树木花期预报的花芽形态测量法研究——以大山樱花期预报为例

树木花期预报在林果、养蜂、园林和旅游业等方面有很大的实用价值。该文以大山樱(Prunus sargentii)为例,探讨通过花芽形态测量进行花期预报的新方法。通过1998~2000年对北京玉渊潭公园大山樱进行的数据采集和处理,建立了线性和指数两种预报模型。2002年的试报检验表明,采用3株的观测数据,并利用3日滑动平均的方法,对观测数据进行处理后所作的预报,误差在3 d以内的预报达80%以上;2003年连续测报的平均误差,模型1为1.6 d,模型2为2.1 d。这一树木花期预报的物候学新方法,简便易行、建模周期短、预报精度高,在春季芽膨大后,直至露瓣期之前,可以逐日连续发布预报。     

预报微生物学在食品安全风险评估中的作用

随着中国食品工业的发展,食品安全问题日益凸显, 建立一种准确及时的食品安全风险评估是产品市场对食品安全体系提出的挑战。预报微生物学是食品安全风险评估的核心预警技术, 依据建立的预报微生物学模型, 可快速地对食品中的致病菌和腐败菌生长情况进行判断, 对食品中病原微生物和腐败微生物的控制有重要的意义。本文概述了预报微生物学模型的建立和研究现状, 探讨预报微生物学在食品安全风险评估中的应用现状, 概述了预报微生物学模型在食品安全风险评估应用中的发展前景。     

一类灰色项时间序列模型及其应用

利用灰色系统理论与时间序列分析,提出了带灰色项的时间序列模型,对这类模型进行了分析,给出了建模与预报方法,并将其应用于我国农业产值问题的预报与研究之中,模型的正确性得到了检验．     

武汉东湖的磷-浮游植物动态模型

本文报道了东湖的一个富营养化模型。这个模型按照1年的时间标度描述东湖藻类的生长和磷循环,其状态变量包括浮游植物磷、藻类生物量、正磷酸盐、碎屑磷和沉积物磷。模型校准和检验结果表明,模型对于系统给定状态的描述是令人满意的,并且对于系统的强制函数的改变能给予合理的响应。根据东湖富营养化工程治理的初步设想,利用模型进行了东湖污水截流前后的水质预报,同时考查了截流后移出沉积物或引灌江水对于改善其水质的效果。模型所提供的各种预报可供拟定东湖治理方案时参考。     

玉米生长条件下农田土壤水分动态预报方法的研究

依据理论分析和田间实测资料,提出了玉米生长条件下农田土壤水分动态预报模型,论述了模型中参数的测取方法,经初步应用结果表明,该模型预报玉米土壤水分动态具有较高的精度。     

福建省竹蝗灾区区划及其应用研究

根据区划理论,应用数学方法和计算机技术,将福建省竹蝗灾区区划为3个区和3个危险程度等级,并建立Ⅰ、Ⅱ级危险程度等级的发生面积预测预报数学模型。经检验,模型精度较高,拟合效果好,可在生产上推广应用。     

全国大豆食心虫发生面积与大气环流、海温的关系及其长期预测预报

对1952-2001年中国大豆食心虫发生面积与食心虫发生前一年1月至当年4月中74个环流特征量及286个网格点海温逐月及不同组合时段距平值进行相关分析,筛选出了大气环流和海温因子,通过逐步回归,分别建立了中国大豆食心虫发生面积的大气环流和海温预报模型,并对模型进行了拟合和延伸预报.结果表明:模型的拟合和延伸预报效果均较好,2002-2004年大气环流模型延伸预报平均准确率达88%,海温模型延伸预报准确率达86%.     

武汉大学樱花花期长度特征及预报方法

武汉大学樱花是武汉的一张"城市名片",开展樱花花期长度的预报工作,可为旅游部门管理工作和游客安排出行时间提供合理的参考。根据1979—2018年40年武汉大学樱园日本樱花树始花期和落花期的观测资料及同期气象资料的研究分析表明:(1)樱花的始花期和落花期在20世纪80—90年代期间有明显的提前;从20世纪90年代末开始至今,始花期与落花期变化趋势不明显,但变率较大,与全球气候变化停滞期相吻合;40年间花期长度变率很大,整体无明显的增多或减少的趋势。(2)平均始花期为3月14—15日,落花期为3月31日—4月1日,平均花期长度为18d。(3)花期长度与当年始花期日序数、开花期间平均气温、开花期间最高平均气温、最低平均气温和温度日较差平均值均呈负相关,与开花期间总降水量呈正相关。与开花期内平均极大风速值、平均降水量和日照时数等无明显相关性。(4)用1979—2015年共37年资料建立了樱花花期长度的单因子拟合、多因子回归及主成分分析模型,用2016—2018年3年资料进行检验,对武汉大学樱花花期长度进行了预报,取得了较好的试验效果。其中主成分回归模型、降水单因子拟合模型和多因子樱花花期长度回归模型预报效果最好,平均绝对误差在1.5d左右,后期将会把预报模型运用到实际的樱花花期预报工作中。     

水稻二化螟［Chilo suppressalis（Walker）］发生期预测模型的探讨

本文利用福建省永安市１９７８－１９８８年诱虫灯下二化螟蛾发生期资料和气象资料,通过影响因子与预测对象的相关分析来选择初选因子．再应用逐步判别分析方法建立第一代二化螟蛾发生期的预测模型。经对历史资料的回报检验,该模型的判别率达９０．１－１００％;经１９８９和１９９０年实报,预报等级符合实际发生等级。     

我国赤潮预报研究取得重要进展

“十五”国家科技攻关计划重点项目——赤潮灾害预报技术研究取得重要进展,赤潮灾害发生率的预报精度达到33%,比项目任务书要求的预报精度高了8个百分点。该课题由国家海洋局二所承担,课题组长由国家海洋局二所首席研究员黄韦艮、国家海洋局一所丁德文院士担纲,这是我国首次为“赤潮灾害预报技术研究”立项。研究期间,项目课题组利用开发成功的赤潮预报技术与模型,进行了试预报,共发布卫星遥感监测预报30次,赤潮统计与数值预报113次,赤潮发生率的预报精度达到33%,超过了25%的任务书指标。这一成果也通过了“十五”国家科技攻关计划重点项目…     

数学判别模型在预测害虫种群动态上的应用

根据两个总体的Fisher判别准则,建立了预测害虫种群动态的数学判别模型,对山东省惠民县1967～1977年共11年二代棉铃虫发生程度的两类资料进行了数量分析,建立了数学模型：y＝0．0127x1－0．023X2,对历史资料的回代验证与独立样本的预测,符合率在90％以上。     

江苏省通州市稻纵卷叶螟中期预测预报的研究

利用多维时间序列分析方法,对江苏通州市稻纵卷叶螟各代次的发生高峰日、高峰日蛾量及世代累计蛾量提前1～2个月作预测,二年预测结果准确率平均超过85％,为害虫的中期预测预报提供了一种新的应用方法。另外,对如何选择预测预报因子也作了深入讨论。     

白背飞虱种群动态关联分析及预测模型的研究

根据灰色系统关联分析的基本原理,提出了白背飞虱种群动态的加权关联度预测法。衢县早稻后期白背飞虱发生量与历年6月25～30日平均百丛虫量X_1（t）、同期若虫比例X_2（t）、迟熟品种比例X_3（t）、6月下旬水分积分指数X_4（t）和平均气温X_5（t）等因素的关联序为：X_2（t）＞X_1（t）＞X_3（t）＞X_5（t）＞X_4（t）。据此建立的加权关联度预测模型,经12年资料回测和试报验证,结果令人满意。     

Improved Neural Networks with Random Weights for Short-Term Load Forecasting

An effective forecasting model for short-term load plays a significant role in promoting the management efficiency of an electric power system. This paper proposes a new forecasting model based on the improved neural networks with random weights (INNRW). The key is to introduce a weighting technique to the inputs of the model and use a novel neural network to forecast the daily maximum load. Eight factors are selected as the inputs. A mutual information weighting algorithm is then used to allocate different weights to the inputs. The neural networks with random weights and kernels (KNNRW) is applied to approximate the nonlinear function between the selected inputs and the daily maximum load due to the fast learning speed and good generalization performance. In the application of the daily load in Dalian, the result of the proposed INNRW is compared with several previously developed forecasting models. The simulation experiment shows that the proposed model performs the best overall in short-term load forecasting.     

A simple Bayesian network to interpret the accuracy of armyworm outbreak forecasts

Forecasting of outbreaks of armyworm (larvae of the moth Spodoptera exempta) employs information from rain gauges and moth traps. Rainfall is an independent variable, but moth catch is affected by rainfall, and outbreak risk is affected by both moth catch and rainfall. A simple Bayesian network was used to describe these relationships and so derive conditional probabilities. The data were from a new initiative, community‐based forecasting of armyworm in Tanzania, in which outbreak risk for a village is determined locally from a single moth trap and rain gauge located within the village. It was found that, following a positive forecast, an armyworm outbreak was approximately twice as likely to occur as would be expected by chance. If the forecast was negative because of insufficient moths, outbreaks were half as likely as would be expected by chance. If the forecast was negative because of insufficient rain, however, the outbreak probability remained similar to chance: an aspect of the forecast that requires improvement. Overall, a high forecasting accuracy can be achieved by village communities using simple rules to predict armyworm outbreaks.     

Progress and opportunities in advancing near-term forecasting of freshwater quality

Near-term freshwater forecasts, defined as sub-daily to decadal future predictions of a freshwater variable with quantified uncertainty, are urgently needed to improve water quality management as freshwater ecosystems exhibit greater variability due to global change. Shifting baselines in freshwater ecosystems due to land use and climate change prevent managers from relying on historical averages for predicting future conditions, necessitating near-term forecasts to mitigate freshwater risks to human health and safety (e.g., flash floods, harmful algal blooms) and ecosystem services (e.g., water-related recreation and tourism). To assess the current state of freshwater forecasting and identify opportunities for future progress, we synthesized freshwater forecasting papers published in the past 5 years. We found that freshwater forecasting is currently dominated by near-term forecasts of water quantity and that near-term water quality forecasts are fewer in number and in the early stages of development (i.e., non-operational) despite their potential as important preemptive decision support tools. We contend that more freshwater quality forecasts are critically needed and that near-term water quality forecasting is poised to make substantial advances based on examples of recent progress in forecasting methodology, workflows, and end-user engagement. For example, current water quality forecasting systems can predict water temperature, dissolved oxygen, and algal bloom/toxin events 5 days ahead with reasonable accuracy. Continued progress in freshwater quality forecasting will be greatly accelerated by adapting tools and approaches from freshwater quantity forecasting (e.g., machine learning modeling methods). In addition, future development of effective operational freshwater quality forecasts will require substantive engagement of end users throughout the forecast process, funding, and training opportunities. Looking ahead, near-term forecasting provides a hopeful future for freshwater management in the face of increased variability and risk due to global change, and we encourage the freshwater scientific community to incorporate forecasting approaches in water quality research and management.     

芽胞气溶胶表面滞留抗力预测模型研究

针对生物威胁的现场处置工作,建立气溶胶芽胞表面滞留抗力的智能预测模型,以准确预测环境表面芽胞污染状况,为大规模的现场洗消任务提供重要依据,有利于实现及时反应、恰当反应和准确防护的目标。以枯草杆菌芽胞为试验菌,在气溶胶实验室进行芽胞的环境因素暴露及活力测定,以模拟环境中芽胞抗力变化规律数据为依据,采用Matlab6.1软件包中的神经网络工具箱进行抗力预测模型研究。根据研究目的、模拟环境条件和数据训练的平滑曲线等特征,设定了5个输入神经元,8个隐层节点和1个输出神经元。‘tansig’、‘purelin’为传递函数,trainlm为训练函数,网络迭代100次。模型回顾预测效率达到100%,前瞻预测效率达到91%。以实验室数据为依据,利用Matlab平台中的BP神经网络建立的芽胞气溶胶表面滞留抗力预测模型能利用环境因素信息有效预测芽胞抗力。     

宁南山区小麦条锈病发生规律预测预报研究

宁南山区小麦条锈病发生规律预测预报研究谢成君（宁夏西吉县农业技术推广中心，７５６２００）ＳｔｕｄｙｏｆＦｏｒｅｃａｓｔｉｎｇｏｆＯｃｃｕｒｒｅｎｃｅＲｅｇｕｌａｒｉｔｙｏｆＰｕｃｉｎｉａＳｔｒｉｆｏｒｍｉｓＷｅｓｔｉｎＭｏｕｎｔａｉｎＡｒｅａｓｏｆＳ...     

OPTICAL MONITORING AND FORECASTING SYSTEMS FOR HARMFUL ALGAL BLOOMS: POSSIBILITY OR PIPE DREAM?

Monitoring programs for harmful algal blooms (HABs) are currently reactive and provide little or no means for advance warning. Given this, the development of algal forecasting systems would be of great use because they could guide traditional monitoring programs and provide a proactive means for responding to HABs. Forecasting systems will require near real-time observational capabilities and hydrodynamic/biological models designed to run in the forecast mode. These observational networks must detect and forecast over ecologically relevant spatial/ temporal scales. One solution is to incorporate a multiplatform optical approach utilizing remote sensing and in situ moored technologies. Recent advances in instrumentation and data-assimilative modeling may provide the components necessary for building an algal forecasting system. This review will outline the utility and hurdles of optical approaches in HAB detection and monitoring. In all the approaches, the desired HAB information must be isolated and extracted from the measured bulk optical signals. Examples of strengths and weaknesses of the current approaches to deconvolve the bulk optical properties are illustrated. After the phytoplankton signal has been isolated, species-recognition algorithms will be required, and we demonstrate one approach developed for Gymnodinium breve Davis. Pattern-recognition algorithms will be species-specific, reflecting the acclimation state of the HAB species of interest.Field data will provide inputs to optically based ecosystem models, which are fused to the observational networks through data-assimilation methods. Potential model structure and data-assimilation methods are reviewed.     

生态预报: 生态学的一个前沿领域

生态预报对于资源,环境的决策和管理将起着越来越重要的作用。计算机科学和定量分析的进步,生态学理论的发展以及新技术的应用增加了人类预测生态系统变化的能力。本文介绍了有关生态预报的内涵,相关研究进展及案例,提出生态预报是生态学的一个重要前沿领域,也将是今后生态学研究的一个重要努力方向。