关键词： crop identification   pre-constrained classification method   multi-year mapping   crop phenology   support vector machine   random forest  

摘要： The accurate mapping of crops can provide effective information for regional agricultural management, which is helpful to improve crop production efficiency. Recently, remote sensing data offers a comprehensive approach to achieve crop identification on a regional scale. However, the classification methods for multi-year mapping needs further study in regions with a complex planting structure, due to the mixed pixels at a spatial distribution and the high error in different years at a temporal scale. The objective of this study is to map the multi-year spatial distribution of three main crops (maize, sunflower, and wheat) in the Hetao irrigation district of China for the period 2012-2016 based on a pre-constrained classification method. The pre-constrained method integrates a parameterized phenology-based vegetation indexes classifier and two non-parametric machine learning algorithms-support vector machine (SVM) and random forest (RF). Results indicated that the performance of the pre-constrained classification method was excellent in the multi-year mapping of major crops in the study area, with absolute relative errors mainly less than 14% in the whole irrigation district and less than 20% in the five counties. The corresponding overall accuracy was 87.9%, and the Kappa coefficient was 0.80. Mapping results showed that maize is mainly distributed in Hangjinhouqi, southern Linhe, northern Wuyuan, and eastern Wulateqianqi, while wheat is relatively less and scatteredly distributed in Hangjinhouqi and Wuyuan. Moreover, the sunflower planting area increased significantly and expanded spatially from Wuyuan and western Wulateqianqi to northern Hangjinhouqi and Linhe from 2012 to 2016. In addition, the phenology-based vegetation indexes classifier was found to be effective in improving the classification accuracy based on the contribution analysis.

关键词： multilayer soil moisture mapping   RF method   remote sensing   ground monitoring  

摘要： Soil moisture mapping at a regional scale is commonplace since these data are required in many applications, such as hydrological and agricultural analyses. The use of remotely sensed data for the estimation of deep soil moisture at a regional scale has received far less emphasis. The objective of this study was to map the 500-m, 8-day average and daily soil moisture at different soil depths in Oklahoma from remotely sensed and ground-measured data using the random forest (RF) method, which is one of the machine-learning approaches. In order to investigate the estimation accuracy of the RF method at both a spatial and a temporal scale, two independent soil moisture estimation experiments were conducted using data from 2010 to 2014: a year-to-year experiment (with a root mean square error (RMSE) ranging from 0.038 to 0.050 m(3)/m(3)) and a station-to-station experiment (with an RMSE ranging from 0.044 to 0.057 m(3)/m(3)). Then, the data requirements, importance factors, and spatial and temporal variations in estimation accuracy were discussed based on the results using the training data selected by iterated random sampling. The highly accurate estimations of both the surface and the deep soil moisture for the study area reveal the potential of RF methods when mapping soil moisture at a regional scale, especially when considering the high heterogeneity of land-cover types and topography in the study area.

关键词： 机器学习方法   滑动   断层   预测   科学家   宾夕法尼亚州立大学   洛斯阿拉莫斯   地震信号  

摘要： 2018 年12 月,Nature Geoscience 发表了两篇涉及机器学习与地震研究的论文。这两篇论文的主要研究者来自美国洛斯阿拉莫斯国家实验室(Los Alamos National Laboratory),同时还有来自宾夕法尼亚州立大学(Pennsylvania State University)的合作者。研究人员利用机器学习方法检测地震信号,准确地预测了卡斯卡迪亚(Cascadia)断层的缓慢滑动?这种破裂在其他俯冲带发生大地震之前曾观测到。

关键词： 神经网络   机器学习   人工智能  

摘要： 随着科学技术的不断发展,以及智能化新的需要,人工智能技术逐渐走进人们的视野。而随着神经网络技术的不断发展,机器学习也有新的理论出现。本文通过分析神经网络技术,研究生物神经网络在机器学习方法中的发展现状及趋势,讨论神经网络机器学习的技术与应用。

关键词： 机器学习   宏观审慎   多层次资本市场  

摘要： 相较于国际社会当前对于"宏观审慎"工具最新发展要求,我国对于宏观审慎监管的实践仍有需要完善的部分。在本项研究中,我们试图完善宏观审慎预警体系并建立多层次资本市场定价行为的评估机制,引入模糊数理论量化场外资本市场报价行为,通过区间支持向量回归模型(F-SVR)建立起一套对于多层次资本市场的报价行为评估体系,进一步丰富宏观审慎监管工具箱。

关键词： 娱乐新闻   朴素贝叶斯   逻辑回归   集成学习   lightGBM   异质集成学习   分类  

摘要： 娱乐新闻是腾讯新闻重要组成部分,根据娱乐文章的文本信息将文章归类是娱乐新闻处理工作中很重要的一部分。人工智能中的机器学习方法在经济生活中有着十分广泛的应用。根据娱乐新闻分类这一问题实例,分别采用了传统的机器学习方法,包括朴素贝叶斯,逻辑回归,基于集成学习的方法lightGBM,对这一分类问题进行探索。最后采用异质集成学习方法对最终结果进行集成。不同的模型在不同的问题中具有不同的效果,本文依据娱乐新闻分类这一具体问题,采用了上述几种算法模型进行预测,综合了不同模型的预测情况得出最后的预测结果。对算法模型进行了原理分析,比较了不同的分类模型解决该问题的效果,横向对比了不同模型的优缺点,并对造成结果差异的原因进行了总结。

关键词： No free lunch theorem   Random forests   River discharge   Stochastic hydrology   Support vector machines   Time series  

摘要： Research within the field of hydrology often focuses on the statistical problem of comparing stochastic to machine learning (ML) forecasting methods. The performed comparisons are based on case studies, while a study providing large-scale results on the subject is missing. Herein, we compare 11 stochastic and 9 ML methods regarding their multi-step ahead forecasting properties by conducting 12 extensive computational experiments based on simulations. Each of these experiments uses 2000 time series generated by linear stationary stochastic processes. We conduct each simulation experiment twice;the first time using time series of 100 values and the second time using time series of 300 values. Additionally, we conduct a real-world experiment using 405 mean annual river discharge time series of 100 values. We quantify the forecasting performance of the methods using 18 metrics. The results indicate that stochastic and ML methods may produce equally useful forecasts.

摘要： Background Existing universal and procedure-specific surgical risk prediction models of death and major complications after elective total joint arthroplasty (TJA) have limitations including poor transparency, poor to modest accuracy, and insufficient validation to establish performance across diverse settings. Thus, the need remains for accurate and validated prediction models for use in pre-operative management, informed consent, shared decision-making, and risk adjustment for reimbursement. Questions/purposes The purpose of this study was to use machine learning methods and large national databases to develop and validate (both internally and externally) parsimonious risk-prediction models for mortality and complications after TJA. Methods Preoperative demographic and clinical variables from all 107,792 nonemergent primary THAs and TKAs in the 2013 to 2014 American College of Surgeons-National Surgical Quality Improvement Program (ACS-NSQIP) were evaluated as predictors of 30-day death and major complications. The NSQIP database was chosen for its high-quality data on important outcomes and rich characterization of preoperative demographic and clinical predictors for demographically and geographically diverse patients. Least absolute shrinkage and selection operator (LASSO) regression, a type of machine learning that optimizes accuracy and parsimony, was used for model development. Tenfold validation was used to produce C-statistics, a measure of how well models discriminate patients who experience an outcome from those who do not. External validation, which evaluates the generalizability of the models to new data sources and patient groups, was accomplished using data from the Veterans Affairs Surgical Quality Improvement Program (VASQIP). Models previously developed from VASQIP data were also externally validated using NSQIP data to examine the generalizability of their performance with a different group of patients outside the VASQIP context. Results The model

关键词： Fine particulate matter   Satellite remote sensing   Aerosol optical depth   Machine learning  

摘要： Ambient exposure to fine particulate matter (PM2.5) is known to harm public health in China. Satellite remote sensing measurements of aerosol optical depth (AOD) were statistically associated with in-situ observations after 2013 to predict PM2.5 concentrations nationwide, while the lack of surface monitoring data before 2013 have created difficulties in historical PM2.5 exposure estimates. Hindcast approaches using statistical models or chemical transport models (CTMs) were developed to overcome this limitation, while those approaches still suffer from incomplete daily coverage due to missing AOD data or limited accuracy due to uncertainties of CTMs. Here we developed a new machine learning (ML) model with high-dimensional expansion (HD-expansion) of numerous predictors (including AOD and other satellite covariates, meteorological variables and CTM simulations). Through comprehensive characterization of the nonlinear effects of, and interactions among different predictors, the HD-expansion parameterized the association between PM2.5 and AOD as a nonlinear function of space and time covariates (e.g., planetary boundary layer height and relative humidity). In this way, the PM2.5-AOD association can vary spatiotemporally. We trained the model with data from 2013 to 2016 and evaluated its performance using annually-iterated cross-validation, which iteratively held out the in-situ observations for a whole calendar year (as testing data) to examine the predictions from a model trained by the rest of the observations. Our estimates were found to be in good agreement with in-situ observations, with correlation coefficients (R-2) of 0.61, 0.68, and 0.75 for daily, monthly and annual averages, respectively. To interpolate the missing predictions due to incomplete AOD data, we incorporated a generalized additive model into the ML model. The two-stage estimates of PM2.5 sacrificed the prediction accuracy on a daily timescale (R-2 = 0.55), but achieved complete spatiotemporal co

关键词： Thermal comfort prediction   Feedback technology   Thermographic camera   HVAC control   Building automation  

摘要： This work introduces a new technique that provides real-time feedback to a Heating, Ventilation, and Air Conditioning (HVAC) system controller with respect to the occupants' thermal preferences to avoid space overheating. We propose a non-invasive approach for automatic prediction of personal thermal comfort and mean time to warm discomfort using machine learning. The prediction framework described uses temperature information extracted from multiple local body parts to model an individual's thermal preference, with sensing measurements that capture local body part variance as well as differences between body parts. We compared the efficacy of using machine learning with classical measurements such as skin temperature along with our approach of using multi-part measurements and derived data. An analysis of the performance of machine learning shows that our method improved the accuracy of personal thermal comfort prediction by an average of 60%, and the accuracy of mean time to warm discomfort prediction by an average of 40%. The proposed thermal models were tested on subjects' data extracted from an office setup with room temperature varying from low (21.11 degrees C) to high (27.78 degrees C). When all proposed features were used, personal thermal comfort was predicted with an accuracy higher than 80% and mean time to warm discomfort with more than 85% accuracy. Further analysis of the machine learning efficacy showed that temperature differences had the highest impact on performance of individual thermal preference prediction, while the proposed approach was found not sensitive to the actual machine learning algorithm.