Estimation of land surface evapotranspiration using the METRIC model in Nepal

Climatic and atmospheric properties vary significantly within a small area for a topographically diverse region like Nepal.Remote sensing can be used for large-scale monitoring of atmospheric parameters in such diverse terrains.This work evaluates the Landsat-based METRIC(Mapping Evapotranspiration at High Resolution with Internalized Calibration)model for estimating Evapotranspiration(ET)in Nepal.The slope and aspect of terrain are accounted for in our implementation,making the model suitable for regions with topographical variations.The estimations obtained from the model were compared with ground-based measurements.The root-meansquare error for hourly ET(daily ET)was 0.06 mm h-1(1.24 mm d-1),while the mean bias error was0.03 mm h-1(0.29 mm d-1).These results are comparable with results from other studies in the literature that have used the METRIC model for different regions of the world.Thus,this work validates the applicability of the METRIC model for ET estimation in a mountainous area like Nepal.Further,this implementation provides ET estimation at a very high resolution of 30 m compared to the best available resolution of 5 km in earlier works,without compromising on the accuracy.ET estimation with high resolution over a large region in Nepal has applications in agricultural planning and monitoring,among others.

Online RGB-D person re-identification based on metric model update

Person re-identification （re-id） on robot platform is an important application for human-robot- interaction （HRI）, which aims at making the robot recognize the around persons in varying scenes. Although many effective methods have been proposed for surveillance re-id in recent years, re-id on robot platform is still a novel unsolved problem. Most existing methods adapt the supervised metric learning offline to improve the accuracy. However, these methods can not adapt to unknown scenes. To solve this problem, an online re-id framework is proposed. Considering that robotics can afford to use high-resolution RGB-D sensors and clear human face may be captured, face information is used to update the metric model. Firstly, the metric model is pre-trained offline using labeled data. Then during the online stage, we use face information to mine incorrect body matching pairs which are collected to update the metric model online. In addition, to make full use of both appearance and skeleton information provided by RGB-D sensors, a novel feature funnel model （FFM） is proposed. Comparison studies show our approach is more effective and adaptable to varying environments.

基于METRIC模型的巴基斯坦农业区蒸散量估算

蒸散量是水分循环和能量循环的重要载体,精确估算农田蒸散量对农业水资源管理具有重要意义。巴基斯坦农业区是世界上重要的灌溉农区之一,如何基于遥感技术估算区域实际蒸散量成为农业水资源精细化管理的基础和前提。本文利用MODIS数据、气象数据以及DEM数据,采用METRIC模型,估算了2019—2020年巴基斯坦农业区的实际蒸散量,并分析了不同作物生育期蒸散量的时空分布特征,以期为巴基斯坦农业水资源合理利用提供科学依据。研究结果表明:1)比较基于METRIC模型在日尺度和月尺度的蒸散估算结果与农业站点蒸渗仪的实际观测数据发现,二者的均方根误差分别为1.2 mm∙d^(−1)和25 mm∙month^(−1),相关系数分别为0.65和0.84;在空间上,与ETMonitor产品比较,METRIC模型估算结果的空间分布和量级更为合理。2)巴基斯坦农业区蒸散量的空间分布与种植结构密切相关,蒸散量自北向南总体呈阶梯递减格局,小麦、棉花、水稻和甘蔗生育期累积蒸散量分别为392 mm、652 mm、745 mm和1224 mm;就同一种作物来说,旁遮普省作物生育期累积蒸散量高于信德省。3)小麦生育期内月蒸散量呈先下降再上升后下降的变化特征;旁遮普省棉花生育期内月蒸散量呈“单峰”变化特征,信德省棉花生育期内月蒸散量呈“双峰”变化特征;水稻和甘蔗生育期内月蒸散量呈“单峰”变化特征。本研究实现了METRIC模型在巴基斯坦农业区的参数本地化应用和适用性分析,为基于遥感手段估算区域或农作物尺度蒸散量提供了方法借鉴,对揭示不同作物蒸散耗水的时空特征和区域农业水资源管理具有重要意义。

MultiDMet: Designing a Hybrid Multidimensional Metrics Framework to Predictive Modeling for Performance Evaluation and Feature Selection

In a competitive digital age where data volumes are increasing with time, the ability to extract meaningful knowledge from high-dimensional data using machine learning (ML) and data mining (DM) techniques and making decisions based on the extracted knowledge is becoming increasingly important in all business domains. Nevertheless, high-dimensional data remains a major challenge for classification algorithms due to its high computational cost and storage requirements. The 2016 Demographic and Health Survey of Ethiopia (EDHS 2016) used as the data source for this study which is publicly available contains several features that may not be relevant to the prediction task. In this paper, we developed a hybrid multidimensional metrics framework for predictive modeling for both model performance evaluation and feature selection to overcome the feature selection challenges and select the best model among the available models in DM and ML. The proposed hybrid metrics were used to measure the efficiency of the predictive models. Experimental results show that the decision tree algorithm is the most efficient model. The higher score of HMM (m, r) = 0.47 illustrates the overall significant model that encompasses almost all the user’s requirements, unlike the classical metrics that use a criterion to select the most appropriate model. On the other hand, the ANNs were found to be the most computationally intensive for our prediction task. Moreover, the type of data and the class size of the dataset (unbalanced data) have a significant impact on the efficiency of the model, especially on the computational cost, and the interpretability of the parameters of the model would be hampered. And the efficiency of the predictive model could be improved with other feature selection algorithms (especially hybrid metrics) considering the experts of the knowledge domain, as the understanding of the business domain has a significant impact.

The Digital Fingerprinting Method for Static Images Based on Weighted Hamming Metric and on Weighted Container Model

The algorithm of fingerprint constructing for still images based on weighted image structure model is proposed. The error correcting codes that are perfect in weighted Hamming metric are used as a base for fingerprint constructing.

Stock Price Prediction and Traditional Models: An Approach to Achieve Short-, Medium- and Long-Term Goals

A comparative analysis of deep learning models and traditional statistical methods for stock price prediction uses data from the Nigerian stock exchange. Historical data, including daily prices and trading volumes, are employed to implement models such as Long Short Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), Autoregressive Integrated Moving Average (ARIMA), and Autoregressive Moving Average (ARMA). These models are assessed over three-time horizons: short-term (1 year), medium-term (2.5 years), and long-term (5 years), with performance measured by Mean Squared Error (MSE) and Mean Absolute Error (MAE). The stability of the time series is tested using the Augmented Dickey-Fuller (ADF) test. Results reveal that deep learning models, particularly LSTM, outperform traditional methods by capturing complex, nonlinear patterns in the data, resulting in more accurate predictions. However, these models require greater computational resources and offer less interpretability than traditional approaches. The findings highlight the potential of deep learning for improving financial forecasting and investment strategies. Future research could incorporate external factors such as social media sentiment and economic indicators, refine model architectures, and explore real-time applications to enhance prediction accuracy and scalability.

基于Vari-Metric的舰载机可修复件库存配置模型

针对舰载机可修复备件配置问题,提出了基于Vari-Metric的多等级、多层级库存优化配置模型。首先,对舰载机保障过程进行分析;其次,进行可修复备件配置优化分析,根据舰载机保障特点,给出了备件短缺数、备件需求率和备件供应渠道模型;然后,给出基于边际优化算法的配置优化模型;最后,进行了实例分析,证明了模型的有效性,能够为保障人员备件配置优化提供一定的决策依据。

基于CreditMetrics模型评估银行信贷的信用风险

随着市场竞争日趋激烈,金融风险管理显得越来越重要。文章首先论述CreditMetrics模型的建模逻辑过程及其特点;基于风险价值(var)概念进行蒙特卡罗模拟,计算得出某商业银行信贷数据的核心参数:信用风险转移矩阵、门槛率、违约回复率以及最终的风险价值,进而利用这些参数测算出该商业银行贷款的风险等级及其分布。

Credit Metrics模型计算信用风险的实例分析

CreditMetrics作为计算资产组合信用风险的模型,是一个联系信用和证券市场的简单、动态的架构。本文从此模型出发,分别讨论了单个贷款和资产组合基于违约率,信用迁移概率的计算原理和实例,并对违约率的测算作了进一步的分析和讨论。

中文学术成果评价的Altmetrics发展及实践路径

为有效解决中文学术成果评价的Altmetrics应用问题,针对中文环境下Altmetrics评价数据基础设施缺失现状,在综述Altmetrics思想、方法、技术发展历程及中文学术成果应用实践的基础上,提出中文学术成果Altmetrics评价需要解决三方面的问题:利益相关者的Altmetrics应用诉求识别,中文Altmetrics工具开发,以引文为基础的传统计量与以网络空间数据为基础的Altmetrics计量的融合模型。只有解决中文环境下Altmetrics评价的这些基础性问题,中文学术成果评价才会具有坚实的数据基础设施。

程序复杂度度量系统pgrmetrics的设计与实现

介绍一种源程序度量系统的实现方法,它以可扩展程序对象模型作为其构建基础,将对源程序的 信息抽取变为对抽象对象模型的信息抽取,故而无论从设计和实现系统的角度看都显得较为简洁易行。

基于METRIC模型对可用度与费用关系曲线的研究

通过对可修备件管理多级法的分析研究,提出基于优化可修航空器材备件计划库存的方法,针对装备上的各项备件计算各个航空部门的最优库存量,航空备件需求量和其他参数,以此来寻求仓库短缺数最低,相当于寻求装备可用度最高。最终以可用度与费用最优关系曲线的形式推荐给航空部门备件主管人员。

Application of the Credit Metrics in the Credit Risk Management of Commercial Banks

Credit risk is one of the main risks the commercial banks faces all over the world,especially in the risk structure of the banks of China.In order to control credit risk more scientifically,we shall connect the qualitative analysis and the quantitative analysis.Put forward by J.P.Morgan Credit Metrics model is the application of the VaR in the field of credit risk,showing great advantage in quantitative bonds and credit risk of loan.This paper studies the Credit Metrics model and analyzes the hypothesis and framework of this model,attempting to explore the application of the model in China in order to promote the realization of the risk quantification of the commercial banks of China.

基于METRIC模型的江汉平原蒸散量估算适应性分析

蒸散发是整个水文循环中的关键环节,现有传统测量方法虽具有较高的点位精度,但空间代表性不足,无法满足大空间尺度的遥感估算。以半湿润区为主的江汉平原为研究区域,以遥感蒸散模型为核心手段,首次引入METRIC模型,探讨METRIC模型在江汉平原蒸散量估算中的适用性,并应用METRIC模型和SEBAL模型对研究区域进行遥感蒸散量反演,同时利用世界粮农组织(FAO)提供的P-M模型参考蒸散量计算公式,计算了气象站点当日参考蒸散量并进行估算误差对比。结果表明:(1)4日中蒸散量较小的3日METRIC模型估算误差较SEBAL模型更小,平均估算误差降低约9%,仅蒸散量较大的1日SEBAL模型较METRIC模型具有更高的估算精度,这表明蒸散量较小的时间段内,METRIC模型在江汉平原表现出良好的适用性,估算误差较SEBAL模型更小,具有更好的应用价值,而蒸散量较大的时间段内,SEBAL模型能够提供较METRIC模型更高的估算精度,具有更好的应用价值;根据季节交替使用两模型能够有效提高区域遥感蒸散量的估算精度;(2)不同蒸散量时两模型出现估算精度差异的最大影响因素为冷热像元即干湿边界的选取原则,SEBAL模型干湿边界的选取原则以水体作为湿边界,适合土壤蒸发量及植被散发量较小的干旱地区,而METRIC模型干湿边界的选取原则以湿润、温度较低的植被覆盖区域作为湿边界,增加了对植被散发的权重考虑,避免了高植被覆盖区域水分胁迫带来的精度影响,并以DEM数据对区域高程、地形坡度加以修正,可降低高程差所带来的"冷却效应",这些改进使该模型在蒸散量较小的时间段内取得了更高的估算精度;但蒸散量较大时,水体蒸散在总蒸散量中的权重大幅增加,降低了植被散发等对最终估算结果的影响程度,此时SEBAL模型以温度较低水体作为湿边界的选取原则使水体蒸散发在总蒸散量估算中占据更大的权重,相较METRIC模型在蒸散量较大时能够提供更高的估算精度。

基于风险价值和期权理论的Credit Metrics模型研究

Credit Metrics模型是国际金融界内流行的现代信用风险度量模型,它以风险价值VaR和期权定价思想为基础,以衡量信贷资产组合的风险价值为核心,用于识别贷款、债券等传统信贷产品的信用风险,开启了银行信用风险量化评估的先河,开创了现代信用风险度量研究的新领域,对中国商业银行的信用风险管理有一定的借鉴作用。

Research on Three-Echelon Inventory Model and Algorithm for Valuable Spare Parts in Weapon Equipment

According to the current situation of repair and supply for valuable spare parts in the weapon equipment of the PLA,using the research results of the repairable spare parts inventory theory,an inventory model of the valuable spare parts in three-echelon repair and supply system is established by expanding the classical METRIC model and theory,and the genetic algorithm is utilized to solve the model.In the algorithm,the chromosome representation and initial population production,the crossover and mutation operators are designed.By using an example,a simulation analysis is carried out to verify the model's correctness.

基于Credit Metrics模型的CDS定价研究

信用违约互换(Credit Default Swaps,CDS)作为当今国际上最流行的的信用衍生工具,被广泛应用于商业银行的信用风险管理中。Credit Metrics模型被广泛运用于度量信用风险的大小,在应用Credit Metrics模型计算商业银行贷款的VaR基础上探讨CDS的定价问题。以单笔贷款为例来说明该模型探讨CDS定价实际运用过程,对我国商业银行信用风险管理的提高有一定指导作用。

钢铁行业信贷风险评估分析--基于改进的Credit Metrics模型

在研究信用风险评估中借鉴传统信用评级模型,结合十四五规划中绿色发展的绿色生态指标,充分考虑到钢铁行业的特殊性,对Credit Metrics模型进行改进,通过对3家传统信用评级相同的钢铁企业进行实证分析,形成不同的VaR值并进行比较,分析可知ESG等级越高的钢铁企业越符合时代要求下的绿色发展。该方法更贴近当今时代的要求,对于公司有很好的警示性和方向性,利于银行把控资金回收的风险。

A Review on Evaluation Methods of Climate Modeling

There is scientific progress in the evaluation methods of recent Earth system models(ESMs).Methods range from single variable to multi-variables,multi-processes,multi-phenomena quantitative evaluations in five layers(spheres)of the Earth system,from climatic mean assessment to climate change(such as trends,periodicity,interdecadal variability),extreme values,abnormal characters and quantitative evaluations of phenomena,from qualitative assessment to quantitative calculation of reliability and uncertainty for model simulations.Researchers started considering independence and similarity between models in multi-model use,as well as the quantitative evaluation of climate prediction and projection efect and the quantitative uncertainty contribution analysis.In this manuscript,the simulations and projections by both CMIP5 and CMIP3 that have been published after 2007 are reviewed and summarized.

Impact of ionospheric irregularity on SBAS integrity:spatial threat modeling and improvement

The ionosphere, as the largest and least predictable error source, its behavior cannot be observed at all places simultaneously. The confidence bound, called the grid ionospheric vertical error(GIVE), can only be determined with the aid of a threat model which is used to restrict the expected ionospheric behavior. However, the spatial threat model at present widespread used, which is based on fit radius and relative centroid metric(RCM), is too conservative or the resulting GIVEs will be too large and will reduce the availability of satellite-based augmentation system(SBAS). In this paper, layered two-dimensional parameters, the vertical direction double RCMs, are introduced based on the spatial variability of the ionosphere. Comparing with the traditional threat model, the experimental results show that the user ionospheric vertical error(UIVE) average reduction rate reaches 16%. And the 95% protection level of conterminous United States(CONUS) is 28%, even under disturbed days, which reaches about 5% reduction rates.The results show that the system service performance has been improved better.