水库抗旱调度分期特征确定方法研究

为在一定程度上减轻流域干旱损失,国家防汛抗旱总指挥部提出旱限水位的概念,并开展水库抗旱调度,降低干旱影响程度。旱限水位是水库低水位运行的控制性水位,其合理设置对提高区域水资源利用率意义重大,而水文气象特征在年内的阶段性变化要求基于旱限水位的水库管理应适应其变化特征。此次研究提出水库抗旱调度分期的确定方法,采用核主成分分析法提取指标的非线性特征,结合熵权法赋予指标权重后利用Fisher最优分割法对干旱的年内阶段性变化进行划分。以黄河流域刘家峡和小浪底水库为例,分期结果刘家峡水库分3期为:7月至9月,10月至3月,4月至6月;小浪底水库分3期为:7月至10月,11月至3月,4月至6月。     

Validation of a Lagrangian dispersion model implementing different kernel methods for density reconstruction

In this paper the inert version of a Lagrangian particle model named photochemical Lagrangian particle model (PLPM) is described and validated. PLPM implements four density reconstruction algorithms based on the kernel density estimator. All these methods are fully grid-free but they differ each other in considering local or global features of the particles distribution, in treating the Cartesian directions separately or together and in being based on receptors or particles positions in space. Each kernel has been shown to have both advantages and disadvantages, but the overall good performances of the model when compared with the well known Copenhagen and Kincaid data sets are very encouraging in view of its extension to fully chemically active simulations, currently under development.     

三重属性的承载力约束下中国水资源利用效率动态演进特征分析

基于水资源"资源禀赋-社会经济-生态环境"三重属性约束建立承载力测算模型,创新性地将水资源承载力作为投入要素,构建SFA效率测算模型,运用2003～2017年中国31个省份面板数据,应用Kernel密度估计和Markov链对水资源使用效率的动态演进特征进行分析.结果表明:①中国大部分省份水资源承载力在稳步提升,西北、华北、东北和华中地区呈较低水平,西南、华南等地区呈较高水平;②中国所有省份水资源利用效率均保持稳定增长,西部地区比中、东部地区增长幅度更大,整体呈现东高西低,南高北低的空间态势;③中国水资源利用效率动态演进存在多极化和趋同化现象,部分省份存在梯度效应,同时具有俱乐部收敛特征,流动性差,马太效应凸显.为实现水资源、社会和环境的协调耦合,降低区域间资源使用效率差异,提升水资源利用效率,提出需要充分考虑水资源承载力,统筹全国及区域水资源供需协调发展战略,构建水权交易市场等建议.     

Kernel-based home range method for data with irregular sampling intervals

Studies of habitat selection and movements often use radio-tracking data for defining animal home ranges. Home ranges (HR) can be approximated by a utilization density distribution (UD) that instead of assuming uniform use of areas within HR boundary provides a probabilistic measure of animal space use. In reality, radio-tracking data contain periods of frequent autocorrelated observations interspersed with temporally more independent observations. Using such temporally irregular data directly may result in biased UD estimates, because areas that have been sampled intensively receive too much weight. The problem of autocorrelation has been tackled by resampling data with an appropriate time interval. However, resampling may cause a large reduction in the data set size along with a loss of information. Evidently, biased UD estimates or reduction in data may prejudice the results on animal habitat selection and movement. We introduce a new method for estimating UDs with temporally irregular data. The proposed method, called the time kernel, accounts for temporal aggregation of observations and gives less weight to temporally autocorrelated observations. A further extension of the method accounts also for spatially aggregated observations with relatively low weights given to observations that are both temporally and spatially aggregated. We test the behaviour of the time kernel method and its spatiotemporal version using simulated data. In addition, the method is applied to a data set of brown bear locations.     

基于历史记录的赤潮灾害危险性区划方法的构建与应用

利用福建省近岸海域1959—2018年的赤潮历史记录数据资料,在地理信息系统(Geographical Information System,GIS)平台下采用核密度估计函数(kernel density estimation)对赤潮历史记录数据中的赤潮发生频次、赤潮发生持续时间、赤潮面积和赤潮藻类毒性等参数进行了空间...     

改革开放以来吉林省城市集聚区的空间演化

改革开放以来,吉林省进入工业化和城市化的快速发展时期,城市布局集群化现象明显。传统的空间分析方法很难深入刻画城镇体系空间格局的时空演变过程。因此,运用Kernel空间密度分析方法,就改革开放以来吉林省城市集聚区的空间演进过程进行系统研究。结果显示:①1986-2007年间,吉林省城市空间分布集中表现为4个城市群组集聚区;②吉林省城市集聚核心区为长、吉两市;③1986-2007年间,吉林省城市空间格局在中东部方向的拓展较为明显,而东部和西部城市空间分布密度变化不明显,其省域空间分布具有中东部偏密集/东、西部较稀疏的空间格局特征;④吉林省城市分布空间的节点结构呈现多空间层次的结构特征。     

新疆塔里木河流域洪水发生率非平稳性特征及气候变化影响研究

新疆洪水灾害近年来有加剧趋势,但其特征与机理尚未有深入探讨。论文利用核估计和Bootstrap方法深入分析新疆塔里木河（塔河）流域洪水发生率的非平稳性及不确定性,同时采用广义可加模型（GAMLSS）构建洪水发生频率与协变量（大气环流因子、降水和气温指标等）的关系并定量辨识主要影响因子。研究表明：1）塔河流域洪水在1960年代左右和1990年代左右两个时期高频发生,两个洪水高发期之间洪水发生次数多为2或3次,且与极端降水发生次数较为吻合;2）洪水发生率呈现显著非平稳性,从1990年左右洪水发生次数持续上升,并达到峰值,表明洪水发生频率及强度呈加剧趋势;3）冬季AMO和AO是影响新疆塔河流域洪水发生的重要因子,而冬季NAO和SOI则是影响塔河流域5个州的洪水发生次数最为显著的大气环流指标。论文研究可为新疆塔河流域洪灾预测与预警及流域洪水管理提供关键理论依据。     

Prediction of gas explosion pressures: A machine learning algorithm based on KPCA and an optimized LSSVM

A gas explosion, as a common accident in public life and industry, poses a great threat to the safety of life and property. The determination and prediction of gas explosion pressures are greatly important for safety issues and emergency rescue after an accident occurs. Compared with traditional empirical and numerical models, machine learning models are definitely a superior approach. However, the application of machine learning in gas explosion pressure prediction has not reached its full potential. In this study, a hybrid gas explosion pressure prediction model based on kernel principal component analysis (KPCA), a least square support vector machine (LSSVM), and a gray wolf optimization (GWO) algorithm is proposed. A dataset consisting of 12 influencing factors of gas explosion pressures and 317 groups of data is constructed for developing and evaluating the KPCA-GWO-LSSVM model. The results show that the correlations among the 12 influencing factors are eliminated and dimensioned down by the KPCA method, and 5 composite indicators are obtained. The proposed KPCA-GWO-LSSVM hybrid model performs well in predicting gas explosion pressures, with coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE) values of 0.928, 26.234, and 12.494, respectively, for the training set; and 0.826, 25.951, and 13.964, respectively, for the test set. The proposed model outperforms the LSSVM, GWO-LSSVM, KPCA-LSSVM, beetle antennae search improved BP neural network (BAS-BPNN) models and reported empirical models. In addition, the sensitivity of influencing factors to the model is evaluated based on the constructed database, and the geometric parameters X1 and X2 of the confined structure are the most critical variables for gas explosion pressure prediction. The findings of this study can help expand the application of machine learning in gas explosion prediction and can truly benefit the treatment of gas explosion accidents.     

Point patterns of forest fire locations

In this paper I demonstrate some of the techniques for the analysis of spatial point patterns that have become available due to recent developments in point process modelling software. These developments permit convenient exploratory data analysis, model fitting, and model assessment. Efficient model fitting, in particular, makes possible the testing of statistical hypotheses of genuine interest, even when interaction between points is present, via Monte Carlo methods. The discussion of these techniques is conducted jointly with and in the context of some preliminary analyses of a collection of data sets which are of considerable interest in their own right. These data sets (which were kindly provided to me by the New Brunswick Department of Natural Resources) consist of the complete records of wildfires which occurred in New Brunswick during the years 1987 through 2003. In treating these data sets I deal with data-cleaning problems, methods of exploratory data analysis, means of detecting interaction, fitting of statistical models, and residual analysis and diagnostics. In addition to demonstrating modelling techniques, I include a discussion on the nature of statistical models for point patterns. This is given with a view to providing an understanding of why, in particular, the Strauss model fails as a model for interpoint attraction and how it has been modified to overcome this difficulty. All actual modelling of the New Brunswick fire data is done only with the intent of illustrating techniques. No substantive conclusions are or can be drawn at this stage. Realistic modelling of these data sets would require incorporation of covariate information which I do not so far have available.