基于Mann-Kendall方法的闽江流域降水趋势分析

基于闽江流域1965年~2020年逐日降水资料,利用Mann-Kendall方法进行趋势分析。结果表明:①闽江流域1965年~2020年平均降水量约为1653 mm,有明显的时间规律和区域分布特点。汛期降水量占全年降水量的70%左右,降水在空间上呈现由上游至下游,从西北至东南减少的趋势;②闽江流域全年降水量呈上升趋势,但趋势不显著。汛期及非汛期期间内,多数站点的降水量呈现递增变化,少数站点表现为弱减少;③闽江流域汛期有雨日的日平均雨强有增加趋势,提高了洪水风险,增加了防洪难度。

Mann-Kendall法在怒江大峡谷降水量分级组成演变规律中的应用

利用怒江流域4个长系列、38个短系列站点降水量数据,运用Mann-Kendall检验法,分析近几十年来怒江流域峡谷段降水量分级组成变化特征,探讨了年降水量分级组成对年降水的贡献情况。结果表明:①怒江流域年降水量以贡山为中点随河流南北两端递减;②怒江大峡谷左右岸年降水量差距明显,年降水量随海拔升高而增加,高黎贡山背风坡降水量随高程增加的幅度为95~99 mm/100m,碧罗雪山迎风坡的降水量随高程增加的幅度为7~30 mm/100m;③流域内小雨、中雨、大雨级别的日降水量每年均有发生;而暴雨仅分布于部分年份,其中峡谷中上段的暴雨发生率高于下段,无特大暴雨发生;④在峡谷段,中雨量对年降水量贡献最大,小雨、大雨对年降水量的贡献次之,之后为暴雨、大暴雨,对于年降水量发生显著增加的贡献,从大到小依次为大雨量、中雨量、小雨量、暴雨量;⑤年降水量在区域存在一个显著增加的时段,这个年降水显著增加的时段主要分布于1995—2023年,其中峡谷段年降水量均在1999—2003年发生显著增加。

伊犁河谷旅游气候变化特征分析——基于Mann-Kendall检验

旅游气候对旅游业发展的影响显著,分析旅游气候规律对于科学合理制定旅游业发展对策具有重要意义。基于Mann-Kendall检验伊犁河谷旅游气候变化规律,发现近59年来伊犁河谷年平均气温总趋势呈现波动升高态势,年降水量略呈波动增长趋势,年平均风速呈波动减小趋势,平均相对湿度变化幅度较小;气温、降水呈“V”型变化趋势;各县市年平均气温均表现为波动增长趋势,但其年平均气温差异较大,且风寒指数、着衣指数差异较大,对旅游具有一定区域性影响。

浅谈Mann-Kendall法分析蚌埠市降水量变化趋势

研究降水的趋势演变规律一直以来都是水文、气象领域的热点,其研究结果对于地区防洪减灾具有非常重要的意义,可以为降水和水资源长期预测提供依据,也为水资源合理开发与利用提供了参考。以安徽省蚌埠水文局辖区内23个水文基本雨量站1950—2022年各月、年降水量资料序列为基础,采用Mann-Kendall趋势分析法,分析了蚌埠市近73年春、夏、秋、冬四季及年降水量序列的趋势。分析结果为淮河流域蚌埠-洪泽湖区间降水预测及水资源合理调配等提供了最新的理论依据。

基于Mann-Kendall和Wavelet分析的唐山市近60年来降水量时空变化研究

降水量时空变化研究,对了解和预测气候变化趋势、合理规划水资源,以及应对极端天气事件具有重要意义。基于唐山市12个标准气象站点的1961-2020年观测资料,采用Mann-Kendall和Wavelet方法相结合的方式,对唐山市降水量时变性进行分析。结果表明,近60年来研究区降水量变化斜率为-1.59mm/a,经Mann-Kendall检测的趋势值Sen’slo值为-1.23mm/a;年际降水量于2014年发生突变,但并不显著;利用Wavelet分析发现,区域降水量存在1~8、7~10、14~16年的变化周期。

基于Mann-Kendall趋势检验的城市能源碳达峰评估方法

能源是城市碳排放的重要组成,评估城市能源碳达峰是践行国家“双碳”战略的必要手段.为此,针对城市能源的碳排放水平,提出了一种基于Mann-Kendall趋势检验的能源碳达峰评估方法.通过构建涵盖能源碳排放量、清洁能源发电量、交通电能替代量等要素的碳监测体系,结合历史数据计算城市的能源碳排放总量.鉴于能源碳排放具有季节性和随机性,采用Mann-Kendall趋势检验法,建立城市能源碳达峰判断模型,衡量不同时期区域碳排放水平.以上海某行政区为例,从年度、季度视角,判断该区域的能源碳达峰状态.计算结果表明,基于年度数据,该地区在2020年已实现能源碳达峰;基于季度数据,夏季与秋季已实现能源碳达峰,春季和冬季仍处于平台期.该方法可应用于评估城市级的碳达峰状态,为检验各省市的碳达峰进程提供参考.

基于Mann-Kendall法的祖厉河流域水沙特征及趋势分析

为了探究半干旱黄土区水沙特征及变化趋势,采用Mann-Kendall非参数检验法,对祖厉河流域2001—2016年的降水量、径流量与输沙量进行了分析研究。结果表明:研究期内年降水量呈不明显增加趋势,年变化幅度为1.83 mm/a,汛期降水量呈减少趋势,年变化幅度为-2.01 mm/a;年径流量与汛期径流量变化曲线基本一致,总体都呈显著波动下降趋势(P<0.05),径流量年变化幅度为-3.85×10^(6) m^(3)/a,汛期径流量年变化幅度为-2.729×10^(6) m^(3)/a,二者突变时间均在2013年左右,突变以后UF k值超过95%的信度线(α=0.05),呈现急剧减少趋势;输沙量和径流量呈现相似的显著减少趋势,年输沙量与汛期输沙量变化曲线基本一致,年输沙量和汛期输沙量年变化幅度均为-1.06×10^(6) t/a,二者突变开始时间均在2011—2012年之间,2014年以后UF k值超过95%的信度线(α=0.05),呈急剧减少趋势。

基于Sen+Mann-Kendall陕西省植被覆盖度时空变化规律研究

研究长时段、多尺度植被覆盖度时空格局变化及其影响因素对于监测陕西省环境变化,保障生态环境高质量发展具有重要意义。基于MOD17A3H数据、采用Sen趋势分析以及Mann-Kendall显著性检验,对陕西省2000—2019年植被覆盖度时空格局变化进行研究。结果表明:时间上,2010—2019年陕西省植被覆盖度呈波动增长趋势,植被生长环境不断改善;空间上,自北向南,植被覆盖度整体呈低-高-低-高分布,植被覆盖度年际变化趋势呈增-减-增-减分布,两者空间上具有一定互补性;Sen+Mann-Kendall方法对于异常值不敏感,对于数据分布无要求,可以广泛应用到植被覆盖度变化趋势分析中。

基于Mann-Kendall和小波功率谱的丹东市近35年降水量变化特征解析

为探究丹东市降水时空变化规律,以1986—2020年站点观测资料和降水栅格数据为基础,运用Mann-Kendall检验、Morlet分析和统计方法,研究了该地降水倾向趋势、变化周期及空间分布。结果显示,丹东近35年来平均降水量以3.79mm/a的速率增加,但趋势不显著。降水变化具有多个短震荡周期,主周期为6a,在2000年发生弱突变。受季风与地形影响,降水量呈现中高周低分布,空间差异为823~1098mm。文章揭示了近35年丹东降水量变化特征,可为区域水资源管理提供参考依据。

基于Mann-Kendall法的流域降雨与地下水位时空变化特征研究

降水量在空间和时间上的变化会影响水资源的可用性,导致农业、生态和基础设施受损。在区域和局部尺度上,降雨均有增加和减少的趋势。降雨强度增加可能导致山洪暴发,降水减少也会影响水的可用性和水质。如何预测降水量在时空中的变化趋势具有重要意义。采用Mann-Kendall方法对河北某区域降雨量变化趋势及特征进行分析,并分析观测井的降雨量与地下水水位之间的关系,研究成果可知:4个观测台站中,H台站接收的年降雨量最大,最大值达到了5 153 mm,而K-K台站最大降雨量仅次于H站台,为3 598 mm。地下水位变化与降雨变化趋势相同。可为相关工程提供参考。

基于Inspect投影的高维数据贝叶斯变点检验

高维数据的多变点检验已经成为了一个热点问题。针对高斯噪声下的高维数据,提出了一种基于Inspect投影的贝叶斯变点检验方法。该方法利用Inspect投影,通过奇异值分解(SVD)计算最优投影方向,沿该方向将高维数据投影到一维空间,并通过引入贝叶斯先验信息,对降维后的数据进行变点检验。通过数值模拟,该方法在样本量n,维度p,变点的稀疏度k的不同设置下的检验结果均优于Inspect方法。最后将该方法应用到膀胱肿瘤患者微阵列数据集(ACGH)中。

基于Mann-Kendall法的西宁市降水量变化规律分析

利用青海省西宁市4个气象站60年的降水资料,采用Mann-Kendall检验方法分析了降水量的年际、年代际变化特征及趋势,研究了该地区多年降水量的时程变化规律并得出该地区降水相关特征量的变化特征。结果表明:西宁地区年降水量整体呈现上升趋势,降水量的季节性差异较大,汛期表现出较为明显的上升趋势;年降水日数也呈现上升趋势,但上升趋势不显著;西宁市降水量在2005年发生突变。分析结果可为西宁市未来降水资源合理利用、洪涝灾害防治等工作提供参考依据。

Investigation into Recent Temperature and Rainfall Trends in Mali Using Mann-Kendall Trend Test: Case Study of Bamako

Rainfall and temperature variability analysis is important for researchers and policy formulators in making critical decisions on water availability and use in communities. The Western Sahel, which comprises Mali is considered as one of the vulnerable regions to climate change, and also encountered the challenges of climatic shocks such as flood and drought. This research therefore sought to investigate climate change effects on hydrological events and trends in Sahelian rainfall intensity using Bamako (Mali) as a case study from 1991 to 2020, as limited data availability did not allow an extended period of study. Monthly observed data provided by MALI-METEO was used to validate daily rainfalls data from African Rainfall Climatology Version 2 (ARC2) satellite-based rainfall product on monthly basis. The validated model performance used Nash-Sutcliffe Efficiency (NSE) and Percent Bias (PBAIS) and gave results of 0.904 and 1.0506 respectively. Trends in annual maximum temperatures and rainfalls were analyzed using Mann-Kendall trend test. The result indicated that the trend in annual maximum rainfalls was decreasing, while annual total rainfall was increasing but not significant at 5% significance level. The rate of increase in annual total rainfalls was 0.475 mm/year according to the observed annual rainfall series and decreased to 0.68 mm/year in annual maximum. The analysis further found that annual maximum temperatures were increasing at the rate of 0.03°C/year at 5% significance level. To provide more accurate climate predictions, it is recommended that further studies on rainfall and temperature with data sets spanning 60 - 90 years be carried out.

A Railway Fastener Inspection Method Based on Abnormal Sample Generation

Regular fastener detection is necessary to ensure the safety of railways.However,the number of abnormal fasteners is significantly lower than the number of normal fasteners in real railways.Existing supervised inspectionmethods have insufficient detection ability in cases of imbalanced samples.To solve this problem,we propose an approach based on deep convolutional neural networks(DCNNs),which consists of three stages:fastener localization,abnormal fastener sample generation based on saliency detection,and fastener state inspection.First,a lightweight YOLOv5s is designed to achieve fast and precise localization of fastener regions.Then,the foreground clip region of a fastener image is extracted by the designed fastener saliency detection network(F-SDNet),combined with data augmentation to generate a large number of abnormal fastener samples and balance the number of abnormal and normal samples.Finally,a fastener inspection model called Fastener ResNet-8 is constructed by being trained with the augmented fastener dataset.Results show the effectiveness of our proposed method in solving the problem of sample imbalance in fastener detection.Qualitative and quantitative comparisons show that the proposed F-SDNet outperforms other state-of-the-art methods in clip region extraction,reaching MAE and max F-measure of 0.0215 and 0.9635,respectively.In addition,the FPS of the fastener state inspection model reached 86.2,and the average accuracy reached 98.7%on 614 augmented fastener test sets and 99.9%on 7505 real fastener datasets.

Development of track geometry inspection equipment for high-speed comprehensive inspection train in China

Purpose–This study aims to analyze the development direction of track geometry inspection equipment for high-speed comprehensive inspection train in China.Design/methodology/approach–The development of track geometry inspection equipment for highspeed comprehensive inspection train in China in the past 20 years can be divided into 3 stages.Track geometry inspection equipment 1.0 is the stage of analog signal.At the stage 1.0,the first priority is to meet the China’s railways basic needs of pre-operation joint debugging,safety assessment and daily dynamic inspection,maintenance and repair after operation.Track geometry inspection equipment 2.0 is the stage of digital signal.At the stage 2.0,it is important to improve stability and reliability of track geometry inspection equipment by upgrading the hardware sensors and improving software architecture.Track geometry inspection equipment 3.0 is the stage of lightweight.At the stage 3.0,miniaturization,low power consumption,self-running and green economy are co-developing on demand.Findings–The ability of track geometry inspection equipment for high-speed comprehensive inspection train will be expanded.The dynamic inspection of track stiffness changes will be studied under loaded and unloaded conditions in response to the track local settlement,track plate detachment and cushion plate failure.The dynamic measurement method of rail surface slope and vertical curve radius will be proposed,to reveal the changes in railway profile parameters of high-speed railways and the relationship between railway profile,track irregularity and subsidence of subgrade and bridges.The 200 m cut-off wavelength of track regularity will be researched to adapt to the operating speed of 400 km/h.Originality/value–The research can provide new connotations and requirements of track geometry inspection equipment for high-speed comprehensive inspection train in the new railway stage.

Central environmental protection inspection and carbon emission reduction: A tripartite evolutionary game model from the perspective of carbon neutrality

Since the carbon neutrality target was proposed,many countries have been facing severe challenges to carbon emission reduction sustainably.This study is conducted using a tripartite evolutionary game model to explore the impact of the central environmental protection inspection(CEPI)on driving carbon emission reduction,and to study what factors influence the strategic choices of each party and how they interact with each other.The research results suggest that local governments and manufacturing enterprises would choose strategies that are beneficial to carbon reduction when CEPI increases.When the initial willingness of all parties increases 20%,50%—80%,the time spent for the whole system to achieve stability decreases from 100%,60%—30%.The evolutionary result of“thorough inspection,regulation implementation,low-carbon management”is the best strategy for the tripartite evolutionary game.Moreover,the smaller the cost and the larger the benefit,the greater the likelihood of the three-party game stability strategy appears.This study has important guiding significance for other developing countries to promote carbon emission reduction by environmental policy.

Distributed Resource Allocation in Dispersed Computing Environment Based on UAV Track Inspection in Urban Rail Transit

With the rapid development of urban rail transit,the existing track detection has some problems such as low efficiency and insufficient detection coverage,so an intelligent and automatic track detectionmethod based onUAV is urgently needed to avoid major safety accidents.At the same time,the geographical distribution of IoT devices results in the inefficient use of the significant computing potential held by a large number of devices.As a result,the Dispersed Computing(DCOMP)architecture enables collaborative computing between devices in the Internet of Everything(IoE),promotes low-latency and efficient cross-wide applications,and meets users’growing needs for computing performance and service quality.This paper focuses on examining the resource allocation challenge within a dispersed computing environment that utilizes UAV inspection tracks.Furthermore,the system takes into account both resource constraints and computational constraints and transforms the optimization problem into an energy minimization problem with computational constraints.The Markov Decision Process(MDP)model is employed to capture the connection between the dispersed computing resource allocation strategy and the system environment.Subsequently,a method based on Double Deep Q-Network(DDQN)is introduced to derive the optimal policy.Simultaneously,an experience replay mechanism is implemented to tackle the issue of increasing dimensionality.The experimental simulations validate the efficacy of the method across various scenarios.

Calibration of CO and CO2 Monitors Used in Periodic Inspection of Vehicles at Fixed Stations for Environmental Control

Global efforts for environmental cleanliness through the control of gaseous emissions from vehicles are gaining momentum and attracting increasing attention. Calibration plays a crucial role in these efforts by ensuring the quantitative assessment of emissions for informed decisions on environmental treatments. This paper describes a method for the calibration of CO/CO2 monitors used for periodic inspections of vehicles in cites. The calibration was performed in the selected ranges: 900 - 12,000 µmol/mol for CO and 2000 - 20,000 µmol/mol for CO2. The traceability of the measurement results to the SI units was ensured by using certified reference materials from CO/N2 and CO2/N2 primary gas mixtures. The method performance was evaluated by assessing its linearity, accuracy, precision, bias, and uncertainty of the calibration results. The calibration data exhibited a strong linear trend with R² values close to 1, indicating an excellent fit between the measured values and the calibration lines. Precision, expressed as relative standard deviation (%RSD), ranged from 0.48 to 4.56% for CO and from 0.97 to 3.53% for CO2, staying well below the 5% threshold for reporting results at a 95% confidence level. Accuracy measured as percent recovery, was consistently high (≥ 99.1%) for CO and ranged from 84.90% to 101.54% across the calibration range for CO2. In addition, the method exhibited minimal bias for both CO and CO2 calibrations and thus provided a reliable and accurate approach for calibrating CO/CO2 monitors used in vehicle inspections. Thus, it ensures the effectiveness of exhaust emission control for better environment.

Automatic Road Tunnel Crack Inspection Based on Crack Area Sensing and Multiscale Semantic Segmentation

The detection of crack defects on the walls of road tunnels is a crucial step in the process of ensuring travel safetyand performing routine tunnel maintenance. The automatic and accurate detection of cracks on the surface of roadtunnels is the key to improving the maintenance efficiency of road tunnels. Machine vision technology combinedwith a deep neural network model is an effective means to realize the localization and identification of crackdefects on the surface of road tunnels.We propose a complete set of automatic inspection methods for identifyingcracks on the walls of road tunnels as a solution to the problem of difficulty in identifying cracks during manualmaintenance. First, a set of equipment applied to the real-time acquisition of high-definition images of walls inroad tunnels is designed. Images of walls in road tunnels are acquired based on the designed equipment, whereimages containing crack defects are manually identified and selected. Subsequently, the training and validationsets used to construct the crack inspection model are obtained based on the acquired images, whereas the regionscontaining cracks and the pixels of the cracks are finely labeled. After that, a crack area sensing module is designedbased on the proposed you only look once version 7 model combined with coordinate attention mechanism (CAYOLOV7) network to locate the crack regions in the road tunnel surface images. Only subimages containingcracks are acquired and sent to the multiscale semantic segmentation module for extraction of the pixels to whichthe cracks belong based on the DeepLab V3+ network. The precision and recall of the crack region localizationon the surface of a road tunnel based on our proposed method are 82.4% and 93.8%, respectively. Moreover, themean intersection over union (MIoU) and pixel accuracy (PA) values for achieving pixel-level detection accuracyare 76.84% and 78.29%, respectively. The experimental results on the dataset show that our proposed two-stagedetection method outperforms other state-of-the-art models in crack region localization and detection. Based onour proposedmethod, the images captured on the surface of a road tunnel can complete crack detection at a speed often frames/second, and the detection accuracy can reach 0.25 mm, which meets the requirements for maintenanceof an actual project. The designed CA-YOLO V7 network enables precise localization of the area to which a crackbelongs in images acquired under different environmental and lighting conditions in road tunnels. The improvedDeepLab V3+ network based on lightweighting is able to extract crack morphology in a given region more quicklywhile maintaining segmentation accuracy. The established model combines defect localization and segmentationmodels for the first time, realizing pixel-level defect localization and extraction on the surface of road tunnelsin complex environments, and is capable of determining the actual size of cracks based on the physical coordinatesystemafter camera calibration. The trainedmodelhas highaccuracy andcanbe extendedandapplied to embeddedcomputing devices for the assessment and repair of damaged areas in different types of road tunnels.

Unmanned Aerial Vehicle Inspection Routing and Scheduling for Engineering Management

Technological advancements in unmanned aerial vehicles(UAVs)have revolutionized various industries,enabling the widespread adoption of UAV-based solutions.In engineering management,UAV-based inspection has emerged as a highly efficient method for identifying hidden risks in high-risk construction environments,surpassing traditional inspection techniques.Building on this foundation,this paper delves into the optimization of UAV inspection routing and scheduling,addressing the complexity introduced by factors such as no-fly zones,monitoring-interval time windows,and multiple monitoring rounds.To tackle this challenging problem,we propose a mixed-integer linear programming(MILP)model that optimizes inspection task assignments,monitoring sequence schedules,and charging decisions.The comprehensive consideration of these factors differentiates our problem from conventional vehicle routing problem(VRP),leading to a mathematically intractable model for commercial solvers in the case of large-scale instances.To overcome this limitation,we design a tailored variable neighborhood search(VNS)metaheuristic,customizing the algorithm to efficiently solve our model.Extensive numerical experiments are conducted to validate the efficacy of our proposed algorithm,demonstrating its scalability for both large-scale and real-scale instances.Sensitivity experiments and a case study based on an actual engineering project are also conducted,providing valuable insights for engineering managers to enhance inspection work efficiency.