多元自适应样条回归预报浮游植物总量分析

在浮游植物总量与环境因子的定量关系研究中,使用了多元自适应样条回归模型。基于2003年5-9月渤海湾地区浮游植物总量及各种环境因子的实测数据,经过与投影寻踪回归模型预报结果对比,表明多元自适应样条回归很好地反映了浮游植物总量与环境因子定量关系并且是预报赤潮的较好模型。     

赤潮预测的人工神经网络方法初步研究

赤潮是一种由多因素综合作用引发的生态异常现象,具有突发性及非线性等特点。对其进行预测预报一直是海洋科学研究的热点。探讨了应用人工神经网络原理进行赤潮预测的方法,简要介绍了BP和RBF算法的基本原理,用2种算法对不同海域赤潮生物与环境因子之间非线性和不确定性的复杂关系进行学习训练和预测检验,并与传统的统计方法进行了比较。结果表明：人工神经网络方法在模拟和预测方面优于传统的统计回归模型,具有较强的模拟预测能力及实用性,值得进一步探索。     

基于深度学习的赤潮发生预报方法研究

赤潮作为海洋灾害,对海洋渔业、生态、经济,以及人类生产、生活造成了严重影响。一直以来,赤潮受到研究者的广泛关注,但由于它的形成机制比较复杂,使得赤潮预报极具挑战性。针对赤潮预报的研究问题,本文收集了厦门海域赤潮发生前后的海洋监测数据,结合皮尔逊相关系数、散布矩阵、复相关系数方法,分析多环境因子与赤潮发生多要素的关联情况,重点采用基于深度学习的LSTM与CNN融合方法,挖掘环境因子的时序依赖,发现序列数据的局部特征,对赤潮发生进行预报。在厦门一号和厦门二号数据集中,本方法在预报未来12 h内的赤潮情况时,RMSE、MAE误差分别达到0.521 8、0.504 3。通过协同对比模型进一步确定赤潮发生的预报概率,在两个数据集上的最终预报准确率分别为67.58%和63.49%。本研究为赤潮的分析预报提供了探索经验,证明了将深度学习方法应用于赤潮预报的可行性。     

基于空间自回归和空间聚类的渔情预报模型

作者针对远洋渔场渔情预报精度偏低的问题,提出一种基于空间自回归和空间聚类的渔情预报模型。该模型利用空间自回归对收集到的渔业历史数据进行预处理,然后通过空间聚类将所有数据样本根据地理位置分划成若干个区域,最后研究每个区域中环境数据与渔获数据之间的数学关系,各自建立栖息地适宜性指数模型(Habitat Suitability Index,HSI),并以印度洋大眼金枪鱼(Thunnus obesus)为例进行验证。结果表明,本模型的均方差为0.1742,与传统线性回归方法的均方差0.2363相比,能更好地表达海洋环境数据与渔获量之间的关系,预测精度显著提高。     

钱塘江河口风暴潮经验预报

钱塘江河口的风暴潮预报工作可归结为澉浦或乍浦两个单站的预报,这使得经验预报成为可能。利用一种动力线性模型将动力学的线性问题转化为统计学的线性回归模型,通过合理选取预报量及预报因子,并采用正交筛选技术确定每个预报因子所对应的系数,建立经验预报方程。后报结果表明该方法可取得较好的效果。     

基于C/S架构的业务化赤潮漂移扩散预报

以风场、三维海流场数值预报结果作为输入强迫,建立了赤潮漂移扩散数值预报模型,并开发了相应的软件模块。模块基于C/S(客户端/服务器端)架构,通过数值模拟技术以及GIS、WebServices等信息技术,实现"提交预报请求—数值模式计算—预报结果可视化—预报产品生成"自动化赤潮漂移与扩散预报工作流程。该预报模块具备数据预处理、人机交互参数输入、数值模式计算、预报结果可视化和预报产品制作功能。以长江口附近海域历史赤潮为例进行后报试验,预测了赤潮藻团在风与流场共同作用下的漂移路径变化,结果与实际监测情况一致。该模块的业务化应用能进一步完善现有的赤潮预报系统,将成为赤潮防灾减灾的有力工具。     

二维赤潮生态数学模型及其在渤海的应用

采用ADI的有限差分方法对不可压缩流体二维浅水环流方程离散和求解,建立水动力数学模型,用迎风格式离散赤潮生物动力学方程,通过水动力学和生物动力学相结合的方法,建立了二维赤潮生态数学模型。将所建立的二维赤潮生态数学模型应用于渤海,针对渤海海域2004年6月11～16日发生的棕囊藻赤潮进行了数值计算。对EOS/MODIS卫星拍摄的2004年6月份的渤海海区卫星遥感图像进行了处理,提取出海水中的赤潮信息,并计算出赤潮面积,使其与模型计算出的赤潮面积进行对比验证,结果基本吻合,表明该模型能够较好地模拟赤潮的生消过程,为渤海地区的赤潮预报提供了科学依据。     

基于Logistic回归的海州湾赤潮环境要素阈值研究

以海州湾2004-2006年赤潮实测资料和同期的水文气象资料为研究对象,通过构建多元logistic回归模型,从各种环境要素中提取出对赤潮暴发有显著影响的无机氮(DIN)、pH值、盐度、水温、硅酸盐和风速等环境要素.根据样本分布状况确定每个环境要素阈值的遍历范围和遍历步长,采用含哑变量的logistic回归模型对有显著影响的环境要素值进行遍历,根据回归的结果建立风险度和相对风险度等指标,并依据这些指标筛查出与赤潮暴发显著相关的各环境要素的阈值分别为:DIN:0.09 mg/L;pH值:8.09;盐度:28.4;水温:25.6 ℃;硅酸盐:0.24 mg/L;风速:3.2 m/s,为在该海域控制赤潮的危害提供一种简便有效的手段.     

西北太平洋热带气旋强度统计动力预报的改进模型

利用NCEP再分析资料、T106L19模式产品和热带气旋历史观测资料,设计和筛选气候持续性因子和动力因子,结合主分量因子分析技术,对统计-动力模式进行了改进,开展了西北太平洋热带气旋强度预报技术研究试验。结果表明,对预报因子进行主分量分析,可提高因子的独立性,降低线性回归模型的维数和不稳定性,从而提高了模式对热带气旋强度的实际预报能力。     

赤潮随机梯度回归分析

赤潮的危害日益严重,为了预测赤潮的发生,运用回归树的随机梯度Boosting算法分析渤海赤潮数据, 建立浮游植物总量与环境因子的定量关系,给出各种环境因子对浮游植物总量相对影响的大小以及浮游植物总量和各种环境因子偏相关的图形,有利于探索赤潮的发生机制,指导菌种的培养. 最后,相比其它算法,回归树的随机梯度Boosting对于＂局部剧增＂的赤潮数据是稳健的,而且具有较高的预测精度.     

Evaluation of Inference Adequacy in Cumulative Logistic Regression Models: An Empirical Validation of ISWRidge Relationships

Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ridge height and potential energy during wave-ridge interaction with a binary and cumulative logistic regression model. In testing the Global Null Hypothesis, all values are p＜0.001, with three statistical methods, such as Likelihood Ratio, Score, and Wald. While comparing with two kinds of models, tests values obtained by cumulative logistic regression models are better than those by binary logistic regression models. Although this study employed cumulative logistic regression model, three probability functions 1, 2 and 3, are utilized for investigating the weighted influence of factors on wave reflection. Deviance and Pearson tests are applied to check the goodness-of-fit of the proposed model. The analytical results demonstrated that both ridge height (X1) and potential energy (X2) significantly impact (p＜0.0001) the amplitude-based reflected rate; the P-values for the deviance and Pearson are all ＞0.05 (0.2839, 0.3438, respectively). That is, the goodness-of-fit between ridge height (X1) and potential energy (X2) can further predict parameters under the scenario of the best parsimonious model.Investigation of 6 predictive powers (R2, Max-rescaled R2, Somers'D, Gamma, Tau-a, and c, respectively) indicate that these predictive estimates of the proposed model have better predictive ability than ridge height alone, and are very similar to the interaction of ridge height and potential energy. It can be concluded that the goodness-of-fit and prediction ability of the cumulative logistic regression model are better than that of the binary logistic regression model.     

Evaluation of Inference Adequacy in Cumulative Logistic Regression Models： An Empirical Validation of ISW-Ridge Relationships

Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ridge height and potential energy during wave-ridge interaction with a binary and cumulative logistic regression model. In testing the Global Null Hypothesis, all values are p<0.001, with three statistical methods, such as Likelihood Ratio, Score, and Wald. While comparing with two kinds of models, tests values obtained by cumulative logistic regression models are better than those by binary logistic regression models. Although this study employed cumulative logistic regression model, three probability functions p^1, p^2 and p^3, are utilized for investigating the weighted influence of factors on wave reflection. Deviance and Pearson tests are applied to check the goodness-of-fit of the proposed model. The analytical results demonstrated that both ridge height (X1) and potential energy (X2) significantly impact (p<0.0001) the amplitude-based reflected rate; the P-values for the deviance and Pearson are all >0.05 (0.2839, 0.3438, respectively). That is, the goodness-of-fit between ridge height (X1) and potential energy (X2) can further predict parameters under the scenario of the best parsimonious model.Investigation of 6 predictive powers (R2, Max-rescaled R2, Somers'D, Gamma, Tau-a, and c, respectively) indicate that these predictive estimates of the proposed model have better predictive ability than ridge height alone, and are very similar to the interaction of ridge height and potential energy. It can be concluded that the goodness-of-fit and prediction ability of the cumulative logistic regression model are better than that of the binary logistic regression model.     

Evaluation of Inference Adequacy in Cumulative Logistic Regression Models:An Empirical Validation of ISW-Ridge Relationships

Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ridge height and potential energy during wave-ridge interaction with a binary and cumulative logistic regression model. In testing the Global Null Hypothesis, all values are p＜0.001, with three statistical methods, such as Likelihood Ratio, Score, and Wald. While comparing with two kinds of models, tests values obtained by cumulative logistic regression models are better than those by binary logistic regression models. Although this study employed cumulative logistic regression model, three probability functions 1, 2 and 3, are utilized for investigating the weighted influence of factors on wave reflection. Deviance and Pearson tests are applied to check the goodness-of-fit of the proposed model. The analytical results demonstrated that both ridge height (X1) and potential energy (X2) significantly impact (p＜0.0001) the amplitude-based reflected rate; the P-values for the deviance and Pearson are all ＞0.05 (0.2839, 0.3438, respectively). That is, the goodness-of-fit between ridge height (X1) and potential energy (X2) can further predict parameters under the scenario of the best parsimonious model.Investigation of 6 predictive powers (R2, Max-rescaled R2, Somers''D, Gamma, Tau-a, and c, respectively) indicate that these predictive estimates of the proposed model have better predictive ability than ridge height alone, and are very similar to the interaction of ridge height and potential energy. It can be concluded that the goodness-of-fit and prediction ability of the cumulative logistic regression model are better than that of the binary logistic regression model.     

病态半参数回归模型的岭估计求解

针对半参数回归模型求解过程可能出现的法方程病态问题,提出了用岭估计原则改进半参数模型的求解。通过模拟算例将岭估计解法和其他方法进行了比较,结果表明,岭估计解法能较好地解决半参数回归模型求解过程中的病态问题。     

灾害天气对日照市电力事故的影响研究

利用雷雨、大风等灾害天气资料和电力事故历史数据资料,分析了电力事故发生的时空分布特征及其与雷雨、大风、日平均气温等天气要素之间的关系。进而利用事件概率回归(regression estimation of event probability,REEP)和Logistic回归分析方法,得到了日照市电力事故发生概率与雷雨、大风和日平均气温之间关系的预警模型。研究结果表明:1)雷雨、大风是造成日照市电力事故的重要气象因素。2)雷雨、大风和高温等灾害天气对电力事故的发生虽都有促成作用,但影响能力存在较大差距。3)两种回归分析模型对因子和变量之间关系均有较好的拟合效果,相较而言,REEP模型更为直观,Logistic回归分析方法更为客观,适用性更强。4)回归分析结果建立在客观资料基础上,回归模型具有准确性、实用性,可为电力事故预警发布系统提供理论和技术支持。     

辽河口湿地生态景观格局形成机制分析

利用2007年景观格局图、DEM数据、人口、GDP等数据,运用地理信息系统(GIS)和Logistic回归分析模型相结合的分析方法,揭示辽河口湿地景观格局形成机制。结果表明:建筑用地、芦苇地、水稻田和养殖区的Logistic回归模型具有较好的拟合优度。模拟结果表明,转为建筑用地的Logistic回归模型的重要的解释变量是农村人口密度和城镇人口密度;转为芦苇地的Logistic回归模型的重要的解释变量是农村人口密度和过境水资源量;转为水稻田和养殖区的Logis-tic回归模型的重要的解释变量都是农村人口密度和第一产业值。在这4种生态景观格局的二元Logistic回归模型中最重要的解释变量都是农村人口密度,这表明辽河口湿地景观格局形成最主要的驱动因素是农村人口密度。     

地图扫描数字化系统误差分析及对策探讨

较全面地分析了地图扫描数字化的系统误差来源及影响。在系统误差纠正模型的选取过程中所采用的数据存在粗差,纠正模型的误差和模型数据的粗差是值得讨论的问题。利用粗差拟准检定法探测粗差准确的优点和判断函数模型的准则理论,提出顾及粗差的系统误差回归函数分析,先确定回归函数,在此基础上进行粗差探测与剔出,然后再进行回归分析,以达到消弱系统误差最优的目的。     

基于回归模型的热带气旋条件下的海表降温参数化方案设计

Combining a linear regression and a temperature budget formula, a multivariate regression model is proposed to parameterize and estimate sea surface temperature(SST) cooling induced by tropical cyclones(TCs). Three major dynamic and thermodynamic processes governing the TC-induced SST cooling(SSTC), vertical mixing, upwelling and heat flux, are parameterized empirically using a combination of multiple atmospheric and oceanic variables:sea surface height(SSH), wind speed, wind curl, TC translation speed and surface net heat flux. The regression model fits reasonably well with 10-year statistical observations/reanalysis data obtained from 100 selected TCs in the northwestern Pacific during 2001–2010, with an averaged fitting error of 0.07 and a mean absolute error of 0.72°C between diagnostic and observed SST cooling. The results reveal that the vertical mixing is overall the pre dominant process producing ocean SST cooling, accounting for 55% of the total cooling. The upwelling accounts for 18% of the total cooling and its maximum occurs near the TC center, associated with TC-induced Ekman pumping. The surface heat flux accounts for 26% of the total cooling, and its contribution increases towards the tropics and the continental shelf. The ocean thermal structures, represented by the SSH in the regression model,plays an important role in modulating the SST cooling pattern. The concept of the regression model can be applicable in TC weather prediction models to improve SST parameterization schemes.     

一种利用浮标站资料改进海浪模式有效波高预报的方法

基于浮标站海浪历史数据,利用回归分析方法建立了海浪数值模式有效波高预报产品的一元二次回归方程订正统计模型。通过2017年7月1日-2018年10月10日期间业务试运行结果发现:订正方程能有效改善有效波高数值预报产品的预报精度,且预报时效越短订正效果越显著。其中,第6~11 h预报时效内的订正前后平均绝对误差值减小0.17~0. 241 m,第6~18 h预报时效内订正前后均方根误差减小幅度为0.103~0. 28 m。这说明应用订正统计模型对海浪模式输出产品进行订正,也是改进海浪模式预报准确率的一种有效途径。