Application of Projection Pursuit Evaluation Model Based on Real-Coded Accelerating Genetic Algorithm in Evaluating Wetland Soil Quality Variations in the Sanjiang Plain, China

A new technique of dimension reduction named projection pursuit is applied to model and evaluatewetland soil quality variations in the Sanjiang Plain, Helongjiang Province, China. By adopting the im-proved real-coded accelerating genetic algorithm (RAGA), the projection direction is optimized and multi-dimensional indexes are converted into low-dimensional space. Classification of wetland soils and evaluationof wetland soil quality variations are realized by pursuing optimum projection direction and projection func-tion value. Therefore, by adopting this new method, any possible human interference can be avoided andsound results can be achieved in researching quality changes and classification of wetland soils.

The application of projection pursuit classification in the process of strategy selection and evaluation based on the real coded accelerating genetic algorithm

During the process of enterprises＇ strategy evaluation and selection, there are many evaluating indicators, and among them there are some potential correlations and conflicts. Thus it poses the problems to the decision-makers how to conduct correct evaluation on a business and how to make strategy adjustment and selection according to the evaluation. Based on the qualitative and quantitative method, the paper introduces the Projection Pursuit Classification （PPC） model based on the Real-coded Accelerating Genetic Algorithm （RAGA） into the process of enterprises＇ strategy evaluation and selection. The characteristic of PPC model is that it ultimately overcomes the influence of the proportion of subjectivity and avoids precocious convergence, thus providing a new objective method for strategy evaluation and selection by pursuing the most objective strategy evaluation to make the relatively sensible strategy portfolio and action.

Projection pursuit model of vehicle emission on air pollution at intersections based on the improved bat algorithm

The projection pursuit model is used to study the assessment of air pollution caused by vehicle emissions at intersections. Based on the analysis of the characteristics and regularities of vehicle emissions at intersections, a vehicle emission model based on projection pursuit is established, and the bat algorithm is used to solve the optimization function. The research results show that the projection pursuit model can not only measure the air pollution of vehicle emissions at intersections, but also effectively evaluate the level of vehicle exhaust emissions at intersections. Taking the air pollution caused by vehicle emissions at intersections as the research object and considering the influence factors of vehicle emissions on air pollution comprehensively, the evaluation index system of vehicle emissions at intersections on air pollution is constructed. Based on large data analysis, a prediction model of air pollution caused by vehicle emissions at intersections is constructed, and an improved bat algorithm is used to realize the assessment process. The application results show that the prediction model of vehicle emissions at intersections can define the degree of air pollution caused by vehicle emissions, and it has good guiding significance and practical value for solving the problem of air pollution caused by vehicle emissions.

Projection Pursuit Dynamic Cluster Model and its Application to Water Resources Carrying Capacity Evaluation

The research shows that projection pursuit cluster (PPC) model is able to form a suitable index for overcom-ing the difficulties in comprehensive evaluation, which can be used to analyze complex multivariate prob-lems. The PPC model is widely used in multifactor cluster and evaluation analysis, but there are a few prob-lems needed to be solved in practice, such as cutoff radius parameter calibration. In this study, a new model-projection pursuit dynamic cluster (PPDC) model-based on projection pursuit principle is developed and used in water resources carrying capacity evaluation in China for the first time. In the PPDC model, there are two improvements compared with the PPC model, 1) a new projection index is constructed based on dynamic cluster principle, which avoids the problem of parameter calibration in the PPC model success-fully;2) the cluster results can be outputted directly according to the PPDC model, but the cluster results can be got based on the scatter points of projected characteristic values or the re-analysis for projected character-istic values in the PPC model. The results show that the PPDC model is a very effective and powerful tool in multifactor data exploratory analysis. It is a new method for water resources carrying capacity evaluation. The PPDC model and its application to water resources carrying capacity evaluation are introduced in detail in this paper.

On grey relation projection model based on projection pursuit

Multidimensional grey relation projection value can be synthesized as one-dimensional projection value by using projection pursuit model. The larger the projection value is,the better the model. Thus,according to the projection value,the best one can be chosen from the model aggregation. Because projection pursuit modeling based on accelerating genetic algorithm can simplify the implementation procedure of the projection pursuit technique and overcome its complex calculation as well as the difficulty in implementing its program,a new method can be obtained for choosing the best grey relation projection model based on the projection pursuit technique.

A Genetic Fuzzy Analytical Hierarchy Process Based Projection Pursuit Method for Selecting Schemes of Water Transportation Projects

The optimal selection of schemes of water transportation projects is a process of choosing a relatively optimal scheme from a number of schemes of water transportation programming and management projects, which is of importance in both theory and practice in water resource systems engineering. In order to achieve consistency and eliminate the dimensions of fuzzy qualitative and fuzzy quantitative evaluation indexes, to determine the weights of the indexes objectively, and to increase the differences among the comprehensive evaluation index values of water transportation project schemes, a projection pursuit method, named FPRM-PP for short, was developed in this work for selecting the optimal water transportation project scheme based on the fuzzy preference relation matrix. The research results show that FPRM-PP is intuitive and practical, the correction range of the fuzzy rained is both stable and accurate; preference relation matrix A it produces is relatively small, and the result obtherefore FPRM-PP can be widely used in the optimal selection of different multi-factor decision-making schemes.

Benefit Evaluation Model of Small Watershed Control Based on Projection Pursuit

A projection pursuit model is presented in this paper for comprehensive evaluation of benefits of small watershed control.By using the model,small watershed control samples with many benefit evaluation indexes can be synthesized projective values with one dimension.The samples can be naturally evaluated according to the projective values.The parameters of the model is optimized by using real coding beased accelerating genetic aglrothm,which overcomes the shortcomings of large computation amount and difficulty of computer programming in traditional projection prusuit methods,and provides a new way for wide applications of projection pursuit technique to different evaluation problems in agricultural systems engineering.

基于WTD-PPAR的中国碳排放预测研究

碳排放预测对我国的碳排放规划和政策颁布具有一定的参考价值。使用小波阈值去噪模型对碳排放数据建模能够有效缓解其存在的非线性和波动性问题,筛选得到数据的有效增长趋势。在此基础上,利用碳排放数据的时间序列特征建立其投影寻踪自回归(PPAR)模型,从而对碳排放进行预测分析。将该模型与未进行去噪的PPAR、BP、LSSVM、SVR、LSTM模型进行对比,发现WTD-PPAR模型的预测精度更高,预测结果更准确。分析结果显示,我国碳排放将在2029年达到峰值,约为1081.89 mt,能够实现碳达峰的目标。

Approach to Weighted Geometric Evaluation Based on Projection Pursuit

Weighted geometric evaluation approach based on Projection pursuit (PP) model is presented in this paper to optimize the choice of schemes. By using PP model, the multi-dimension evaluation index values of schemes can be synthesized into projection value with one dimension. The scheme with a bigger projection value is much better, so the schemes sample can be an optimized choice according to the projection value of each scheme. The modeling of PP based on accelerating genetic algorithm can predigest the realized process of projection pursuit technique, can overcome the shortcomings of large computation amount and the difficulty of computer programming in traditional projection pursuit methods, and can give a new method for application of projection pursuit technique to optimize choice of schemes by using weighted geometric evaluation. The analysis of an applied sample shows that applying PP model driven directly by samples data to optimize choice of schemes is both simple and feasible, that its projection values are relatively decentralized and profit decision-making, that its applicability and maneuverability are high. It can avoid the shortcoming of subjective weighing method, and its results are scientific and objective.

CLUSTERING VIA DIMENSIONAL REDUCTION METHOD FOR THE PROJECTION PURSUIT BASED ON THE ICSA

The performance of the classical clustering algorithm is not always satisfied with the high-dimensional datasets, which make clustering method limited in many application. To solve this problem, clustering method with Projection Pursuit dimension reduction based on Immune Clonal Selection Algorithm (ICSA-PP) is proposed in this paper. Projection pursuit strategy can maintain consistent Euclidean distances between points in the low-dimensional embeddings where the ICSA is used to search optimizing projection direction. The proposed algorithm can converge quickly with less iteration to reduce dimension of some high-dimensional datasets, and in which space, K-mean clustering algorithm is used to partition the reduced data. The experiment results on UCI data show that the presented method can search quicker to optimize projection direction than Genetic Algorithm (GA) and it has better clustering results compared with traditional linear dimension reduction method for Principle Component Analysis (PCA).

基于蜣螂算法优化的投影寻踪生态环境评价方法构建

矿山开采会破坏和占用大量土地资源,对生态环境造成持久的负面影响,因此评价矿区生态环境质量变化迫在眉睫。为了准确监测矿区生态环境质量,以陕煤集团张家峁矿区2000—2023年24期逐年的Landsat影像为基础数据,通过计算绿度(Normalized Difference Vegetation Index,NDVI)、湿度(Humidity Index,WET)、热度(Land Surface Temperature,LST)、干度(Normalized Differential Build-up and bare Soil Index,NDBSI)4项生态指标,并采用基于果蝇优化算法的投影寻踪(Fruit Fly Optimization Algorithm-Projection Pursuit Clustering,FOA–PPC)、基于粒子群优化算法的投影寻踪(Particle Swarm Optimization-Projection Pursuit Clustering,PSO–PPC)、基于灰狼优化算法的投影寻踪(Grey Wolf Optimizer-Projection Pursuit Clustering,GWO–PPC)和基于蜣螂优化算法的投影寻踪(Dung Beetle Optimizer-Projection Pursuit Clustering,DBO–PPC)4种群智能优化算法构建矿区生态环境质量评价方法,并利用平均相关度进行精度验证。结果表明:①DBO–PPC模型的平均相关度和类内聚集度均高于PSO–PPC模型、FOA–PPC模型和GWO–PPC模型,且与EI指数更接近,表明DBO–PPC能更好的评估研究区生态环境;②基于DBO–PPC模型的张家峁矿区2000—2023年生态环境质量均值为0.4,生态环境质量以差和较差等级为主,面积占比约55.94%,空间上呈西部差东部好,常家沟水库的生态环境在研究期内表现为优等级,矿区东北部和中部区域的生态环境较好,植被覆盖较多;③矿区发生沉陷面积占比为81.28%,沉陷最大值达−0.15 m;采区发生沉陷现象面积占比明显高于矿区,占采区面积的89.56%,生态环境质量以−0.0004的速率下降,表明采矿活动使得研究区的地表发生了沉陷,进而影响到生态环境。综上所述DBO–PPC模型在监测评价矿区生态环境质量方面具有较强的合理性,从而为矿区生态环境可持续发展提供技术手段。

时空异质性视角下长江经济带水资源系统韧性评估及调控因子分析

水资源系统韧性的评估与调控是缓解水资源危机和规避水资源风险的重要途径之一。从水资源的自然资源、社会经济、生态环境等属性出发,综合考虑水资源系统韧性的抵抗性、恢复性、适应性特征,构建了包含水资源、水经济、水福利、水管理、水生态和水环境6个维度的水资源系统韧性评价指标体系,建立了基于麻雀搜索算法的投影寻踪模型,运用自然断点法识别区域空间分异特征,采用因子探测识别影响水资源系统韧性空间分布格局的调控因子。通过对2010—2019年长江经济带11个省(市)水资源系统韧性的分析发现,在空间上,长江经济带水资源系统韧性表现为西部最优、东部次之、中部最末,且南部优于北部;在时间上,长江经济带水资源系统韧性虽有波动但整体呈现上升的趋势。从6个维度来看,水生态韧性维度的韧性值区间跨度较大,在0.08~0.52之间波动,水福利韧性最高,水环境韧性次之。水资源和水福利的相关因子对水资源系统韧性的影响较为明显,水经济中的工业废水治理投资占GDP的比重和万元工业增加值用水量2个指标以及水福利中的供水管道密度和人均供水量2个指标的影响力逐渐上升。通过对长江经济带水资源系统韧性和调控因子的分析,有针对性地提出了提高长江经济带水资源系统韧性的对策建议。

数字乡村建设对防返贫能力的影响及空间效应研究

防止规模性返贫是扎实推进乡村振兴的关键所在,数字乡村建设对提升农村防返贫能力具有重要意义。本文基于2016~2022年中国30个省(区、市)的面板数据,运用基于加速遗传算法的投影寻踪模型测度数字乡村建设水平与防返贫能力,通过核密度估计刻画两者的时空分异特征,利用空间杜宾模型深入分析数字乡村建设对防返贫能力的空间溢出效应。研究发现:2016~2022年,数字乡村建设水平与防返贫能力整体上在不断提高,数字乡村建设存在多极化趋势,防返贫能力两极化现象逐渐减弱。农村防返贫能力具有显著的空间聚集特征,主要表现为“高—高”和“低—低”聚集模式。空间杜宾溢出效应分解表明:数字乡村建设对防返贫有着显著的正向影响以及正的空间溢出效应。据此,对未来建设数字乡村提出相关建议,并探索出提升防返贫能力的新路径,未来可尝试从溢出边界进一步探索空间溢出效应。

巷道通风摩擦阻力系数遗传投影寻踪回归预测

为满足智能通风准确获得巷道摩擦风阻的需求,基于遗传算法的投影寻踪回归预测方法预测矿井通风摩擦阻力系数。研究空气密度变化和巷道有无支护两种类型时模型预测的准确性。利用通风摩擦阻力系数影响因素进行训练,对不同支护类型巷道采集学习样本建立模型,使用部分样本进行验证。将模型预测结果与主成分分析预测和BP神经网络预测结果进行比较。对不同支护类型巷道进行了预测,最大误差为1.76%,平均误差为1.07%;仅对圆木支护进行了分析,最大误差为1.73%,平均误差为0.79%;对不同密度无支护巷道预测表明,平均误差为-0.99%,最大误差为-1.01%,风流密度对模型预测结果的准确性基本没有影响。无论是风流密度还是支护形式,该方法预测精度均优于主成分分析和BP神经网络。

基于FA-ISSA-PPR模型的旋风分离器分离效率预测

旋风分离器是气田开发中常用的气固分离设备,准确预测旋风分离器的分离效率对于指导其结构设计和方法优化具有重要意义。在对数据集进行相关性分析的基础上,采用因子分析(factor analysis, FA)简化变量,降低预测模型的复杂程度,利用改进的樽海鞘群算法(improved salp swarm algorithm, ISSA)对投影寻踪(projection pursuit regression, PPR)的模型参数进行优化,形成FA-ISSA-PPR组合模型。结果表明,利用FA模型,原数据集的10个变量可以简化合并为4个公因子,分别代表尺寸参数、颗粒沉降特性、粒子运行轨迹和等效分割粒径对分离效率的影响;与半经验模型和其余机器学习模型相比,组合模型在预测精度和训练时间上具有一定的优越性,在测试样本上的平均绝对误差(MAE)为0.005 91,R^(2)可达0.995,证明了其在小样本、非线性数据分析上的准确性、鲁棒性和泛化性。

基于投影寻踪优化算法的自然资源与经济高质量发展关系研究——以桂西南喀斯特—北部湾为例

开展自然资源利用综合研究,对于实现自然资源统一管理、服务高质量发展与实现中国式现代化具有重要意义。运用基于遗传算法的投影寻踪模型测算桂西南喀斯特-北部湾2005-2020年自然资源利用与经济高质量发展水平,结合Tapio脱钩模型和对数平均迪氏指数(LMDI)分解模型揭示其驱动效应。结果表明:①桂西南喀斯特-北部湾自然资源利用水平与经济高质量发展水平均上升且空间特征明显,自然资源利用呈现自西北向东南递减的空间特征,经济高质量发展表现为中部与东南高、西北低的时空特征;②自然资源利用与经济高质量发展的脱钩关系波动变化,以弱脱钩为主,伴有增长连结、扩张型负脱钩和强负脱钩类型;二者之间的可持续性减弱;脱钩程度表现为由西北喀斯特地区向东南沿海地区减弱的空间特征;③空间效应具有抑制作用,技术效应具有促进作用,人口效应与经济效应对自然资源利用的影响方向和强度不稳定;各因素的驱动作用排序为:空间效应>经济效应>基数效应>人口效应。未来应坚持“两山论”的思想为统领,以规范化的评价体系、创新性的经济手段为重要抓手,全面提高桂西南喀斯特-北部湾自然资源利用效率与助推经济高质量发展。

基于机器学习方法的高光谱遥感图像目标检测研究

针对高光谱遥感图像目标检测的难题,提出基于机器学习的高光谱遥感图像目标检测方法。首先,通过动力演化算法找到满足偏度、峰度最大化的投影方向,将高维图像数据投影至低维子空间,从而提取图像的光谱信息。然后,通过线性判别法将提取的信息转换为直方图形式,再利用自动标记分水岭算法和KNN方法进行目标区域的初分割和分类,以去除非目标光谱像元。测试结果表明,对目标信息进行检测处理后,图像像元整体分类精度与平均分类精度数值区间为[0.97,0.98],信息熵数值仅有0.01,说明该方法具有较高精度,结果可信度较高。

基于多目标投影寻踪模型的电力发展水平评价方法

电力生产活动所释放的碳排放在全球碳排放总量中占比较高。因此,电力行业成为推动“碳减排”目标达成的关键责任主体。借助量化综合评价的方式对国家电力发展水平进行评估,不仅可以清晰地描绘出各国在电力领域的发展脉络,而且有助于更准确地识别出中国与其他国家在电力发展上的差距。该文对国家电力发展水平进行综合评价与研究,提出一种基于改进多目标粒子群算法优化投影寻踪模型的电力发展水平评价方法。首先,提出改进的多目标粒子群优化算法;其次,建立2种投影寻踪模型,利用改进的多目标粒子群算法优化构建2种投影寻踪模型,得到投影向量的最优Pareto解集;再次,利用模糊综合评价,得到最优权重折中解,将最优权重代入前景理论模型,得到各个国家电力发展水平的综合评分值,并基于此评分,对各国电力发展水平优劣进行客观排序,最后,利用实际的国家电力发展水平数据集对该文所提方法进行验证。研究结果表明:该方法可实现对国家电力发展水平的有效排序,其评价准确性优于现有的电力发展水平评价方法的。

基于投影寻踪的中国省际文化产业发展实例研究

文化产业高质量发展已成为中国现在乃至未来社会发展的必然趋势,了解我国现阶段文化产业发展状况,提升省际文化产业发展水平至关重要。论文从创新、协调、开放和共享四个维度维度出发构建一个包含8个方面、21个指标的省际文化产业发展评价指标,采集2012-2019年各省市的相关数据,运用多元宇宙优化的投影寻踪模型对省际文化产业发展水平进行综合评价、排序和分类研究。研究结果显示,广东、北京、江苏、浙江、上海文化产业发展水平始终位列前茅,其中文化产业创新劳动力投入强度对文化产业发展水平影响最大。

“双碳”战略下我国碳交易效率评估研究——基于2014—2020年交易数据的实证分析

提升碳交易市场效率,推动供应链主动减排,对于实现“双碳”目标具有重要意义。基于我国8个碳交易市场2014—2020年交易数据,从市场公平性、流动性、波动性和目标性4个层面构建碳交易市场活性综合评价指标,并运用PP-SQP模型对区域碳交易效率进行实证研究。结果表明:交易效率较高的市场以低碳化目标为导向,表现出良好的公平性、流动性、波动性,差异化碳配额、合理的碳价、高效的换手率有利于激发市场活性;而低效率市场各方面的表现缺乏积极性,其原因在于成交量少、企业参与率较低和配额过量等。