A comparison of predictive methods in modelling the distribution of periglacial landforms in Finnish Lapland

This study compares the predictive accuracy of eight state‐of‐the‐art modelling techniques for 12 landforms types in a cold environment. The methods used are Random Forest (RF), Artificial Neural Networks (ANN), Generalized Boosting Methods (GBM), Generalized Linear Models (GLM), Generalized Additive Models (GAM), Multivariate Adaptive Regression Splines (MARS), Classification Tree Analysis (CTA) and Mixture Discriminant Analysis (MDA). The spatial distributions of 12 periglacial landforms types were recorded in sub‐Arctic landscape of northern Finland in 2032 grid squares at a resolution of 25 ha. First, three topographic variables were implemented into the eight modelling techniques (simple model), and then six other variables were added (three soil and three vegetation variables; complex model) to reflect the environmental conditions of each grid square. The predictive accuracy was measured by two methods: the area under the curve (AUC) of a receiver operating characteristic (ROC) plot, and the Kappa index (κ), based on spatially independent model evaluation data. The mean AUC values of the simple models varied between 0·709 and 0·796, whereas the AUC values of the complex model ranged from 0·725 to 0·825. For both simple and complex models GAM, GLM, ANN and GBM provided the highest predictive performances based on both AUC and κ values. The results encourage further applications of the novel modelling methods in geomorphology. Copyright © 2008 John Wiley & Sons, Ltd.     

开封春季24h大风预报方法

利用开封1981-2000年3—5月的大风资料，依据冷空气路径，将大风分为东北型、西北型；选出预报因子，用逐步回归方法，对因子进行筛选，选出效果较好的预报因子建立方程。     

自动气象站蓄电池预测性维护的监测系统评定

为提高自动气象站蓄电池的维护效率,本文将预测性维护引入气象保障领域,通过对蓄电池预测性维护的技术要点、测试方案及流程等的介绍,选取3种不同劣化程度的蓄电池依次进行离线状态测试、浮充状态测试以及放电测试,通过数据对比分析初步给出了蓄电池预测性维护监测系统的综合评定。结果表明,在浮充状态下,监测系统电压和内阻的测试精度良好,能够很好地测试出蓄电池在不同健康状况下的电压与内阻值,即对报废电池、具有一定劣化程度电池和新电池的测试数据存在显著差别,在自动气象站蓄电池预测性维护中可利用浮充状态下测得的内阻数据对蓄电池劣化程度进行有效判别。     

华南冬季异常冷月预测概念模型Ⅱ──大气环流、极冰、积雪等物理因子特征

统计分析了华南冬季异常冷月及其前期大气环流及外部强迫因子的变化特征。结果表明,异常冷月当月至其前期３月西太平洋副热带高压带高压持续偏弱或位置偏东偏南。前一年东亚夏季风弱,当年冬季风强。前期７、８、９月和当年冬季亚洲极涡扩展。１月前期１１月和１２月青藏高原积雪日数偏少。２月前期１１个月,特别是３～８月北极Ⅳ区极冰偏多,１２月前期１～１１月Ⅱ期极冰偏多,３～８月Ⅲ区极冰偏少。综合上述因子以及５００ｈｐ     

基于透岩浆流体成矿理论的GIS矿产预测研究

本文以内生金属矿床透岩浆流体成矿理论为基础,以分级预测体制的思想为指导,认为在成矿理论指导下提出的各级预测体制对应的宏观地质标志是连接透岩浆流体成矿理论与GIS成矿信息的纽带,并从成矿地质背景、成矿作用触发机制和金属大规模堆积三方面,分别说明了各级预测体制及可能利用的宏观地质标志,最后通过研究目前广泛使用的GIS评价方法,初步建立起透岩浆流体成矿理论应用于宏观地质标志矿产预测的GIS预测系统框架。     

基于正态分布时间函数地表动态沉陷预测研究

为了研究地表动态沉陷规律,基于正态分布时间函数,结合地表沉陷预测公式,构建了能够进行任意点任意时刻地表动态沉陷预测的函数模型,分析曲线形态系数对时间函数和计算误差的影响,讨论正态分布时间函数的时空完备性,建立了基于时间函数的地表动态下沉计算公式。以辛置煤矿五采区开采为例,利用空间曲面拟合方法求取了地表动态沉陷预测参数,并对特征点的下沉趋势进行了预测。结果表明,地表沉陷预测时曲线形态系数δ > 2为其合理取值,理论预测相对中误差不会超过±4.55%,且随着δ的增大,预测误差逐渐减小;正态分布时间函数在地表下沉、下沉速度以及加速度方面均体现了地表沉陷时空分布的完备性。基于叠加原理的空间曲面拟合求参方法能够进行预测参数的自动求取,地表特征点下沉趋势预测最大中误差为±64 mm,相对中误差为±5.7%,理论值与实测值相吻合,基于正态分布时间函数的预测模型能够体现地表动态下沉的时空分布特征。     

乌鲁木齐河源1号冰川年物质平衡量的预报方法与资料插补

本利用乌鲁木齐河源１号冰川物质平衡量实测资料、天山大西沟温度大降水、乌鲁木齐地温资源，讨论了该冰川物质平衡与某些气候要素的关系；７月气温、夏季５－７月降水及３－４月地温是控制当年物质平衡量的主要气象要素；通过寻找最佳因子，利用多元线性回归方法，建立了该冰川年物质平衡的预报公式，同时讨论了资料插补问题。     

基于多手段异常结果的地震综合概率预测

地震综合预测是,根据地震地质、地震活动性、地震前兆异常和环境因素等多种分析手段,与前兆信息监测所进行的现代防震减灾研究.最初的综合预测方法主要以预报经验的数学化统计为主,主要包含测震学、地壳形变学、地磁地电、地下流体等4种学科的预测方法,以及衍生出的,如综合概率法、模糊聚类、模式识别、专家系统以及模糊综合判定等分析方法...     

成矿理论的预测能力及其改善途径

成矿理论预测能力决定了成矿理论的价值及其对找矿勘查的贡献。通过分析成矿理论预测能力的历史和现状,探讨了制约成矿理论预测能力的因素,并针对这些因素提出促使成矿理论预测能力提高的途径。成矿系统的复杂性和研究方法上的不足,使得应用成矿理论进行成矿预测时,既没有能完全把握成矿的本质过程及控制矿体定位的关键因素,也没能弄清勘查区域的地质特征与地质过程,还进行了条件并不充分的不严格推理,从而使得当前成矿理论的预测能力并不高。从历史的变化规律来看,任何一种成矿理论经过一段时间应用后,其预测能力是逐渐下降的;针对某一特定矿床建立的成矿模式的预测能力要比根据某一类矿床的共同特征而建立的成矿理论低得多,区域成矿的理论预测远比矿床(体)预测容易。针对创造和应用成矿理论的不足,提高成矿预测能力的途径主要有:(1)不盲目追求高新技术和新奇理论,扎扎实实做好基础地质工作;(2)发展地球信息探测技术,在更大的范围更准确地探测地球信息;(3)创新地壳构造理论,更加准确推测地壳构造时空演化特征;(4)以整体论方法研究成矿理论系统,揭示成矿作用的复杂性规律;(5)去掉多余假设,使理论的内核尽可能简单化;(6)发展地质动力学计算模拟,通过计算模拟更深入了解成矿及相关地质系统;(7)发展多源信息三维空间集成技术,实现推理计算化。     

Application of distributed hydrological models for predictions in ungauged basins: a method to quantify predictive uncertainty

Stream flow predictions in ungauged basins are one of the most challenging tasks in surface water hydrology because of nonavailability of data and system heterogeneity. This study proposes a method to quantify stream flow predictive uncertainty of distributed hydrologic models for ungauged basins. The method is based on the concepts of deriving probability distribution of model's sensitive parameters by using measured data from a gauged basin and transferring the distribution to hydrologically similar ungauged basins for stream flow predictions. A Monte Carlo simulation of the hydrologic model using sampled parameter sets with assumed probability distribution is conducted. The posterior probability distributions of the sensitive parameters are then computed using a Bayesian approach. In addition, preselected threshold values of likelihood measure of simulations are employed for sizing the parameter range, which helps reduce the predictive uncertainty. The proposed method is illustrated through two case studies using two hydrologically independent sub‐basins in the Cedar Creek watershed located in Texas, USA, using the Soil and Water Assessment Tool (SWAT) model. The probability distribution of the SWAT parameters is derived from the data from one of the sub‐basins and is applied for simulation in the other sub‐basin considered as pseudo‐ungauged. In order to assess the robustness of the method, the numerical exercise is repeated by reversing the gauged and pseudo‐ungauged basins. The results are subsequently compared with the measured stream flow from the sub‐basins. It is observed that the measured stream flow in the pseudo‐ungauged basin lies well within the estimated confidence band of predicted stream flow. Copyright © 2013 John Wiley & Sons, Ltd.