This study aims to reveal the impacts of three important uncertainty issues in landslide susceptibility prediction(LSP),namely the spatial resolution,proportion of model training and testing datasets and selection of ...This study aims to reveal the impacts of three important uncertainty issues in landslide susceptibility prediction(LSP),namely the spatial resolution,proportion of model training and testing datasets and selection of machine learning models.Taking Yanchang County of China as example,the landslide inventory and 12 important conditioning factors were acquired.The frequency ratios of each conditioning factor were calculated under five spatial resolutions(15,30,60,90 and 120 m).Landslide and non-landslide samples obtained under each spatial resolution were further divided into five proportions of training and testing datasets(9:1,8:2,7:3,6:4 and 5:5),and four typical machine learning models were applied for LSP modelling.The results demonstrated that different spatial resolution and training and testing dataset proportions induce basically similar influences on the modeling uncertainty.With a decrease in the spatial resolution from 15 m to 120 m and a change in the proportions of the training and testing datasets from 9:1 to 5:5,the modelling accuracy gradually decreased,while the mean values of predicted landslide susceptibility indexes increased and their standard deviations decreased.The sensitivities of the three uncertainty issues to LSP modeling were,in order,the spatial resolution,the choice of machine learning model and the proportions of training/testing datasets.展开更多
How to deal with uncertainty is crucial in exact concept mapping between ontologies. This paper presents a new framework on modeling uncertainty in ontologies based on bayesian networks （BN）. In our approach, ontolo...How to deal with uncertainty is crucial in exact concept mapping between ontologies. This paper presents a new framework on modeling uncertainty in ontologies based on bayesian networks （BN）. In our approach, ontology Web language （OWL） is extended to add probabilistie markups for attaching probability information, the source and target ontol ogies （expressed by patulous OWL） are translated into hayesian networks （BNs）, the mapping between the two ontologies can be digged out by constructing the conditional probability tables （CPTs） of the BN using a improved algorithm named I-IPFP based on iterative proportional fitting procedure （IPFP）. The basic idea of this framework and algorithm are validated by positive results from computer experiments.展开更多
新能源的随机性、波动性及弱调节特性给电力系统静态电压的安全及稳定性带来了挑战。针对此问题,提出一种考虑源荷双侧不确定性的高比例新能源电力系统静态电压稳定裕度在线概率评估方法。首先,基于新能源无功调节特性与传统机组的差异...新能源的随机性、波动性及弱调节特性给电力系统静态电压的安全及稳定性带来了挑战。针对此问题,提出一种考虑源荷双侧不确定性的高比例新能源电力系统静态电压稳定裕度在线概率评估方法。首先,基于新能源无功调节特性与传统机组的差异,分析了大量新能源替代传统机组对稳定裕度的影响。然后,分析了新能源出力不确定性对稳定裕度分布范围的影响,并建立源荷不确定性模型以生成典型场景。最后,为了应对新能源快速波动性给稳定裕度带来的影响,提出基于优化ELM-KDE的稳定裕度在线概率评估方法。利用优化极限学习机(extreme learning machine,ELM)预测典型场景稳定裕度并通过核密度估计(kernel density estimation,KDE)准确获得其概率分布函数。构建了静态电压稳定期望裕度和静态电压稳定风险度两个指标对结果进行表征。分别在New England 39和IEEE300节点系统进行了仿真测试,并将结果与传统蒙特卡洛方法计算结果对比,验证了所提方法的有效性。展开更多
高比例风电接入系统趋势下,考虑将规模化新能源转化为氢气等清洁气体,配合燃气设备发电,以解决可再生能源消纳和减少碳排放问题。首先介绍了电-气-氢能源互联系统能流结构框架和关键设备运行模型;其次,计及源、荷的不确定性,构建含燃气...高比例风电接入系统趋势下,考虑将规模化新能源转化为氢气等清洁气体,配合燃气设备发电,以解决可再生能源消纳和减少碳排放问题。首先介绍了电-气-氢能源互联系统能流结构框架和关键设备运行模型;其次,计及源、荷的不确定性,构建含燃气机组、储氢装置、燃料电池以及电转气(Power To Gas,P2G)设备的优化规划模型,通过二阶锥松弛和分段线性法将其转化为混合整数线性规划模型进行求解;最后采用IEEE-9节点电力网络和7节点天然气网络联合系统进行算例验证。结果表明,储氢装置及燃料电池的应用,降低规划总成本的同时能够提升风电消纳和减少碳排放,且考虑不确定性的规划方案能够提高系统灵活性,保证系统可靠运行。展开更多
基金This research is funded by the National Natural Science Foundation of China(41807285,41762020,51879127 and 51769014E)Natural Science Foundation of Hebei Province(D2022202005).
文摘This study aims to reveal the impacts of three important uncertainty issues in landslide susceptibility prediction(LSP),namely the spatial resolution,proportion of model training and testing datasets and selection of machine learning models.Taking Yanchang County of China as example,the landslide inventory and 12 important conditioning factors were acquired.The frequency ratios of each conditioning factor were calculated under five spatial resolutions(15,30,60,90 and 120 m).Landslide and non-landslide samples obtained under each spatial resolution were further divided into five proportions of training and testing datasets(9:1,8:2,7:3,6:4 and 5:5),and four typical machine learning models were applied for LSP modelling.The results demonstrated that different spatial resolution and training and testing dataset proportions induce basically similar influences on the modeling uncertainty.With a decrease in the spatial resolution from 15 m to 120 m and a change in the proportions of the training and testing datasets from 9:1 to 5:5,the modelling accuracy gradually decreased,while the mean values of predicted landslide susceptibility indexes increased and their standard deviations decreased.The sensitivities of the three uncertainty issues to LSP modeling were,in order,the spatial resolution,the choice of machine learning model and the proportions of training/testing datasets.
基金Supported by the National Natural Science Foun-dation of China (60403027)
文摘How to deal with uncertainty is crucial in exact concept mapping between ontologies. This paper presents a new framework on modeling uncertainty in ontologies based on bayesian networks （BN）. In our approach, ontology Web language （OWL） is extended to add probabilistie markups for attaching probability information, the source and target ontol ogies （expressed by patulous OWL） are translated into hayesian networks （BNs）, the mapping between the two ontologies can be digged out by constructing the conditional probability tables （CPTs） of the BN using a improved algorithm named I-IPFP based on iterative proportional fitting procedure （IPFP）. The basic idea of this framework and algorithm are validated by positive results from computer experiments.
文摘新能源的随机性、波动性及弱调节特性给电力系统静态电压的安全及稳定性带来了挑战。针对此问题,提出一种考虑源荷双侧不确定性的高比例新能源电力系统静态电压稳定裕度在线概率评估方法。首先,基于新能源无功调节特性与传统机组的差异,分析了大量新能源替代传统机组对稳定裕度的影响。然后,分析了新能源出力不确定性对稳定裕度分布范围的影响,并建立源荷不确定性模型以生成典型场景。最后,为了应对新能源快速波动性给稳定裕度带来的影响,提出基于优化ELM-KDE的稳定裕度在线概率评估方法。利用优化极限学习机(extreme learning machine,ELM)预测典型场景稳定裕度并通过核密度估计(kernel density estimation,KDE)准确获得其概率分布函数。构建了静态电压稳定期望裕度和静态电压稳定风险度两个指标对结果进行表征。分别在New England 39和IEEE300节点系统进行了仿真测试,并将结果与传统蒙特卡洛方法计算结果对比,验证了所提方法的有效性。
文摘高比例风电接入系统趋势下,考虑将规模化新能源转化为氢气等清洁气体,配合燃气设备发电,以解决可再生能源消纳和减少碳排放问题。首先介绍了电-气-氢能源互联系统能流结构框架和关键设备运行模型;其次,计及源、荷的不确定性,构建含燃气机组、储氢装置、燃料电池以及电转气(Power To Gas,P2G)设备的优化规划模型,通过二阶锥松弛和分段线性法将其转化为混合整数线性规划模型进行求解;最后采用IEEE-9节点电力网络和7节点天然气网络联合系统进行算例验证。结果表明,储氢装置及燃料电池的应用,降低规划总成本的同时能够提升风电消纳和减少碳排放,且考虑不确定性的规划方案能够提高系统灵活性,保证系统可靠运行。
