Automatic Determination of Stellar Atmospheric Parameters Using Neural Networks and Instance-Based Machine Learning

In this article we show how machine learning methods can beeffectively applied to the problem of automatically predictingstellar atmospheric parameters from spectral information, a veryimportant problem in stellar astronomy. We apply feedforwardneural networks, Kohonen's self-organizing maps andlocally-weighted regression to predict the stellar atmosphericparameters effective temperature, surface gravity and metallicityfrom spectral indices. Our experimental results show that thethree methods are capable of predicting the parameters with verygood accuracy. Locally weighted regression gives slightly betterresults than the other methods using the original dataset asinput, while self-organizing maps outperform the other methods when significant amounts of noise are added. We also implemented a heterogeneous ensemble of predictors, combining the results given by the three algorithms. This ensemble yields better results than any of the three algorithms alone, using both the original and the noisy data.     

Ensemble prediction of regional droughts using climate inputs and the SVM–copula approach

In this study, the climate teleconnections with meteorological droughts are analysed and used to develop ensemble drought prediction models using a support vector machine (SVM)–copula approach over Western Rajasthan (India). The meteorological droughts are identified using the Standardized Precipitation Index (SPI). In the analysis of large‐scale climate forcing represented by climate indices such as El Niño Southern Oscillation, Indian Ocean Dipole Mode and Atlantic Multidecadal Oscillation on regional droughts, it is found that regional droughts exhibits interannual as well as interdecadal variability. On the basis of potential teleconnections between regional droughts and climate indices, SPI‐based drought forecasting models are developed with up to 3 months' lead time. As traditional statistical forecast models are unable to capture nonlinearity and nonstationarity associated with drought forecasts, a machine learning technique, namely, support vector regression (SVR), is adopted to forecast the drought index, and the copula method is used to model the joint distribution of observed and predicted drought index. The copula‐based conditional distribution of an observed drought index conditioned on predicted drought index is utilized to simulate ensembles of drought forecasts. Two variants of drought forecast models are developed, namely a single model for all the periods in a year and separate models for each of the four seasons in a year. The performance of developed models is validated for predicting drought time series for 10 years' data. Improvement in ensemble prediction of drought indices is observed for combined seasonal model over the single model without seasonal partitions. The results show that the proposed SVM–copula approach improves the drought prediction capability and provides estimation of uncertainty associated with drought predictions. Copyright © 2013 John Wiley & Sons, Ltd.     

基于对流可分辨集合预报的阜宁龙卷概率预报研究

本文针对2016年6月23日江苏阜宁龙卷,设计了两组对流可分辨尺度集合预报:一组以ERA5再分析资料为初始和侧边界(CEFS_ERA5);另一组以NCEP GEFS为初始和侧边界(CEFS_GEFS),评估了两组试验对此次龙卷的预报能力。结果显示:两组对流尺度集合预报均有约半数以上成员能够再现龙卷超级单体的特征;2~5 km上升螺旋度(UH25)对本次龙卷超级单体有较好的预报指示意义。在上述分析的基础上,考虑位置预报偏差,提出了一种基于UH25的邻域龙卷概率预报产品,分析了龙卷概率预报技巧对关键参数邻域半径和UH25阈值的敏感性,CEFS_ERA5邻域半径取15个格点,UH25阈值取250 m²·s^-2最优;而CEFS_GEFS邻域半径取15个格点,UH25阈值取100 m²·s^-2最优。总的来说,邻域概率预报产品显著提升了对此次龙卷概率预报水平。     

A hybrid ensemble method for improved prediction of slope stability

Accurate prediction of slope stability is a significant issue in geomechanics with many artificial intelligence (AI) techniques being utilised. However, the application of AI has not reached its full potential because of the lack of more robust algorithms. In this paper, we proposed a hybrid ensemble method for the improved prediction of slope stability using classifier ensembles and genetic algorithm. Gaussian process classification, quadratic discriminant analysis, support vector machine, artificial neural networks, adaptive boosted decision trees, and k‐nearest neighbours were chosen to be individual AI techniques, and the weighted majority voting was used as the combination method. Validation method was chosen to be the 10‐fold cross‐validation, and performance measures were selected to be the accuracy, the receiver operating characteristic curve, and the area under the receiver operating characteristic curve (AUC). Grid search and genetic algorithm were used for the hyperparameter tuning and weight tuning respectively. The results show that the proposed hybrid ensemble method has great potential in improving the prediction of slope stability. Compared with individual classifiers, the optimum ensemble classifier achieved the highest AUC value (0.943) and the highest accuracy (0.902) on the testing set, denoting that the predictive performance has been improved. The optimum ensemble classifier with the Youden's cut‐off was recommended for slope stability prediction with respect to the AUC value, the accuracy, the true positive rate, and the true negative rate. This research indicates that the use of the classifier ensembles, rather than the search for the ideal individual classifiers, might help for the slope stability prediction.     

EXTRA-SEASONAL PREDICTIONS OF SUMMER RAINFALL IN CHINA AND ENSO IN 2001 BY CLIMATE MODELS

China is a monsoon country.The most rainfalls in China concentrate on the summer seasons.More frequent floods or droughts occur in some parts of China.Therefore,the prediction ofsummer rainfall in China is a significant issue.As we know,the obvious impacts of the sea surfacetemperature anomalies(SSTA)on the summer rainfall over China have been noticed.Thepredictions of the SSTA have been involved in the research.The key project on short-term climate modeling prediction system has been finished in 2000.The system included an atmospheric general circulation model named AGCM95,a coupledatmospheric-oceanic general circulation model named AOGCM95,a regional climate model overChina named RegCM95,a high-resolution Indian-Pacific OGCM named IPOGCM95,and asimplified atmosphere-ocean dynamic model system named SAOMS95.They became theoperational prediction models of National Climate Center(NCC).Extra-seasonal predictions in 2001 have been conducted by several climate models,which werethe AGCM95,AOGCM95,RegCM95,IPOGCM95,AIPOGCM95,OSU/NCC,SAOMS95,IAPAPOGCM and CAMS/ZS.All of those models predicted the summer precipitation over China and/or the annual SSTA over the tropical Pacific Ocean in the Modeling Prediction Workshop held inMarch 2001.The assessments have shown that the most models predicted the distributions of main rain beltover Huanan and parts of Jiangnan and droughts over Huabei-Hetao and Huaihe River Valleyreasonably.The most models predicted successfully that a weaker cold phase of the SSTA over thecentral and eastern tropical Pacific Ocean would continue in 2001.The evaluations of extra-seasonal predictions have also indicated that the models had a certaincapability of predicting the SSTA over the tropical Pacific Ocean and the summer rainfall overChina.The assessment also showed that multi-model ensemble(super ensembles)predictionsprovided the better forecasts for both SSTA and summer rainfall in 2001,compared with the singlemodel.It is a preliminary assessment for the extra-seasonal predictions by the climate models.Thefurther investigations will be carried out.The model system should be developed and improved.     

On the correlation between precipitation and potential evapotranspiration climate change signals for hydrological impact analyses

State-of-the-art hydrological climate impact assessment involves ensemble approaches to address uncertainties. For precipitation, a wide range of climate model runs is available. However, for particular meteorological variables used for the calculation of potential evapotranspiration (ET_o), availability of climate model runs is limited. It is preferred that climate model runs are considered coupled when calculating changes in precipitation and ET_o amounts, in order to preserve the internal physical consistency. This results in constraints on the maximum ensemble size. In this paper, we investigate the correlation between climate change signals of precipitation and ET_o. It is found that, for two medium-sized catchments in Belgium, uncoupling climate model runs used for calculation of change signals of precipitation and ET_o amounts does not result in a significant bias for changes in extreme flow. With these results, future impact studies can be conducted with larger ensemble sizes, resulting in a more complete uncertainty estimation.     

Modelling uncertainties in short-term reservoir operation using fuzzy sets and a genetic algorithm / Modélisation d’incertitudes dans la gestion de barrage à court terme grâce à des ensembles flous et à un algorithme génétique

Abstract

Abstract Various uncertainties are inherent in modelling any reservoir operation problem. Two of these are addressed in this study: uncertainty involved in the expression of reservoir penalty functions, and uncertainty in determining the target release value. Fuzzy set theory was used to model these uncertainties where the preferences of the decision maker for the fuzzified parameters are expressed as membership functions. Nonlinear penalty functions are used to determine the penalties due to deviations from targets. The optimization was performed using a genetic algorithm with the objectives to minimize the total penalty and to maximize the level of satisfaction of the decision maker with fuzzified input parameters. The proposed formulation was applied to the problem of finding the optimal release and storage values, taking Green reservoir in Kentucky, USA as a case study. The approach offers more flexibility to reservoir decision-making by demonstrating an efficient way to represent subjective uncertainties, and to deal with non-commensurate objectives under a fuzzy multi-objective environment.     

EXTRA-SEASONAL PREDICTIONS OF SUMMER RAINFALL IN CHINA AND ENSO IN 2001 BY CLIMATE MODELS*

China is a monsoon country.The most rainfalls in China concentrate on the summer seasons.More frequent floods or droughts occur in some parts of China.Therefore,the prediction of summer rainfall in China is a significant issue.As we know,the obvious impacts of the sea surface temperature anomalies(SSTA)on the summer rainfall over China have been noticed.The predictions of the SSTA have been involved in the research.The key project on short-term climate modeling prediction system has been finished in 2000.The system included an atmospheric general circulation model named AGCM95,a coupled atmospheric-oceanic general circulation model named AOGCM95,a regional climate model over China named RegCM95,a high-resolution Indian-Pacific OGCM named IPOGCM95,and a simplified atmosphere-ocean dynamic model system named SAOMS95.They became the operational prediction models of National Climate Center(NCC).Extra-seasonal predictions in 2001 have been conducted by several climate models,which were the AGCM95,AOGCM95,RegCM95,IPOGCM95,AIPOGCM95,OSU/NCC,SAOMS95,IAP APOGCM and CAMS/ZS.All of those models predicted the summer precipitation over China and/or the annual SSTA over the tropical Pacific Ocean in the Modeling Prediction Workshop held in March 2001.The assessments have shown that the most models predicted the distributions of main rain belt over Huanan and parts of Jiangnan and droughts over Huabei-Hetao and Huaihe River Valley reasonably.The most models predicted successfully that a weaker cold phase of the SSTA over the central and eastern tropical Pacific Ocean would continue in 2001.The evaluations of extra-seasonal predictions have also indicated that the models had a certain capability of predicting the SSTA over the tropical Pacific Ocean and the summer rainfall over China.The assessment also showed that multi-model ensemble(super ensembles)predictions provided the better forecasts for both SSTA and summer rainfall in 2001,compared with the single model.It is a preliminary assessment for the extra-seasonal predictions by the climate models.The further investigations will be carried out.The model system should be developed and improved.     

Assessing the impact of land use change on hydrology by ensemble modelling (LUCHEM) II: Ensemble combinations and predictions

This paper reports on a project to compare predictions from a range of catchment models applied to a mesoscale river basin in central Germany and to assess various ensemble predictions of catchment streamflow. The models encompass a large range in inherent complexity and input requirements. In approximate order of decreasing complexity, they are DHSVM, MIKE-SHE, TOPLATS, WASIM-ETH, SWAT, PRMS, SLURP, HBV, LASCAM and IHACRES. The models are calibrated twice using different sets of input data. The two predictions from each model are then combined by simple averaging to produce a single-model ensemble. The 10 resulting single-model ensembles are combined in various ways to produce multi-model ensemble predictions. Both the single-model ensembles and the multi-model ensembles are shown to give predictions that are generally superior to those of their respective constituent models, both during a 7-year calibration period and a 9-year validation period. This occurs despite a considerable disparity in performance of the individual models. Even the weakest of models is shown to contribute useful information to the ensembles they are part of. The best model combination methods are a trimmed mean (constructed using the central four or six predictions each day) and a weighted mean ensemble (with weights calculated from calibration performance) that places relatively large weights on the better performing models. Conditional ensembles, in which separate model weights are used in different system states (e.g. summer and winter, high and low flows) generally yield little improvement over the weighted mean ensemble. However a conditional ensemble that discriminates between rising and receding flows shows moderate improvement. An analysis of ensemble predictions shows that the best ensembles are not necessarily those containing the best individual models. Conversely, it appears that some models that predict well individually do not necessarily combine well with other models in multi-model ensembles. The reasons behind these observations may relate to the effects of the weighting schemes, non-stationarity of the climate series and possible cross-correlations between models.     

Climate impacts on river flow: projections for the Medway catchment,UK, with UKCP09 and CATCHMOD

The potential impact of climate change on areas of strategic importance for water resources remains a concern. Here, river flow projections for the River Medway, above Teston in southeast England are presented, which is just such an area of strategic importance. The river flow projections use climate inputs from the Hadley Centre Regional Climate Model (HadRM3) for the time period 1960–2080 (a subset of the early release UKCP09 projections). River flow predictions are calculated using CATCHMOD, the main river flow prediction tool of the Environment Agency (EA) of England and Wales. In order to use this tool in the best way for climate change predictions, model setup and performance are analysed using sensitivity and uncertainty analysis. The model's representation of hydrological processes is discussed and the direct percolation and first linear storage constant parameters are found to strongly affect model results in a complex way, with the former more important for low flows and the latter for high flows. The uncertainty in predictions resulting from the hydrological model parameters is demonstrated and the projections of river flow under future climate are analysed. A clear climate change impact signal is evident in the results with a persistent lowering of mean daily river flows for all months and for all projection time slices. Results indicate that a projection of lower flows under future climate is valid even taking into account the uncertainties considered in this modelling chain exercise. The model parameter uncertainty becomes more significant under future climate as the river flows become lower. This has significant implications for those making policy decisions based on such modelling results. Copyright © 2010 John Wiley & Sons, Ltd.