Doline probability map using logistic regression and GIS technology in the central Ebro Basin (Spain)

In the surroundings of Zaragoza, karstification processes are especially intense in covered karst areas where fluvial terraces lie directly on Tertiary evaporites. Since the beginning of Quaternary, these processes have lead to the development of collapse and subsidence dolines with a wide range of sizes, which have significant economic impacts. To reduce economic impact and increase safety, a regional analysis of this phenomenon is needed for spatial management. Therefore, a probability map of dolines was developed using logistic regression and geographic information system (GIS) techniques. This paper covers the selection of input data, manipulation of data using the GIS technology, and the use of logistic regression to generate a doline probability map. The primary variable in the doline development in this area is geomorphology, represented by the location of endorheic areas and different terrace levels. Secondary variables are the presence of irrigation and the water table gradient.     

青岛港风暴潮经验统计预报

本文利用青岛港多年实测资料,分析了该港风暴潮概况。而后通过多元回归技术,求取了该港极值增减水的预报公式。经非独立和独立检验,结果令人满意。     

成都城市发展对热岛强度影响的主成份回归分析

本文对成都市总人口、建成区面积等11个因子、作了主成份回归L-S估计和M-估计,讨论了成都城市发展对“热岛”强度的主要影响因子。结果表明,城区房屋建筑面积及总人口数是影响城市气候(气温)的主要因子,其次为城市人口总户数、建成面积等。 文中,对回归方程进行了拟合计算,回归效果比较满意(尤其是稳健回归)。     

Comparison of structural and least-squares lines for estimating geologic relations

Two different goals in fitting straight lines to data are to estimate a true linear relation (physical law) and to predict values of the dependent variable with the smallest possible error. Regarding the first goal, a Monte Carlo study indicated that the structural-analysis (SA) method of fitting straight lines to data is superior to the ordinary least-squares (OLS) method for estimating true straight-line relations. Number of data points, slope and intercept of the true relation, and variances of the errors associated with the independent (X) and dependent (Y) variables influence the degree of agreement. For example, differences between the two line-fitting methods decrease as error in X becomes small relative to error in Y. Regarding the second goal—predicting the dependent variable—OLS is better than SA. Again, the difference diminishes as X takes on less error relative to Y. With respect to estimation of slope and intercept and prediction of Y, agreement between Monte Carlo results and large-sample theory was very good for sample sizes of 100, and fair to good for sample sizes of 20. The procedures and error measures are illustrated with two geologic examples.     

合肥市耕地和粮食可持续发展研究

收集1980～1999年合肥市耕地、人口、粮食等资料，定量分析在城市化快速发展下耕地的数量、质量变化和耕地减少的形式以及人口、粮食的变化，并用回归法分析它们的变化趋势。研究认为，在2005，2010年合肥市食物保障能力分别为97％和95％，对粮食生产与其影响因素进行了灰色关联分析，提出提高粮食产量的对策和实现途径。     

The separation of the influence of nutrients and climate on the varve time-series of baldeggersee, Switzerland

The varve data-set from a freeze-core taken in the deepest part of Baldeggersee was subjected to different multivariate statistical analyses in order to estimate the amount of variance in the varve thickness measures explicable by past climate and by the trophic state of the lake. A comparison of two different time-periods (1902 to1992 versus 1920 to 1980) revealed that the lake restoration programme since 1982 has had a significant impact on the formation of the seasonal layers. Results of the partitioning of the variance in the varve thickness measures showed that about two thirds of the variance are unexplained by a climate and trophic state model and that trophic state explains 6%, whereas climate accounts for about 28% of the variance before the effect of lake restoration had a strong impact on the varves. Among the climate parameters the amount of annual precipitation is a strong predictor for explaining the thickness of both dark layer and total couplet thickness, whereas summer precipitation is important for the thickness of the light layer.     

基于门限回归的气温对光伏发电量的影响分析

光伏发电与气象要素密切相关,正确认识气象因子对于光伏发电量及其发电效率的影响是进行发电量预测和维持光伏电站稳定运行的重要基础。采用江苏省淮安市某小型光伏电站2018—2020年发电量数据、淮安市太阳辐射观测站数据及光伏电站附近气象站观测数据,分析气温、太阳总辐射等气象因子对光伏发电量的影响。相关性分析结果表明,光伏发电量与太阳总辐射呈极强的正相关,基本随太阳总辐射的变化而变化;与气温、降水分别呈弱的正相关和弱的负相关;与风速几乎无相关。然而,灰色关联度分析显示,光伏发电量还与气温呈现出较高的灰色关联度。进一步分析表明,光伏发电量随太阳总辐射的变化受到气温的明显调制,不同气温下光伏发电效率不同。门限回归分析显示,当气温达到一定阈值时光伏发电效率会出现下降,具体表现为：当气温达到12.1 ℃,光伏发电效率下降26.7%;当气温超过22.8 ℃,光伏发电效率下降43.7%。使用这两个温度阈值建立的TR（Threshold Regression）模型的准确率要比线性回归模型提高约10%。     

基于FY-3C微波辐射成像仪海表温度产品的偏差订正方法研究

提出一种针对FY-3C搭载的微波辐射成像仪(MWRI)海表温度产品的分段回归偏差订正方法,该方法通过引进气候态海表温度数据,建立与关联实测海表温度相匹配的回归模型,并通过对模型中关联变量的误差分析,选择最优样本进行分段回归,以实现对海表温度数据的重新估计。通过对MWRI海表温度数据的偏差订正试验表明,采用分段回归方法获得的订正结果无论在误差指标的空间分布还是时间序列上,都要明显优于采用传统概率密度函数偏差订正方法的结果。其中,采用概率密度函数方法订正后的海表温度产品误差标准差和均方根误差从订正前的0.9—1.0℃,减小到0.8℃左右,而采用分段回归方法获得相应的订正误差仅为0.6℃左右,订正效果有明显改善。     

海南省连片贫困地区农户致贫风险分析

农村贫困与减贫是世界性难题,也是中国各级政府高度重视并着力解决的重大民生问题。基于农户及致贫风险的文献梳理,从区位、社会和劳动力3个要素维度构建了农户致贫风险分析的二元Logistic回归模型。采用484户农户问卷调查数据,分析了海南省连片贫困地区农户的致贫风险,提出有效减贫和持续发展对策。研究发现:①海南连片贫困地区生态环境良好但贫困发生率较高,家庭劳动力较充裕但受教育水平较低,子女教育支出负担重,因病因残致贫比例较高,女性务工人口较多,农户自身脱贫致富的发展动力不足。②海拔高度200 m以下、男性户主、拥有残疾或患病成员、务工人口比例低、女性务工人员占比高、以及单位劳动力供养学生数高的农户具有更大的致贫风险。③研究未发现女性户主、少数民族、低受教育水平户主、大型规模家庭有更高的致贫风险,女性成员比例、抚养比等因素对农户贫困影响较小。激发农户内生动力、大力发展特色化和规模化农业、增加农户就业机会、加强针对农民工、女性务工人员和病残群体的社会保障等减贫政策制定实施是实现脱贫攻坚目标的重要途径。     

一种基于核学习的储集层渗透率预测新方法

基于核学习的支持向量机,是一种采用结构风险最小化原则代替传统经验风险最小化原则的新型统计学习方法,具有完备的理论基础。这里提出了核学习技术在储集层非均质特性描述中渗透率参数预测的新用途。在复杂地层中,基于支持向量机的智能和自适应模式识别能力而建立了常规测井多参数信息输入的渗透率预测模型,然后对实际油田储集层渗透率进行了预测。与常规线性回归模型预测结果相对比,所提出的方法更易于使用,很少受不确定因素的影响,并具有较强的信息整合能力以及更高的预测准确性和可信度。