将微粒群和支持向量机用于耕地驱动因子选择的研究

结合微粒群算法(PSO)具有执行速度快、受问题维数变化影响小的优点及支持向量机算法(SVM)结构风险最小化原理,构建了基于离散二进制微粒群(BPSO)与支持向量机的耕地驱动力因子选择方法,使用特征子集中确定的特征来训练支持向量回归机,用适应度函数来评价回归机的性能,指导BPSO的搜索。实验表明,该方法能有效地提取出耕地驱动因子的特征子集,从而降低了指标的维数,保留了关键信息,以获得知识的最小表达。     

高精度重力测量在岩溶地区的初步应用

<正> 一、概 况 在山东省泰安县潘河桥头Ⅳ号工区,解决岩溶地区坍陷问题中,试验研究采用大比例尺高精度重力测量,取得一定效果。 工区内灰岩埋深一般在25米左右,其上为第四系复盖,地形平坦,第四系的密度为1.9～2.0克/厘米~3,灰岩的密度为2.5～2.7克/厘米~3,上部为鲡状灰岩,溶蚀现象十分明显,岩溶发育,溶洞直径小的有几厘米,大的有几米,但最大直径没有超过10米。鲡状灰岩下部为豹皮灰岩,结构致密,也有小的溶洞存在。溶洞中多数充填砂粒、粘土等第四系沉积物。     

Using raw regional climate model outputs for quantifying climate change impacts on hydrology

General circulation model outputs are rarely used directly for quantifying climate change impacts on hydrology, due to their coarse resolution and inherent bias. Bias correction methods are usually applied to correct the statistical deviations of climate model outputs from the observed data. However, the use of bias correction methods for impact studies is often disputable, due to the lack of physical basis and the bias nonstationarity of climate model outputs. With the improvement in model resolution and reliability, it is now possible to investigate the direct use of regional climate model (RCM) outputs for impact studies. This study proposes an approach to use RCM simulations directly for quantifying the hydrological impacts of climate change over North America. With this method, a hydrological model (HSAMI) is specifically calibrated using the RCM simulations at the recent past period. The change in hydrological regimes for a future period (2041–2065) over the reference (1971–1995), simulated using bias‐corrected and nonbias‐corrected simulations, is compared using mean flow, spring high flow, and summer–autumn low flow as indicators. Three RCMs driven by three different general circulation models are used to investigate the uncertainty of hydrological simulations associated with the choice of a bias‐corrected or nonbias‐corrected RCM simulation. The results indicate that the uncertainty envelope is generally watershed and indicator dependent. It is difficult to draw a firm conclusion about whether one method is better than the other. In other words, the bias correction method could bring further uncertainty to future hydrological simulations, in addition to uncertainty related to the choice of a bias correction method. This implies that the nonbias‐corrected results should be provided to end users along with the bias‐corrected ones, along with a detailed explanation of the bias correction procedure. This information would be especially helpful to assist end users in making the most informed decisions.     

Soil property variation mapping through data mining of soil category maps

Spatial information on soil properties is an important input to hydrological models. In current hydrological modelling practices, soil property information is often derived from soil category maps by the linking method in which a representative soil property value is linked to each soil polygon. Limited by the area‐class nature of soil category maps, the derived soil property variation is discontinuous and less detailed than high resolution digital terrain or remote sensing data. This research proposed dmSoil, a data‐mining‐based approach to derive continuous and spatially detailed soil property information from soil category maps. First, the soil–environment relationships are extracted through data mining of a soil map. The similarity of the soil at each location to different soil types in the soil map is then estimated using the mined relationships. Prediction of soil property values at each location is made by combining the similarities of the soil at that location to different soil types and the representative soil property values of these soil types. The new approach was applied in the Raffelson Watershed and Pleasant Valley in the Driftless Area of Wisconsin, United States to map soil A horizon texture (in both areas) and depth to soil C horizon (in Pleasant Valley). The property maps from the dmSoil approach capture the spatial gradation and details of soil properties better than those from the linking method. The new approach also shows consistent accuracy improvement at validation points. In addition to the improved performances, the inputs for the dmSoil approach are easy to prepare, and the approach itself is simple to deploy. It provides an effective way to derive better soil property information from soil category maps for hydrological modelling. Copyright © 2014 John Wiley & Sons, Ltd.     

基于主体功能区约束的大气污染物总量控制目标分配研究

考虑经济发展水平、污染物排放现状、污染物治理水平、空气质量,特别是国家主体功能区环境目标约束等因素,构建大气污染物排放总量分配的指标体系,用改进的等比例分配方法对2015年国家SO2,NOx总量控制目标进行区域分配。分配结果表明:SO2和NOx削减量大的省份主要集中在华北平原及其周围地区,这些地区污染物排放量大、空气质量较差;削减比例较大的地区主要集中在西部地区以及北京、天津2个直辖市,这些地区单位GDP能耗高、工业污染物去除率低、空气质量差;削减量相对较小的地区主要集中在西南和南部一些省份;削减比例较小的地区主要集中在中南部和南部几个省份,这些地区污染物排放量相对较少,空气质量好于其他省份。