摘要： This paper investigates the possibility of predicting the time series of the geomagnetic index Dst. The prediction is based on parameters of the solar wind and interplanetary magnetic field measured at Lagrange point L1 within the Advanced Composition Explorer (ACE) spacecraft experiment using machine learning methodsartificial neural networks: classical perceptrons, recurrent networks of long short-term memory (LSTM), and committees of predictive models. Ultimately, the best results have been obtained using heterogeneous committees based on neural networks of both types.

关键词： Heterogeneity brain aging   Functional connectivity   Structural MRI   Functional connectivity   Resting-state fMRI  

摘要： Disentangling the heterogeneity of brain aging in cognitively normal older adults is challenging, as multiple co-occurring pathologic processes result in diverse functional and structural changes. Capitalizing on machine learning methods applied to magnetic resonance imaging data from 400 participants aged 50 to 96 years in the Baltimore Longitudinal Study of Aging, we constructed normative cross-sectional brain aging trajectories of structural and functional changes. Deviations from typical trajectories identified individuals with resilient brain aging and multiple subtypes of advanced brain aging. We identified 5 distinct phenotypes of advanced brain aging. One group included individuals with relatively extensive structural and functional loss and high white matter hyperintensity burden. Another subgroup showed focal hippocampal atrophy and lower posterior-cingulate functional coherence, low white matter hyperintensity burden, and higher medial-temporal connectivity, potentially reflecting high brain tissue reserve counterbalancing brain loss that is consistent with early stages of Alzheimer's disease. Other subgroups displayed distinct patterns. These results indicate that brain changes should not be measured seeking a single signature of brain aging but rather via methods capturing heterogeneity and subtypes of brain aging. Our findings inform future studies aiming to better understand the neurobiological underpinnings of brain aging imaging patterns. (C) 2018 Elsevier Inc. All rights reserved.

关键词： agriculture   machine learning   climate change impacts  

摘要： Crop yields are critically dependent on weather. A growing empirical literature models this relationship in order to project climate change impacts on the sector. We describe an approach to yield modeling that uses a semiparametric variant of a deep neural network, which can simultaneously account for complex nonlinear relationships in high-dimensional datasets, as well as known parametric structure and unobserved cross-sectional heterogeneity. Using data on corn yield from the US Midwest, we show that this approach outperforms both classical statistical methods and fully-nonparametric neural networks in predicting yields of years withheld during model training. Using scenarios from a suite of climate models, we show large negative impacts of climate change on corn yield, but less severe than impacts projected using classical statistical methods. In particular, our approach is less pessimistic in the warmest regions and the warmest scenarios.

关键词： Multiple stressors   Cumulative impacts   Regression   Spatial prediction   Model selection   Kelp forests  

摘要： Coastal ecosystems are exposed to multiple anthropogenic stressors such as fishing, pollution, and climate change. Ecosystem-based coastal management requires understanding where the combination of multiple stressors has large cumulative effects and where actions to address impacts are most urgently needed. However, the effects of multiple stressors on coastal and marine ecosystems are often non-linear and interactive. This complexity is not captured by commonly used spatial models for mapping human impacts. Flexible statistical and machine learning models like random forests have thus been used as an alternative modeling approach to identify important stressors and to make spatial predictions of their combined effects. However, tests of such models' prediction skill have been limited. Therefore, we tested how well ten statistical and machine learning methods predicted three ecological indicators of coastal marine ecosystem condition (kelp biodiversity, fish biomass, and rocky intertidal biodiversity) off California, USA. Spatial data representing anthropogenic stressors and ocean uses as well as natural gradients were used as predictors. The models' prediction errors were estimated by double spatial block cross-validation. The best models achieved mean squared errors about 25% lower than a null model for kelp biodiversity and fish biomass;none of the tested models worked well for rocky intertidal biodiversity. The models captured general trends, but not local variability of the indicators. For kelp biodiversity, the best performing method was principal components regression. For fish biomass, the best performing method was boosted regression trees. However, after tuning, this model did not include any interactions between stressors, and ridge regression (a constrained linear model) performed almost as well. While in theory flexible machine learning methods are required to represent the complex stressor-ecosystem state relationships revealed by experimental ecologis

摘要： Determination of in-season N requirement for corn (Zea mays L.) is challenging due to interactions of genotype, environment, and management. Machine learning (ML), with its predictive power to tackle complex systems, may solve this barrier in the development of locally based N recommendations. The objective of this study was to explore application of ML methodologies to predict economic optimum nitrogen rate (EONR) for corn using data from 47 experiments across the US Corn Belt. Two features, a water table adjusted available water capacity (AWC(wt)) and a ratio of in-season rainfall to AWC(wt) (RAWC(wt)), were created to capture the impact of soil hydrology on N dynamics. Four ML models-linear regression (LR), ridge regression (RR), least absolute shrinkage and selection operator (LASSO) regression, and gradient boost regression trees (GBRT)-were assessed and validated using "leave-one-location-out" (LOLO) and "leave-one-year-out" (LOYO) approaches. Generally, RR outperformed other models in predicting both at planting and split EONR times. Among the 47 tested sites, for 33 sites the predicted split EONR using RR fell within the 95% confidence interval, suggesting the chance of using the RR model to make an acceptable prediction of split EONR is similar to 70%. When RR was used to test split EONR prediction with input weather features surrogated with 10 yr of historical weather data, the model demonstrated robustness (MAE, 33.6 kg ha(-1);R-2 = 0.46). Incorporating mechanistically derived hydrological features significantly enhanced the ability of the ML procedures to model EONR. Improvement in estimating in-season soil hydrological status seems essential for success in modeling N demand.

关键词： Toxicity prediction   machine learning   structure-activity relationship   prediction accuracy   model reliability   molecular descriptors  

摘要： In silico toxicity prediction plays an important role in the regulatory decision making and selection of leads in drug design as in vitro/vivo methods are often limited by ethics, time, budget, and other resources. Many computational methods have been employed in predicting the toxicity profile of chemicals. This review provides a detailed end-to-end overview of the application of machine learning algorithms to Structure-Activity Relationship (SAR)-based predictive toxicology. From raw data to model validation, the importance of data quality is stressed as it greatly affects the predictive power of derived models. Commonly overlooked challenges such as data imbalance, activity cliff, model evaluation, and definition of applicability domain are highlighted, and plausible solutions for alleviating these challenges are discussed.

关键词： 机器学习方法   随机森林算法   适宜性评价   农村居民点   分类和预测   房县  

摘要： 农村居民点适宜性评价是优化农村土地利用布局的基础性工作之一,解决该研究领域存在的问题,提高评价结果的科学合理性,使之更好地服务于农村土地资源的转型、优化利用,事关乡村振兴战略的实施。本文引入机器学习方法,通过对小规模已知样本的学习实现大规模未知数据的高精度分类与评价,从方法可行性、数模结合、优化策略和预测实现等方面进行探讨,应用随机森林算法对湖北省房县农村居民点利用的适宜性问题展开实证研究。结果显示:(1)房县农村居民点适宜性受其可通达能力、海拔高度、地块面积及当地农业生产活跃程度的影响较大,也与农民生活、收入水平紧密相关;(2)训练模型的性能受特征(评价因子)规模变化的影响较小,其中,16维特征集合模型的测试精度可达83.54%;(3)不同训练模型的预测结果均反映出房县的非适宜型居民点规模较大,该算法的鲁棒性强,评价结果稳定可靠。研究表明,机器学习方法能较好地支撑农村居民点适宜性评价工作,评价结果能为优化农村土地利用和基层治理提供数据基础。

关键词： 高斯过程回归   水温预测   气温   密苏里河   机器学习方法  

摘要： 通过收集密苏里河三个站点的多年实测水温、气温数据,建立了水温-气温的高斯过程回归模型.和传统的水温回归模型（线性、非线性及随机回归模型）相比,高斯过程回归模型的精度较高,各研究站点的相关系数值均较高（0.966 4-0.989 7）,均方根误差值相对较小（1.978 4-1.495 0）.高斯过程回归作为一种较为先进的机器学习方法,和其他机器学习方法相比,其突出的优点在于结合了许多机器学习任务,包括模型训练、不确定性分析及超参数估计等,该方法可以有效地应用于天然河流站点水温的预测.

关键词： 机器学习   决策树   LightGBM   气温   预报试验  

摘要： 利用2015—2017年湖北89个气象站地面观测温度、欧洲中心再分析资料和0～12 h预报资料回归模式输出要素与地面气温之间的关系,建立了LightGBM模型,并在2018年数据集上进行测试。结果表明,定时气温平均绝对误差由模式本身的1.8℃下降到1.1℃,2℃以内预报准确率由65.9%上升至86.6%,决定系数(拟合优度)高达0.97。该模型已经在武汉中心气象台业务化,初步选取定时气温中的极值进行2018年2—6月预报评分,24 h高、低温预报准确率分别为76.9%和91.4%,在客观产品中排名前列,较数值预报模式产品提升明显,低温预报准确率超过预报员水平。LightGBM作为一个年轻的机器学习框架,在气象要素预报方面具备良好的应用前景。

关键词： 质子转移反应质谱(PTR-MS)   挥发性有机物(VOCs)   数据预处理   机器学习  

摘要： 质子转移反应质谱(PTR-MS)法是一种用于检测挥发性有机物(VOCs)的分析技术。它具有检测限低、响应速度快、无需样品前处理、实时分析等特点,在大气化学、环境化学、食品、生物医学等领域得到广泛应用。随着PTR-MS应用的扩展和样品种类的增加,如何从复杂的质谱数据中提取特征,并寻找内在规律,对分析算法的处理能力提出了更高的要求。本工作从数据预处理技术和机器学习方法两方面展开论述,归纳了具有PTR-MS特点的数据预处理技术,总结了不同机器学习算法在PTR-MS数据分析中的应用,并讨论了它们的优点和不足。