针对电力行业碳排放量预测问题,在传统人工蜂群算法中引入遗传学习策略进行改进,提出一种基于进化人工蜂群算法优化的随机森林回归预测模型。首先在可拓展随机性环境影响评估模型(stochastic imacts by regression on population afflue...针对电力行业碳排放量预测问题,在传统人工蜂群算法中引入遗传学习策略进行改进,提出一种基于进化人工蜂群算法优化的随机森林回归预测模型。首先在可拓展随机性环境影响评估模型(stochastic imacts by regression on population affluence and technlogy,STIRPAT)模型基础上确定电力行业碳排放量影响因素,将其作为预测模型的输入自变量,继而利用进化人工蜂群算法优化随机森林回归模型,从而避免模型参数主观设置不合理对预测精度的不利影响,最后运用参数优化后的模型对电力行业碳排放量进行预测。实际测算数据验证结果表明,该模型可以准确反映电力行业未来碳排放趋势,并且与其他预测模型相比,预测精度更高、优势更加明显,能够为节能减排政策制定提供参考借鉴。展开更多
Model accuracy and runtime are two key issues for flood warnings in rivers.Traditional hydrodynamic models,which have a rigorous physical mechanism for flood routine,have been widely adopted for water level prediction...Model accuracy and runtime are two key issues for flood warnings in rivers.Traditional hydrodynamic models,which have a rigorous physical mechanism for flood routine,have been widely adopted for water level prediction in river,lake,and urban areas.However,these models require various types of data,in-depth domain knowledge,experience with modeling,and intensive computational time,which hinders short-term or real-time prediction.In this paper,we propose a new framework based on machine learning methods to alleviate the aforementioned limitation.We develop a wide range of machine learning models such as linear regression(LR),support vector regression(SVR),random forest regression(RFR),multilayer perceptron regression(MLPR),and light gradient boosting machine regression(LGBMR)to predict the hourly water level at Le Thuy and Kien Giang stations of the Kien Giang river based on collected data of 2010,2012,and 2020.Four evaluation metrics,that is,R^(2),Nash-Sutcliffe efficiency,mean absolute error,and root mean square error,are employed to examine the reliability of the proposed models.The results show that the LR model outperforms the SVR,RFR,MLPR,and LGBMR models.展开更多
目的:建立注射用苦碟子HPLC数字化指纹图谱,为注射用苦碟子质量控制提供依据。方法:采用反相HPLC法分析注射用苦碟子水溶液,以Century SIL BDS柱(250mm×4.6mm,5μm),流动相为1%醋酸水-1%醋酸乙腈低压梯度洗脱,检测波长265nm,柱温(3...目的:建立注射用苦碟子HPLC数字化指纹图谱,为注射用苦碟子质量控制提供依据。方法:采用反相HPLC法分析注射用苦碟子水溶液,以Century SIL BDS柱(250mm×4.6mm,5μm),流动相为1%醋酸水-1%醋酸乙腈低压梯度洗脱,检测波长265nm,柱温(30±0.15)℃,进样量20μL。用"中药色谱指纹图谱超信息特征数字化评价系统3.0"软件计算不同批次的注射用苦碟子HPLC指纹图谱的色谱指纹图谱指数F和色谱指纹图分离量指数RF等42个参数进行潜信息特征数字化评价。以双参照物体系结合定量结构-性质相关(QSPR)原理标定指纹峰的表观分子量、峰位、洗脱动量数δ、折合相对积分φ。结果:以咖啡酸峰为参照物峰,确定21个共有峰,建立了注射用苦碟子HPLC数字化指纹图谱,获得了判别注射用苦碟子质量的重要数字化信息。以双定性双定量相似度法评价注射用苦碟子批间质量稳定。结论:所建立HPLC数字化指纹图谱具有较好的精密度和重现性,适用于注射用苦碟子的质量控制。数字化指纹图谱是数字中药的核心技术,双定性双定量相似度法是宏观定性定量评价中药质量的最准确和最佳技术。展开更多
文摘针对电力行业碳排放量预测问题,在传统人工蜂群算法中引入遗传学习策略进行改进,提出一种基于进化人工蜂群算法优化的随机森林回归预测模型。首先在可拓展随机性环境影响评估模型(stochastic imacts by regression on population affluence and technlogy,STIRPAT)模型基础上确定电力行业碳排放量影响因素,将其作为预测模型的输入自变量,继而利用进化人工蜂群算法优化随机森林回归模型,从而避免模型参数主观设置不合理对预测精度的不利影响,最后运用参数优化后的模型对电力行业碳排放量进行预测。实际测算数据验证结果表明,该模型可以准确反映电力行业未来碳排放趋势,并且与其他预测模型相比,预测精度更高、优势更加明显,能够为节能减排政策制定提供参考借鉴。
基金Scientific Research and Technology Development Project。
文摘Model accuracy and runtime are two key issues for flood warnings in rivers.Traditional hydrodynamic models,which have a rigorous physical mechanism for flood routine,have been widely adopted for water level prediction in river,lake,and urban areas.However,these models require various types of data,in-depth domain knowledge,experience with modeling,and intensive computational time,which hinders short-term or real-time prediction.In this paper,we propose a new framework based on machine learning methods to alleviate the aforementioned limitation.We develop a wide range of machine learning models such as linear regression(LR),support vector regression(SVR),random forest regression(RFR),multilayer perceptron regression(MLPR),and light gradient boosting machine regression(LGBMR)to predict the hourly water level at Le Thuy and Kien Giang stations of the Kien Giang river based on collected data of 2010,2012,and 2020.Four evaluation metrics,that is,R^(2),Nash-Sutcliffe efficiency,mean absolute error,and root mean square error,are employed to examine the reliability of the proposed models.The results show that the LR model outperforms the SVR,RFR,MLPR,and LGBMR models.
文摘目的:建立注射用苦碟子HPLC数字化指纹图谱,为注射用苦碟子质量控制提供依据。方法:采用反相HPLC法分析注射用苦碟子水溶液,以Century SIL BDS柱(250mm×4.6mm,5μm),流动相为1%醋酸水-1%醋酸乙腈低压梯度洗脱,检测波长265nm,柱温(30±0.15)℃,进样量20μL。用"中药色谱指纹图谱超信息特征数字化评价系统3.0"软件计算不同批次的注射用苦碟子HPLC指纹图谱的色谱指纹图谱指数F和色谱指纹图分离量指数RF等42个参数进行潜信息特征数字化评价。以双参照物体系结合定量结构-性质相关(QSPR)原理标定指纹峰的表观分子量、峰位、洗脱动量数δ、折合相对积分φ。结果:以咖啡酸峰为参照物峰,确定21个共有峰,建立了注射用苦碟子HPLC数字化指纹图谱,获得了判别注射用苦碟子质量的重要数字化信息。以双定性双定量相似度法评价注射用苦碟子批间质量稳定。结论:所建立HPLC数字化指纹图谱具有较好的精密度和重现性,适用于注射用苦碟子的质量控制。数字化指纹图谱是数字中药的核心技术,双定性双定量相似度法是宏观定性定量评价中药质量的最准确和最佳技术。
