Multi-model ensemble forecasting of 10-m wind speed over eastern China based on machine learning optimization

风对人类活动和电力运行有重大影响,准确预报短期风速具有深远的社会和经济意义.基于中国东部100个站点,本研究首先评估了5个业务模式对10米风速的预报能力,日本气象厅JMA模式在减少预报误差方面表现最好.进一步,利用5种数值模式和多种机器学习方法,将动力和统计相结合,对每个站点分别进行了特征工程和机器学习算法优选,建立了10米风速多模式集成预报模型。针对24至96小时预报时长,将该方法的预报性能与基于岭回归的多模式集成和JMA单模式进行比较.结果表明,基于机器学习优选的多模型集成方法可以将JMA模式的预报误差降低39%以上,预报效果的提升在11月最明显.此外,该方法优于基于岭回归的多模式集成方法.

Validation of the effects of temperature simulated by multi-model ensemble and prediction of mean temperature changes for the next three decades in China

Using series of daily average temperature observations over the period of 1961-1999 of 701 meteorological stations in China, and simulated results of 20 global climate models （such as BCCR_BCM2.0, CGCM3T47） during the same period as the observation, we validate and analyze the simulated results of the models by using three factor statistical method, achieve the results of mul- ti-model ensemble, test and verify the results of multi-model ensemble by using the observation data during the period of 1991-1999. Finally, we analyze changes of the annual mean temperature result of multi-mode ensemble prediction for the period of 2011-2040 under the emission scenarios A2, A1B and B 1. Analyzed results show that： （1） Global climate models can repro- duce Chinese regional spatial distribution of annual mean temperature, especially in low latitudes and eastern China. （2） With the factor of the trend of annual mean temperature changes in reference period, there is an obvious bias between the model and the observation. （3） Testing the result of multi-model ensemble during the period of 1991-1999, we can simulate the trend of temper- ature increase. Compared to observation, the result of different weighing multi-model ensemble prediction is better than the same weighing ensemble. （4） For the period of 20ll-2040, the growth of the annual mean temperature in China, which results from multi-mode ensemble prediction, is above 1℃. In the spatial distribution of annual mean temperature, under the emission scenarios of A2, A1B and B 1, the trend of growth in South China region is the smallest, the increment is less than or equals to 0.8℃; the trends in the northwestern region and south of the Qinghai-Tibet Plateau are the largest, the increment is more than 1℃.

Statistical Downscaling for Multi-Model Ensemble Prediction of Summer Monsoon Rainfall in the Asia-Pacific Region Using Geopotential Height Field

The 21-yr ensemble predictions of model precipitation and circulation in the East Asian and western North Pacific （Asia-Pacific） summer monsoon region （0°-50°N, 100° 150°E） were evaluated in nine different AGCM, used in the Asia-Pacific Economic Cooperation Climate Center （APCC） multi-model ensemble seasonal prediction system. The analysis indicates that the precipitation anomaly patterns of model ensemble predictions are substantially different from the observed counterparts in this region, but the summer monsoon circulations are reasonably predicted. For example, all models can well produce the interannual variability of the western North Pacific monsoon index （WNPMI） defined by 850 hPa winds, but they failed to predict the relationship between WNPMI and precipitation anomalies. The interannual variability of the 500 hPa geopotential height （GPH） can be well predicted by the models in contrast to precipitation anomalies. On the basis of such model performances and the relationship between the interannual variations of 500 hPa GPH and precipitation anomalies, we developed a statistical scheme used to downscale the summer monsoon precipitation anomaly on the basis of EOF and singular value decomposition （SVD）. In this scheme, the three leading EOF modes of 500 hPa GPH anomaly fields predicted by the models are firstly corrected by the linear regression between the principal components in each model and observation, respectively. Then, the corrected model GPH is chosen as the predictor to downscale the precipitation anomaly field, which is assembled by the forecasted expansion coefficients of model 500 hPa GPH and the three leading SVD modes of observed precipitation anomaly corresponding to the prediction of model 500 hPa GPH during a 19-year training period. The cross-validated forecasts suggest that this downscaling scheme may have a potential to improve the forecast skill of the precipitation anomaly in the South China Sea, western North Pacific and the East Asia Pacific regions, where the anomaly correlation coefficient （ACC） has been improved by 0.14, corresponding to the reduced RMSE of 10.4% in the conventional multi-model ensemble （MME） forecast.

Improving Multi-model Ensemble Probabilistic Prediction of Yangtze River Valley Summer Rainfall

Seasonal prediction of summer rainfall over the Yangtze River valley（YRV） is valuable for agricultural and industrial production and freshwater resource management in China, but remains a major challenge. Earlier multi-model ensemble（MME） prediction schemes for summer rainfall over China focus on single-value prediction, which cannot provide the necessary uncertainty information, while commonly-used ensemble schemes for probability density function（PDF） prediction are not adapted to YRV summer rainfall prediction. In the present study, an MME PDF prediction scheme is proposed based on the ENSEMBLES hindcasts. It is similar to the earlier Bayesian ensemble prediction scheme, but with optimization of ensemble members and a revision of the variance modeling of the likelihood function. The optimized ensemble members are regressed YRV summer rainfall with factors selected from model outputs of synchronous 500-h Pa geopotential height as predictors. The revised variance modeling of the likelihood function is a simple linear regression with ensemble spread as the predictor. The cross-validation skill of 1960–2002 YRV summer rainfall prediction shows that the new scheme produces a skillful PDF prediction, and is much better-calibrated, sharper, and more accurate than the earlier Bayesian ensemble and raw ensemble.

A Bayesian Scheme for Probabilistic Multi-Model Ensemble Prediction of Summer Rainfall over the Yangtze River Valley

A Bayesian probabilistic prediction scheme of the Yangtze River Valley (YRV) summer rainfall is proposed to combine forecast information from multi-model ensemble dataset provided by ENSEMBLES project.Due to the low forecast skill of rainfall in dynamic models,the time series of regressed YRV summer rainfall are selected as ensemble members in the new scheme,instead of commonly-used YRV summer rainfall simulated by models.Each time series of regressed YRV summer rainfall is derived from a simple linear regression.The predictor in each simple linear regression is the skillfully simulated circulation or surface temperature factor which is highly linear with the observed YRV summer rainfall in the training set.The high correlation between the ensemble mean of these regressed YRV summer rainfall and observation benefit extracting more sample information from the ensemble system.The results show that the cross-validated skill of the new scheme over the period of 1960 to 2002 is much higher than equally-weighted ensemble,multiple linear regression,and Bayesian ensemble with simulated YRV summer rainfall as ensemble members.In addition,the new scheme is also more skillful than reference forecasts (random forecast at a 0.01 significance level for ensemble mean and climatology forecast for probability density function).

A Hybrid Neural Network Model for Marine Dissolved Oxygen Concentrations Time-Series Forecasting Based on Multi-Factor Analysis and a Multi-Model Ensemble

Dissolved oxygen(DO)is an important indicator of aquaculture,and its accurate forecasting can effectively improve the quality of aquatic products.In this paper,a new DO hybrid forecasting model is proposed that includes three stages:multi-factor analysis,adaptive decomposition,and an optimizationbased ensemble.First,considering the complex factors affecting DO,the grey relational(GR)degree method is used to screen out the environmental factors most closely related to DO.The consideration of multiple factors makes model fusion more effective.Second,the series of DO,water temperature,salinity,and oxygen saturation are decomposed adaptively into sub-series by means of the empirical wavelet transform(EWT)method.Then,five benchmark models are utilized to forecast the sub-series of EWT decomposition.The ensemble weights of these five sub-forecasting models are calculated by particle swarm optimization and gravitational search algorithm(PSOGSA).Finally,a multi-factor ensemble model for DO is obtained by weighted allocation.The performance of the proposed model is verified by timeseries data collected by the pacific islands ocean observing system(PacIOOS)from the WQB04 station at Hilo.The evaluation indicators involved in the experiment include the Nash–Sutcliffe efficiency(NSE),Kling–Gupta efficiency(KGE),mean absolute percent error(MAPE),standard deviation of error(SDE),and coefficient of determination(R^(2)).Example analysis demonstrates that:①The proposed model can obtain excellent DO forecasting results;②the proposed model is superior to other comparison models;and③the forecasting model can be used to analyze the trend of DO and enable managers to make better management decisions.

STUDY OF THE MODIFICATION OF MULTI-MODEL ENSEMBLE SCHEMES FOR TROPICAL CYCLONE FORECASTS

This study investigates multi-model ensemble forecasts of track and intensity of tropical cyclones over the western Pacific, based on forecast outputs from the China Meteorological Administration, European Centre for Medium-Range Weather Forecasts, Japan Meteorological Agency and National Centers for Environmental Prediction in the THORPEX Interactive Grand Global Ensemble(TIGGE) datasets. The multi-model ensemble schemes, namely the bias-removed ensemble mean(BREM) and superensemble(SUP), are compared with the ensemble mean(EMN) and single-model forecasts. Moreover, a new model bias estimation scheme is investigated and applied to the BREM and SUP schemes. The results showed that, compared with single-model forecasts and EMN, the multi-model ensembles of the BREM and SUP schemes can have smaller errors in most cases. However, there were also circumstances where BREM was less skillful than EMN, indicating that using a time-averaged error as model bias is not optimal. A new model bias estimation scheme of the biweight mean is introduced. Through minimizing the negative influence of singular errors, this scheme can obtain a more accurate model bias estimation and improve the BREM forecast skill. The application of the biweight mean in the bias calculation of SUP also resulted in improved skill. The results indicate that the modification of multi-model ensemble schemes through this bias estimation method is feasible.

Intercomparison of multi-model ensemble-processing strategies within a consistent framework for climate projection in China

Climate change adaptation and relevant policy-making need reliable projections of future climate.Methods based on multi-model ensemble are generally considered as the most efficient way to achieve the goal.However,their efficiency varies and inter-comparison is a challenging task,as they use a variety of target variables,geographic regions,time periods,or model pools.Here,we construct and use a consistent framework to evaluate the performance of five ensemble-processing methods,i.e.,multimodel ensemble mean(MME),rank-based weighting(RANK),reliability ensemble averaging(REA),climate model weighting by independence and performance(ClimWIP),and Bayesian model averaging(BMA).We investigate the annual mean temperature(Tav)and total precipitation(Prcptot)changes(relative to 1995–2014)over China and its seven subregions at 1.5 and 2℃warming levels(relative to pre-industrial).All ensemble-processing methods perform better than MME,and achieve generally consistent results in terms of median values.But they show different results in terms of inter-model spread,served as a measure of uncertainty,and signal-to-noise ratio(SNR).ClimWIP is the most optimal method with its good performance in simulating current climate and in providing credible future projections.The uncertainty,measured by the range of 10th–90th percentiles,is reduced by about 30%for Tav,and 15%for Prcptot in China,with a certain variation among subregions.Based on ClimWIP,and averaged over whole China under 1.5/2℃global warming levels,Tav increases by about 1.1/1.8℃(relative to 1995–2014),while Prcptot increases by about 5.4%/11.2%,respectively.Reliability of projections is found dependent on investigated regions and indices.The projection for Tav is credible across all regions,as its SNR is generally larger than 2,while the SNR is lower than 1 for Prcptot over most regions under 1.5℃warming.The largest warming is found in northeastern China,with increase of 1.3(0.6–1.7)/2.0(1.4–2.6)℃(ensemble’s median and range of the 10th–90th percentiles)under 1.5/2℃warming,followed by northern and northwestern China.The smallest but the most robust warming is in southwestern China,with values exceeding 0.9(0.6–1.1)/1.5(1.1–1.7)℃.The most robust projection and largest increase is achieved in northwestern China for Prcptot,with increase of 9.1%(–1.6–24.7%)/17.9%(0.5–36.4%)under 1.5/2℃warming.Followed by northern China,where the increase is 6.0%(–2.6–17.8%)/11.8%(2.4–25.1%),respectively.The precipitation projection is of large uncertainty in southwestern China,even with uncertain sign of variation.For the additional half-degree warming,Tav increases more than 0.5℃throughout China.Almost all regions witness an increase of Prcptot,with the largest increase in northwestern China.

Assessment of total and extreme precipitation over central Asia via statistical downscaling: Added value and multi-model ensemble projection

Central Asia(CA)is highly sensitive and vulnerable to changes in precipitation due to global warming,so the projection of precipitation extremes is essential for local climate risk assessment.However,global and regional climate models often fail to reproduce the observed daily precipitation distribution and hence extremes,especially in areas with complex terrain.In this study,we proposed a statistical downscaling(SD)model based on quantile delta mapping to assess and project eight precipitation indices at 73 meteorological stations across CA driven by ERA5 reanalysis data and simulations of 10 global climate models(GCMs)for present and future(2081-2100)periods under two shared socioeconomic pathways(SSP245 and SSP585).The reanalysis data and raw GCM outputs clearly underestimate mean precipitation intensity(SDII)and maximum 1-day precipitation(RX1DAY)and overestimate the number of wet days(R1MM)and maximum consecutive wet days(CWD)at stations across CA.However,the SD model effectively reduces the biases and RMSEs of the modeled precipitation indices compared to the observations.Also it effectively adjusts the distributional biases in the downscaled daily precipitation and indices at the stations across CA.In addition,it is skilled in capturing the spatial patterns of the observed precipitation indices.Obviously,SDII and RX1DAY are improved by the SD model,especially in the southeastern mountainous area.Under the intermediate scenario(SSP245),our SD multi-model ensemble projections project significant and robust increases in SDII and total extreme precipitation(R95PTOT)of 0.5 mm d^(-1) and 19.7 mm,respectively,over CA at the end of the 21st century(2081-2100)compared to the present values(1995-2014).More pronounced increases in indices R95PTOT,SDII,number of very wet days(R10MM),and RX1DAY are projected under the higher emission scenario(SSP585),particularly in the mountainous southeastern region.The SD model suggested that SDII and RX1DAY will likely rise more rapidly than those projected by previous model simulations over CA during the period 2081-2100.The SD projection of the possible future changes in precipitation and extremes improves the knowledge base for local risk management and climate change adaptation in CA.

基于改进SMOTE算法和Ensemble模型的学习结果预测方法

为解决不同领域的数据分类和预测任务中单个机器学习算法适用性较差的问题,以及缓解数据集严重不平衡对预测性能的影响,提出了基于合成少数类过采样(SMOTE)和Ensemble集成模型的数据分类方法。传统SMOTE算法通过对少数类样本进行插值来生成新的合成样本,合成样本中存在噪声和样本间相似性较高的问题。为此,提出了改进的SMOTE算法,通过距离计算移除噪声样本和易混淆样本,得到高区分度的纯净合成样本。然后,利用Ensemble方法调整样本和分类器权重,并组成分类效果更好的强分类器。在公开在线学习数据集Kalboard360上的实验结果表明,使用极限随机树(ERT)分类器时,结合改进SMOTE和Ensemble模型后实现了97.9%的预测准确度,比单个ERT分类器提升了5.5%,证明所提改进SMOTE算法能够生成高质量的均衡化数据,且集成学习模型的性能显著优于单个机器学习算法。

Statistical Process Monitoring Based on Ensemble Structure Analysis

Dear Editor,This letter presents a novel process monitoring model based on ensemble structure analysis(ESA).The ESA model takes advantage of principal component analysis(PCA),locality preserving projections(LPP),and multi-manifold projections(MMP)models,and then combines the multiple solutions within an ensemble result through Bayesian inference.In the developed ESA model,different structure features of the given dataset are taken into account simultaneously,the suitability and reliability of the ESA-based monitoring model are then illustrated through comparison.Introduction:The requirement for ensuring safe operation and improving process efficiency has led to increased research activity in the field of process monitoring.

EDSUCh:A robust ensemble data summarization method for effective medical diagnosis

Identifying rare patterns for medical diagnosis is a challenging task due to heterogeneity and the volume of data.Data summarization can create a concise version of the original data that can be used for effective diagnosis.In this paper,we propose an ensemble summarization method that combines clustering and sampling to create a summary of the original data to ensure the inclusion of rare patterns.To the best of our knowledge,there has been no such technique available to augment the performance of anomaly detection techniques and simultaneously increase the efficiency of medical diagnosis.The performance of popular anomaly detection algorithms increases significantly in terms of accuracy and computational complexity when the summaries are used.Therefore,the medical diagnosis becomes more effective,and our experimental results reflect that the combination of the proposed summarization scheme and all underlying algorithms used in this paper outperforms the most popular anomaly detection techniques.

基于Local Cascade Ensemble方法的胎儿健康自动分类

为更好地自动评估胎儿宫内状态,提出一种基于local cascade ensemble(LCE)方法的胎儿健康状态分类模型。选用UCI数据集,使用ADASYN方法对不平衡数据集进行填充平衡,接着结合随机森林算法对数据特征进行选择,最后使用LCE方法对胎儿状态进行自动分类。实验结果表明,所提出模型使用的方法平均准确率、精确率、召回率和F1分数分别达到了0.9554、0.9054、0.9557和0.9290,对比传统的机器学习算法能得到更好的分类效果,有效降低了误判率。

Entropy of deterministic trajectory via trajectories ensemble

We present a formulation of the single-trajectory entropy using the trajectories ensemble. The single-trajectory entropy is affected by its surrounding trajectories via the distribution function. The single-trajectory entropies are studied in two typical potentials, i.e., harmonic potential and double-well potential, and in viscous environment by interacting trajectory method. The results of the trajectory methods are in agreement well with the numerical methods(Monte Carlo simulation and difference equation). The single-trajectory entropies increasing(decreasing) could be caused by absorption(emission) heat from(to) the thermal environment. Also, some interesting trajectories, which correspond to the rare evens in the processes, are demonstrated.

A redundant subspace weighting procedure for clock ensemble

A redundant-subspace-weighting(RSW)-based approach is proposed to enhance the frequency stability on a time scale of a clock ensemble.In this method,multiple overlapping subspaces are constructed in the clock ensemble,and the weight of each clock in this ensemble is defined by using the spatial covariance matrix.The superimposition average of covariances in different subspaces reduces the correlations between clocks in the same laboratory to some extent.After optimizing the parameters of this weighting procedure,the frequency stabilities of virtual clock ensembles are significantly improved in most cases.

Securing Cloud-Encrypted Data:Detecting Ransomware-as-a-Service(RaaS)Attacks through Deep Learning Ensemble

Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware operations.This study provides a new approach for RaaS attack detection which uses an ensemble of deep learning models.For this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is analyzed.In the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are developed.Later,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested methodology.The proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%F1-score.The empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual MLPmodels.In expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.

Physics-Constrained Robustness Enhancement for Tree Ensembles Applied in Smart Grid

With the widespread use of machine learning(ML)technology,the operational efficiency and responsiveness of power grids have been significantly enhanced,allowing smart grids to achieve high levels of automation and intelligence.However,tree ensemble models commonly used in smart grids are vulnerable to adversarial attacks,making it urgent to enhance their robustness.To address this,we propose a robustness enhancement method that incorporates physical constraints into the node-splitting decisions of tree ensembles.Our algorithm improves robustness by developing a dataset of adversarial examples that comply with physical laws,ensuring training data accurately reflects possible attack scenarios while adhering to physical rules.In our experiments,the proposed method increased robustness against adversarial attacks by 100%when applied to real grid data under physical constraints.These results highlight the advantages of our method in maintaining efficient and secure operation of smart grids under adversarial conditions.

An Initial Perturbation Method for the Multiscale Singular Vector in Global Ensemble Prediction

Ensemble prediction is widely used to represent the uncertainty of single deterministic Numerical Weather Prediction(NWP) caused by errors in initial conditions(ICs). The traditional Singular Vector(SV) initial perturbation method tends only to capture synoptic scale initial uncertainty rather than mesoscale uncertainty in global ensemble prediction. To address this issue, a multiscale SV initial perturbation method based on the China Meteorological Administration Global Ensemble Prediction System(CMA-GEPS) is proposed to quantify multiscale initial uncertainty. The multiscale SV initial perturbation approach entails calculating multiscale SVs at different resolutions with multiple linearized physical processes to capture fast-growing perturbations from mesoscale to synoptic scale in target areas and combining these SVs by using a Gaussian sampling method with amplitude coefficients to generate initial perturbations. Following that, the energy norm,energy spectrum, and structure of multiscale SVs and their impact on GEPS are analyzed based on a batch experiment in different seasons. The results show that the multiscale SV initial perturbations can possess more energy and capture more mesoscale uncertainties than the traditional single-SV method. Meanwhile, multiscale SV initial perturbations can reflect the strongest dynamical instability in target areas. Their performances in global ensemble prediction when compared to single-scale SVs are shown to(i) improve the relationship between the ensemble spread and the root-mean-square error and(ii) provide a better probability forecast skill for atmospheric circulation during the late forecast period and for short-to medium-range precipitation. This study provides scientific evidence and application foundations for the design and development of a multiscale SV initial perturbation method for the GEPS.

Classification and Comprehension of Software Requirements Using Ensemble Learning

The software development process mostly depends on accurately identifying both essential and optional features.Initially,user needs are typically expressed in free-form language,requiring significant time and human resources to translate these into clear functional and non-functional requirements.To address this challenge,various machine learning(ML)methods have been explored to automate the understanding of these requirements,aiming to reduce time and human effort.However,existing techniques often struggle with complex instructions and large-scale projects.In our study,we introduce an innovative approach known as the Functional and Non-functional Requirements Classifier(FNRC).By combining the traditional random forest algorithm with the Accuracy Sliding Window(ASW)technique,we develop optimal sub-ensembles that surpass the initial classifier’s accuracy while using fewer trees.Experimental results demonstrate that our FNRC methodology performs robustly across different datasets,achieving a balanced Precision of 75%on the PROMISE dataset and an impressive Recall of 85%on the CCHIT dataset.Both datasets consistently maintain an F-measure around 64%,highlighting FNRC’s ability to effectively balance precision and recall in diverse scenarios.These findings contribute to more accurate and efficient software development processes,increasing the probability of achieving successful project outcomes.

Application of multi-algorithm ensemble methods in high-dimensional and small-sample data of geotechnical engineering:A case study of swelling pressure of expansive soils

Geotechnical engineering data are usually small-sample and high-dimensional,which brings a lot of challenges in predictive modeling.This paper uses a typical high-dimensional and small-sample swell pressure(P_(s))dataset to explore the possibility of using multi-algorithm hybrid ensemble and dimensionality reduction methods to mitigate the uncertainty of soil parameter prediction.Based on six machine learning(ML)algorithms,the base learner pool is constructed,and four ensemble methods,Stacking(SG),Blending(BG),Voting regression(VR),and Feature weight linear stacking(FWL),are used for the multi-algorithm ensemble.Furthermore,the importance of permutation is used for feature dimensionality reduction to mitigate the impact of weakly correlated variables on predictive modeling.The results show that the proposed methods are superior to traditional prediction models and base ML models,where FWL is more suitable for modeling with small-sample datasets,and dimensionality reduction can simplify the data structure and reduce the adverse impact of the small-sample effect,which points the way to feature selection for predictive modeling.Based on the ensemble methods,the feature importance of the five primary factors affecting P_(s) is the maximum dry density(31.145%),clay fraction(15.876%),swell percent(15.289%),plasticity index(14%),and optimum moisture content(13.69%),the influence of input parameters on P_(s) is also investigated,in line with the findings of the existing literature.