A comparative study of spatial interpolation methods fordetermining fishery resources density in the Yellow Sea

Spatial interpolation is a common tool used in the study of fishery ecology, especially for the construction of ecosystem models. To develop an appropriate interpolation method of determining fishery resources density in the Yellow Sea, we tested four frequently used methods, including inverse distance weighted interpolation(IDW), global polynomial interpolation(GPI), local polynomial interpolation(LPI) and ordinary kriging(OK).A cross-validation diagnostic was used to analyze the efficacy of interpolation, and a visual examination was conducted to evaluate the spatial performance of the different methods. The results showed that the original data were not normally distributed. A log transformation was then used to make the data fit a normal distribution. During four survey periods, an exponential model was shown to be the best semivariogram model in August and October 2014, while data from January and May 2015 exhibited the pure nugget effect.Using a paired-samples t test, no significant differences(P>0.05) between predicted and observed data were found in all four of the interpolation methods during the four survey periods. Results of the cross-validation diagnostic demonstrated that OK performed the best in August 2014, while IDW performed better during the other three survey periods. The GPI and LPI methods had relatively poor interpolation results compared to IDW and OK. With respect to the spatial distribution, OK was balanced and was not as disconnected as IDW nor as overly smooth as GPI and LPI, although OK still produced a few 'bull's-eye' patterns in some areas.However, the degree of autocorrelation sometimes limits the application of OK. Thus, OK is highly recommended if data are spatially autocorrelated. With respect to feasibility and accuracy, we recommend IDW to be used as a routine interpolation method. IDW is more accurate than GPI and LPI and has a combination of desirable properties, such as easy accessibility and rapid processing.

Accurate machine learning models based on small dataset of energetic materials through spatial matrix featurization methods

A large database is desired for machine learning(ML) technology to make accurate predictions of materials physicochemical properties based on their molecular structure.When a large database is not available,the development of proper featurization method based on physicochemical nature of target proprieties can improve the predictive power of ML models with a smaller database.In this work,we show that two new featurization methods,volume occupation spatial matrix and heat contribution spatial matrix,can improve the accuracy in predicting energetic materials' crystal density(ρ_(crystal)) and solid phase enthalpy of formation(H_(f,solid)) using a database containing 451 energetic molecules.Their mean absolute errors are reduced from 0.048 g/cm~3 and 24.67 kcal/mol to 0.035 g/cm~3 and 9.66 kcal/mol,respectively.By leave-one-out-cross-validation,the newly developed ML models can be used to determine the performance of most kinds of energetic materials except cubanes.Our ML models are applied to predict ρ_(crystal) and H_(f,solid) of CHON-based molecules of the 150 million sized PubChem database,and screened out 56 candidates with competitive detonation performance and reasonable chemical structures.With further improvement in future,spatial matrices have the potential of becoming multifunctional ML simulation tools that could provide even better predictions in wider fields of materials science.

Three-dimensional Extension of the Unit-Feature Spatial Classification Method for Cloud Type

We describe how the Unit-Feature Spatial Classification Method(UFSCM) can be used operationally to classify cloud types in satellite imagery efficiently and conveniently.By using a combination of Interactive Data Language(IDL) and Visual C++(VC) code in combination to extend the technique in three dimensions(3-D),this paper provides an efficient method to implement interactive computer visualization of the 3-D discrimination matrix modification,so as to deal with the bi-spectral limitations of traditional two dimensional(2-D) UFSCM.The case study of cloud-type classification based on FY-2C satellite data (0600 UTC 18 and 0000 UTC 10 September 2007) is conducted by comparison with ground station data, and indicates that 3-D UFSCM makes more use of the pattern recognition information in multi-spectral imagery,resulting in more reasonable results and an improvement over the 2-D method.

Comparing different spatial interpolation methods to predict the distribution of fishes:A case study of Coilia nasus in the Changjiang River Estuary

Spatial-temporal distribution of marine fishes is strongly influenced by environmental factors.To obtain a more continuous distribution of these variables usually measured by stationary sampling designs,spatial interpolation methods(SIMs)is usually used.However,different SIMs may obtain varied estimation values with significant differences,thus affecting the prediction of fish spatial distribution.In this study,different SIMs were used to obtain continuous environmental variables(water depth,water temperature,salinity,dissolved oxygen(DO),p H,chlorophyll a and chemical oxygen demand(COD))in the Changjiang River Estuary(CRE),including inverse distance weighted(IDW)interpolation,ordinary Kriging(OK)(semivariogram model:exponential(OKE),Gaussian(OKG)and spherical(OKS))and radial basis function(RBF)(regularized spline function(RS)and tension spline function(TS)).The accuracy and effect of SIMs were cross-validated,and two-stage generalized additive model(GAM)was used to predict the distribution of Coilia nasus from 2012 to 2014 in CRE.DO and COD were removed before model prediction due to their autocorrelation coefficient based on variance inflation factors analysis.Results showed that the estimated values of environmental variables obtained by the different SIMs differed(i.e.,mean values,range etc.).Cross-validation revealed that the most suitable SIMs of water depth and chlorophyll a was IDW,water temperature and salinity was RS,and p H was OKG.Further,different interpolation results affected the predicted spatial distribution of Coilia nasus in the CRE.The mean values of the predicted abundance were similar,but the differences between and among the maximum value were large.Studies showed that different SIMs can affect estimated values of the environmental variables in the CRE(especially salinity).These variations further suggest that the most applicable SIMs to each variable will also differ.Thus,it is necessary to take these potential impacts into consideration when studying the relationship between the spatial distribution of fishes and environmental changes in the CRE.

Analysis and comparison of spatial interpolation methods for temperature data in Xinjiang Uygur Autonomous Region, China

Spatial interpolation methods are frequently used to estimate values of meteorological data in locations where they are not measured. However, very little research has been investigated the relative performance of different interpolation methods in meteorological data of Xinjiang Uygur Autonomous Region (Xinjiang). Actually, it has importantly practical significance to as far as possibly improve the accuracy of interpolation results for meteorological data, especially in mountainous Xinjiang. There- fore, this paper focuses on the performance of different spatial interpolation methods for monthly temperature data in Xinjiang. The daily observed data of temperature are collected from 38 meteorological stations for the period 1960- 2004. Inverse distance weighting (IDW), ordinary kriging (OK), temperature lapse rate method (TLR) and multiple linear regressions (MLR) are selected as interpolated methods. Two rasterized methods, multiple regression plus space residual error and directly interpolated observed temperature (DIOT) data, are used to analyze and compare the performance of these interpolation methods respectively. Moreover, cross-validation is used to evaluate the performance of different spatial interpolation methods. The results are as follows: 1) The method of DIOT is unsuitable for the study area in this paper. 2) It is important to process the observed data by local regression model before the spatial interpolation. 3) The MLR-IDW is the optimum spatial interpolation method for the monthly mean temperature based on cross-validation. For the authors, the reliability of results and the influence of measurement accuracy, density, distribution and spatial variability on the accuracy of the interpolation methods will be tested and analyzed in the future.

Assessment of the Spatial and Temporal Water Eutrophication for Lake Baiyangdian Based on Integrated Fuzzy Method

Water quality evaluation entails both randomness and fuzziness. Considering that water eutrophication evaluation involves many indices, different classifications and interval values, fuzzy variable sets theory was developed to Lake Baiyangdian as a study case. Taking reference to eutrophication standard of Chinese lakes and local characteristic of Lake Baiyangdian, eutrophication degree of lake was divided into 8 levels. Total phosphorus, total nitrogen, and CODMn were selected as evaluation indices in this research. Based on the measured data, index feature value matrix of sample was built. Index weights were determined by means of pure threshold value method. Relative membership degree of each index to each classification was calculated with relative difference function model. Then the stability of feature value of classification corresponding was received by the comprehensive calculation with the relative membership degree and index weights. The results show that the proposed models are effective tools for generating a set of realistic and flexible optimal solutions for complicated water quality evaluation issues. It concluded that the model was reasonable and practical.

Monte Carlo Method for the Uncertainty Evaluation of Spatial Straightness Error Based on New Generation Geometrical Product Specification

Straightness error is an important parameter in measuring high-precision shafts. New generation geometrical product speeifieation（GPS） requires the measurement uncertainty characterizing the reliability of the results should be given together when the measurement result is given. Nowadays most researches on straightness focus on error calculation and only several research projects evaluate the measurement uncertainty based on ＂The Guide to the Expression of Uncertainty in Measurement（GUM）＂. In order to compute spatial straightness error（SSE） accurately and rapidly and overcome the limitations of GUM, a quasi particle swarm optimization（QPSO） is proposed to solve the minimum zone SSE and Monte Carlo Method（MCM） is developed to estimate the measurement uncertainty. The mathematical model of minimum zone SSE is formulated. In QPSO quasi-random sequences are applied to the generation of the initial position and velocity of particles and their velocities are modified by the constriction factor approach. The flow of measurement uncertainty evaluation based on MCM is proposed, where the heart is repeatedly sampling from the probability density function（PDF） for every input quantity and evaluating the model in each case. The minimum zone SSE of a shaft measured on a Coordinate Measuring Machine（CMM） is calculated by QPSO and the measurement uncertainty is evaluated by MCM on the basis of analyzing the uncertainty contributors. The results show that the uncertainty directly influences the product judgment result. Therefore it is scientific and reasonable to consider the influence of the uncertainty in judging whether the parts are accepted or rejected, especially for those located in the uncertainty zone. The proposed method is especially suitable when the PDF of the measurand cannot adequately be approximated by a Gaussian distribution or a scaled and shifted t-distribution and the measurement model is non-linear.

An Artificial Neural Network-Based Response Surface Method for Reliability Analyses of c-φ Slopes with Spatially Variable Soil

This paper presents an artificial neural network（ANN）-based response surface method that can be used to predict the failure probability of c-φslopes with spatially variable soil.In this method,the Latin hypercube sampling technique is adopted to generate input datasets for establishing an ANN model;the random finite element method is then utilized to calculate the corresponding output datasets considering the spatial variability of soil properties;and finally,an ANN model is trained to construct the response surface of failure probability and obtain an approximate function that incorporates the relevant variables.The results of the illustrated example indicate that the proposed method provides credible and accurate estimations of failure probability.As a result,the obtained approximate function can be used as an alternative to the specific analysis process in c-φslope reliability analyses.

Spatial charge and compensation method in a whirler

Based on particle-in-cell simulation, we studied the motions of ions and electrons. The results have shown that electrons are bounded by a magnetic field and only a small number of electrons can pass through the whirler channel. The plasma becomes non-neutral when it is emitted from the whirler, and the spatial charge leads to a beam divergence, which is unfavorable for mass separation. In order to compensate the spatial charge, a cathode is designed to transmit electrons and the quasi-neutral plasma beam. Experiment results have demonstrated that the auxiliary cathode can obviously improve the compensation degree of the spatial charge.

Investigation of the electrical conductivity beneath China using geomagnetic spatial gradient method

The data of the year 1992 from 25 geomagnetic observatories affiliated to the geomagnetic network of State Seismological Bureau of China were processed using the principle of geomagnetic spatial gradient method. Through finding out the polynomial form of optimum fitting, comparatively good C values for four harmonic components of diurnal variations were obtained. By using the inverse method of non linear underdetermined problem, the electrical conductivity structures under the observatories were investgated. It is shown that there are differences of the C values and conductivity structures in the deep underground under the south western part and northern parts and other parts of China. We studied the possibility of improving the gradient method for investigation of the deep underground conductivity structure, and it is indicated that the gradient method is hopeful in the investigation of earth′s deep conductivity structure and the applied studies concerned.

A Review of Methods of Studying the Spatial Distribution of Atmospheric Pollutants

Characteristics of studies on the spatial distribution of atmospheric pollutants are shown as follows: the main object of the studies in China is a single city instead of a region and the country; studying the spatial distribution of fine particulate matter becomes a hot spot presently;research methods have developed from traditional techniques into modernized techniques. Current methods of studying the spatial distribution of atmospheric pollutants mainly include spatial interpolation model,remote sensing method,land use regression model and BP neural network approach,etc. Each method has both advantages and disadvantages,and combining various methods to study the spatial distribution of atmospheric pollutants becomes a new problem that needs to be solved urgently.

Numerical study on spatially varying control parameters of a marine ecosystem dynamical model with adjoint method

Based on the simulation of a marine ecosystem dynamical model in the Bohai Sea, the Yellow Sea and the East China Sea, chlorophyll data are assimilated to study the spatially varying control parameters （CPs） by using the adjoint method. In this study, the CPs at some grid points are selected as the independent CPs, while the CPs at other grid points can be obtained through linear interpolation with the independent CPs. The independent CPs are uniformly selected from each 30′ × 30′area, and we confirm that the optimal influence radius is 1.2° by a twin experiment. In the following experiments, when only the maximum growth rate of phytoplankton （Vm） is estimated by two given types of spatially varying CPs, the mean relative errors of Vm are 1.22% and 0.94% while the decrease rates of the mean error of chlorophyll in the surface are 94.6% and 95.8%, respectively. When the other four CPs are estimated respectively, the results are also satisfactory, which indicates that the adjoint method has a strong ability of optimizing the prescribed CP with spatial variations. However, when all these five most important CPs are estimated simultaneously, the collocation of the changing trend of each parameter influences the estimation results remarkably. Only when the collocation of the changing trend of each parameter is consistent with the ecological mechanisms which influence the growth of the phytoplankton in marine ecosystem, could the five most important CPs be estimated more accurately.

图卷积神经网络综述

图卷积神经网络是图论与深度学习的交叉,已成为机器学习领域的研究热点。基于此,介绍了图卷积神经网络的形成,梳理了两大类经典的图卷积神经网络:谱方法和空间方法,详细介绍了这两类图卷积神经网络模型,分析了图卷积操作的核心理论基础,介绍了图卷积神经网络在各领域的应用,总结了图卷积神经网络面临的主要挑战,展望了图卷积神经网络的发展趋势,并分析了图卷积神经网络在野外环境下蝴蝶识别任务中的潜在应用。

基于误差幅空特性分析的空间负荷预测误差评价方法

对空间负荷预测误差进行有效评价是客观认识预测结果,指导预测结果合理应用的前提。然而,现有空间负荷预测误差评价的研究存在对误差的空间分布不考虑或考虑不充分导致评价不准确的问题。为此,提出一种基于误差幅空特性分析的空间负荷预测误差评价方法。首先,从空间负荷预测误差幅值大小和空间分布对电网规划影响的角度出发,对误差的幅空特性进行详细分析;其次,利用运输问题的数学模型来表征正负误差的幅空抵消特性,使用各空间邻近度–幅值误差值曲线与x轴围成面积之和来表征剩余未抵消误差的幅空叠加特性;然后,分别通过伏格尔法和各梯形面积累加公式来计算正负误差的幅空抵消影响值和剩余未抵消误差的幅空叠加影响值,并在此基础上构建空间负荷预测误差评价指标;最后,基于误差对电网规划的实际影响给出对误差评价指标性能的检验方法。算例分析表明,与传统方法相比,该文所提误差评价方法从幅值和空间两个维度实现了对空间负荷预测误差更为全面的评估,与误差对电网规划影响的实际情况贴近度更高。

我国畜牧业空间集聚特征及演化趋势分析

畜牧业区域优化合理布局是农业供给侧改革以及高质量发展的必然要求,该文利用全国各省畜产品产量面板数据,采用空间自相关方法分析畜牧业地理集聚特征,并对其形成原因进行剖析。研究发现:中国畜牧业呈现明显的集聚特征,各省份畜牧业发展受邻近省份发展的影响较为显著;产业空间集聚呈现非均衡特征,东、中部以“高-高”集聚为主,西部以“低-低”集聚特征为主;畜牧业布局相对稳定,未来一段时间内将继续保持现状。基于此,该文提出推动适度规模经营,发展专业化、市场化、一体化产业链,完善畜牧业配套体系等政策建议。

传统村落空间分布特征及成因分析——以陕西地区省级村落为例

以陕西地区429个省级传统村落为研究对象,采用野外调研和地理信息系统(GIS)空间分析相融合的方法,从地理区位、地形地貌、气候条件、水系分布、经济条件、历史文化等多维度对陕西地区省级传统村落的空间分布形态与形成机理进行研究.结果表明:陕西地区省级传统村落空间分布呈现出陕北区域分布较为集中,延安和榆林地区尤为突出,而陕南和关中区域除安康和渭南地区分布相对密集,整体分布较为分散,全省已经形成4个集中聚集区域;陕西地区省级传统村落分布特征与其所处区海拔高度和水系分布密切相关,低海拔区传统村落分布数量较多,且多邻近水源地或沿河流水系分布,地形地貌特征和河流水系分布直接影响陕西地区省级传统村落分布基本格局,地域文化、经济条件及历史文化等因素进一步促进了4个核心区域的形成.

1999—2022年安徽省耕地安全评价

耕地安全评价对于确保区域粮食安全具有重要意义。利用1999年至2022年的相关数据,采用加权TOPSIS法、障碍因子诊断模型和空间自相关法,从时间和空间角度对安徽省的耕地安全进行评价。结果表明:全省耕地安全指数整体呈上升态势,从临界安全逐渐过渡到相对安全状态。其中,数量安全缓慢下降,质量安全快速增长,生态安全波动下降,投入安全平稳增长。障碍度方面,生态安全障碍和投入安全障碍是主要问题。农业从业人口、每公顷农药使用量、人均耕地面积、氮肥比重和农业机械总动力是排位靠前的耕地安全障碍指标。市域耕地安全分为较不安全、临界安全和相对安全三种状态。呈现出东北-西南走向的三条带状区域分布格局。各市的耕地安全水平展现了一定的高值和低值集聚特征。为提升全省的耕地安全水平,应采取转变耕地利用方式、强化生态治理和优化农业生产布局等措施。

基于多模式预报的四川盆地强降水订正方法

四川盆地地形地貌复杂,强降水预报难度大,对模式降水预报产品进行订正,是提升强降水预报质量的重要手段。本文选取2018—2019年发生在四川盆地的35次强降水过程,对ECMWF、CMA_MESO和SWC_WARMS三种模式的24 h强降水预报采用常规评分和空间平移两个方法进行检验,并利用最优评分、多模式集成和位移订正三种方法进行订正试验。结果表明:最优评分订正方法可以有效改善各模式降水预报的强度,而多模式集成订正法可以改进降水落区和极值预报,在此基础上计算位移偏差,根据最优的位移偏差值对降水预报进行位移订正,可以进一步改进强降水落区预报效果。然后利用2020—2021年强降水过程进行订正效果验证,结果显示:经订正后的降水极值预报更接近实况,各量级降水预报评分明显优于单一模式,暴雨和大暴雨预报的TS评分提高率较最优单模式分别可达24.3%和42.8%,订正后空报率基本维持,漏报率显著下降,订正效果良好。

黄河流域城市高质量发展空间关联及障碍因子分析

基于新发展理念,构建综合评价指标体系,运用熵值法计算黄河流域61个地级市2010年、2013年、2016年和2019年的高质量发展水平综合得分,并运用自然间断点法、空间自相关和障碍度模型,分析黄河流域城市高质量发展的时空演变规律、空间关联性和障碍因子。结果表明:(1)黄河流域整体高质量发展水平不断提升。根据高质量发展水平将城市分为四大类,其中高水平城市主要为省会和中心城市,较高水平城市分布在中心城市周围,较低水平城市由下游向中上游扩散,低水平城市集聚在中上游。(2)黄河流域总体空间关联性较弱,局部存在集聚效应,东部高值区被高值区包围的高高集聚较明显,西部低值区被低值区包围的低低集聚较明显。(3)黄河流域城市高质量发展障碍因子主要包括入境旅游人数、外资依存度、万人专利申请量、外贸依存度和万人在校生数。基于此,提出黄河流域高质量发展相关建议。