual, population and community scales, and may be analysed and tested using community, network (food web) and population dynamic theory. We show that we can include management effects, as perturbations to parameter values, for modelling the effects of change and indicating management responses to change. This model will require robust analysis, testing and validation, and we discuss how we will achieve this in the future.     

BP神经网络对蚯蚓滤池处理COD的模拟预测

基于蚯蚓滤池处理去除污染物的非线性特点,利用BP神经网络建立了蚯蚓滤池处理COD的基本模型结构。同时对实验数据进行了验证和预测,通过权值贡献率分析确定了各种输入因素对COD出水浓度的影响。结果表明:COD的出水模型预测值与实际值平均误差较小,模型稳定,预测效果好。输入神经元为4,隐含神经元为8,输出神经元为1,学习速率为0.1,动量为0.1,训练次数为10 000的BP神经网络模型,预测的COD出水值最接近真实值。COD进水浓度对COD出水影响最大,符合理论研究结果。BP神经网络模型建立的成功为后续生活污水智能化控制的研究提供了相应的理论基础。     

铁镁质尾矿中的钙镁浸取效果优化

铁镁质尾矿含有丰富的钙镁,利用浸取剂可以促进铁镁质尾矿中钙镁的释放,使这些尾矿成为固定CO2的材料。尾矿中钙镁的浸出率大小跟多个因素相关,如何寻求浸取钙镁的最优化条件,是有效利用铁镁质尾矿的重要内容。考虑环境友好的方案,采用EDTA、柠檬酸和柠檬酸钠浸取尾矿中的钙镁,并对比了不同条件下的浸取效果。通过比较,显示柠檬酸钠的浸取效果较好,在此基础上利用响应面法对粒度、pH值、固液比等主要影响因子进行优化处理。当反应液的pH=1,尾矿粒度<75μm,固液比为2.6∶100时,模型预测实验尾矿淋滤的Mg2+浓度为143.4 mg/L,Mg达到最佳溶出率2.03%。实验模型的预测精度良好,表明响应面法适用于尾矿的浸取实验优化。     

Prediction of global solar radiation using support vector machines

This article utilizes Support Vector Machines (SVM) for predicting global solar radiation (GSR) for Sharurha, a city in the southwest of Saudi Arabia. The SVM model was trained using measured air temperature and relative humidity. Measured data of 1812 values for the period from 1998–2002 were obtained. The measurement data of 1600 were used for training the SVM, and the remaining 212 were used for comparison between the measured and predicted values of GSR. The GSR values were predicted using the following four combinations of data sets: (i) Daily mean air temperature and day of the year as inputs, and global solar radiation as output; (ii) daily maximum air temperature and day of the year as inputs, and GSR as output; (iii) daily mean air temperature and relative humidity and day of the year as inputs, and GSR as output; and (iv) daily mean air temperature, day of the year, relative humidity, and previous day’s GSR as inputs, and GSR as output. The mean square error was found to be 0.0027, 0.0023, 0.0021, and 7.65 × 10^?4 for case (i), (ii,), (iii), and (iv) respectively, while the corresponding absolute mean percentage errors were 5.64, 5.08, 4.48, and 2.8%. Obtained results show that the SVM method is capable of predicting GSR from measured values of temperature and relative humidity.     

基于集对分析的降水酸度及水质相似预测模型研究

应用集对分析法刻画历史样本之间的相似性,用多个最相似的历史样本的加权平均值作为当前样本预测值,建立了基于集对分析的降水酸度及水质相似预测模型(SFM-SPA),并运用该模型进行了降水酸度及水质预测的实例验证。结果表明,在主要影响因子选择适当、历史样本的代表性和相似性较好等限定条件下,利用模型进行环境预测是可行的,它较直观、计算简便,为环境污染预测提供了新的途径。     

Prediction of Crop Production using Drought indices at Different Time Scales and Climatic Factors to Manage Drought Risk1

Sadat Noori, S.M., A.M. Liaghat, and K. Ebrahimi, 2011. Prediction of Crop Production Using Drought Indices at Different Time Scales and Climatic Factors to Manage Drought Risk. Journal of the American Water Resources Association (JAWRA) 48(1): 1‐9. DOI: 10.1111/j.1752‐1688.2011.00586.x Abstract: Drought causes great damage to rainfed and irrigated farming. Therefore, prediction of crop production during the drought period is essential in order to manage drought risk. Thus, proceeding to agricultural drought risk management can be very useful. This study shows the results of early crop prediction using the combination of climate factors and drought indices at different time scales. The study region was Hamadan, a semiarid region in Iran. The methodology demonstrated here has allowed the prediction of production several months before harvest. Moreover, the predictive models constructed have explained 89% of the temporal variability of wheat production. This method could be very efficient for managing crop production. Moreover, having clear prediction, decision makers can plan better for overcoming drought impacts to reduce crop uncertainty for farmers in insurance companies.     

高分辨率遥感图像在城市快速震害预测中的应用研究

以河南省某县城为例,通过分析高分辨率遥感图像,获取建筑物的属性信息,结合相关调查数据,建立震害矩阵,得到震害预测结果,计算综合地震危险性指数,评价该区域综合抗震能力。与实际调研结果对比后发现,基于高分表率遥感图像的建筑物信息提取技术具有较高精度,能够满足大范围快速震害预测的需要。     

基于仿真分析的复杂电子装备测试性预计技术研究

测试性是产品的一种设计特性,是伴随功能原理设计时赋予产品的一种固有属性。随着电子产品及设备的复杂和多样化,导致其故障诊断越来越困难,产品的测试性也逐渐得到生产和使用单位的重视。为尽早在研制阶段对产品的测试性水平进行评估,发现测试性设计的薄弱环节,首先对测试性仿真技术进行了简单介绍,之后详细阐述了基于TEAMS的测试性仿真方法,最后以某型柴油机系统为例,对其测试性指标进行预计,同时发现产品测试性设计上的薄弱环节,为产品的设计改进提供建议。     

数据分解模式下PM2.5与气态污染物的组合预测研究

大气污染治理是我国实现生态文明的必经之路,制定有效性的大气治理方案,作为参考的大气污染物月均浓度预测结果是至关重要的.针对大气环境污染物月均浓度时间序列的高噪音、非平稳和非线性等特点,本文提出一种基于数据分解模式的组合预测模型.上海市的实例验证及与其他3种模型的对比研究表明：本文所提出的组合预测模型适用于政策制定所需但样本量受限的月均或年均数据预测;所提出的子序列重构的新模式比传统求和算法重构模式提高预测精度12.5%;相较于其他模型,其预测性能最优（绝对百分比误差的均值仅为9.05,且对历史拟合的皮尔逊系数均为0.90以上）.实现了对大气污染物月均浓度高精度预测,可为相关政策的制定提供科学的定量参考.