基于大数据分析下的气候模型

为研究全球变暖与极寒天气间的关系,对加拿大13个省代表性测站10年的观测数据进行时空变化趋势分析,采用经验正交函数（EOF）寻找海洋表面温度历史数据的变化规律。另外利用BP神经网络建立了年平均温度、日降水量与地球吸热、散热、海表面温度、当地纬度间的关系,预测未来25年气候的变化,并建立了“极寒天气”与气候变化的关系模型。研究表明：高纬度地区温度、降水量普遍较低,同经度地区的温度差异较小且降水量变化不大;加拿大地区温度呈周期性变化,符合北半球的季节变化特征;北大西洋的东部与其他海洋的温度是反相关的,西太平洋南北回归线附近的海洋表面温度升高;“极寒天气”出现频率与气候变化有一定关系,局地极寒现象与全球变暖的大趋势并不矛盾。本研究为人们认识和理解“全球变暖”提供了一个新的思路。     

最优权组合预测法在采煤沉陷变形预测中的应用

煤矿地表沉降变形预测多基于煤矿开采沉陷预计理论展开,基于变形分析理论的变形预测模型目前多集中在单模型预测。本文基于组合预测思想,以非等间隔灰色预测模型与BP神经网络模型为预测单模型,以陕西北部某煤矿采煤工作面上方实测地表沉降值为数据源,以最优加权法对单模型预测结果开展了最优权组合,组合模型中两种单模型的权重分别为0.466 7、0.533 3。选取部分监测点的预测结果进行模型精度评价,结果表明：3种预测模型精度均达到了一级。经对比3种模型预测结果,最优权组合预测的模型精度较单模型明显提升,预测结果较非等间隔灰色预测模型与BP神经网络预测模型有明显增益。     

Biogeochemical Variation in Dimethylsulfide, Phytoplankton Pigments and Heterotrophic Bacterial Production in the Subarctic North Pacific during Summer

Dimethylsulfide (DMS), chlorophyll a (Chl-a), accessory pigments (fucoxanthin, peridinin and 19-hexanoyloxyfucoxanthin), and bacterial production (BP) were measured in the surface layer (0–100 m) of the subarctic North Pacific, including the Bering Sea, during summer (14 July–5 September, 1997). In surface sewater, the concentrations of DMS and Chl-a varied widely from 1.3 to 13.2 nM (5.1 ± 3.0 nM, mean ± S.D., n = 48) and from 0.1 to 2.4 µg L^–1 (0.6 ± 0.6 µg L^–1, n = 24), respectively. In the subarctic North Pacific, DMS to Chl-a ratios (DMS/Chl-a) were higher on the eastern side than the western side (p < 0.0001). Below the euphotic zone, DMS/Chl-a ratios were law and the correlation between DMS and Chl-a was relatively strong (r ² = 0.700, n = 27, p < 0.0001). In the euphotic zone, DMS/Chl-a ratios were higher and the correlation between DMS and Chl-a was weak (r ² = 0.128, n = 50, p = 0.01). The wide variation in DMS/Chl-a ratios would be at least partially explained by the geographic variation in the taxonomic composition of phytoplankton, because of the negative correlation between DMS/Chl-a and fucoxanthin-to-Chl-a ratios (Fuc/Chl-a) (r ² = 0.476, n = 26, p = 0.0001). Furthermore, there was a positive correlation between DMS and BP (r ² = 0.380, n = 19, p = 0.005). This suggests that BP did not represent DMS and dimethylsulfoniopropionate (DMSP) removal by bacterial consumption but rather DMSP degradation to DMS by bacterial enzyme.     

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基于改进BP神经网络的海底底质分类

通过采用遗传算法优化神经网络初始权值的方法,将GA算法与BP神经网络有机结合,应用于海底底质分类。基于多波束测深系统获取的反向散射强度数据,应用改进的BP神经网络分类方法,实现对海底基岩、砾石、砂、细砂和泥等底质类型的快速、准确识别。通过实验比较,GA-BP神经网络分类精度明显高于BP神经网络,证明了该方法的有效性和可靠性。     

基于贝叶斯正则化BP神经网络的DEM趋势面逼近

趋势面从宏观上揭示了研究对象的特性,在各领域发挥着重要作用。BP神经网络可以对复杂系统进行无限逼近,进而进行预测。建立了基于贝叶斯正则化BP神经网络的数字高程模型趋势面,与二次多项式建立的数字高程模型趋势面进行比较分析,证明了该方法的可行性和有效性。     

GA-BP网络模型在GPS似大地水准面精化中的应用

针对误差反向传播（BP）算法训练速度慢和易于陷入局部最小值的缺点,提出了利用遗传算法（GA）的全局寻优性,结合GA和BP的各自优点,分析和建立了进化神经网络（GA-BP）模型,并将该模型应用于似大地水准面模型精化。最后以南方某市E级GPS控制网高程数据为例,进行BP和GA-BP模型的对比实验,通过对内、外符合精度及MAPE（平均绝对误差百分比）指标分析,验证了该方法的可行性。     

山东沂沭河流域2000BC前后古文化兴衰的环境考古

Neolithic culture series in the Yishu River Basin developed in the order of Beixin culture-Dawenkou culture-Longshan culture-Yueshi culture. During the early and middle stage of Longshan culture （4600-4300 cal. yr BP）, the climate in the Yishu River Basin was warm and wet. Paddy-oriented agriculture planted paddy was very developed. The society was flourishing with great amount of archaeological sites. The cooling starting in 4200 cal. yr BP made the paddies shortfall in output or even no seeds were gathered. This situation intensified the discrepancy between population and resource. The scarcity in natural resource led to substantial decrease in population and subsequent drop in archaeological sites. About 4000 cal. yr BP Longshan culture was displaced by Yueshi culture which was relatively un- derdeveloped, simple and unsophisticated.     

古尔班通古特沙漠灌木冠幅预测模型

古尔班通古特沙漠是中国第二大沙漠,也是中国固定和半固定沙丘主要分布区,固沙灌木种较多。冠幅不仅是反映固沙灌木可视化的重要参数,也是反映沙漠植被生长情况的重要变量。以3种沙丘（固定沙丘、半固定沙丘和流动沙丘）上主要固沙灌木为研究对象,利用12种基础模型、BP(Backpropagation Neural Network)神经网络和支持向量机（Support Vector Machine,SVM）机器学习算法建立了基于固沙灌木株高和冠长率的冠幅预测模型,同时将两种机器学习算法拟合结果与基础模型进行比较,最终选出了适合研究区的冠幅预测模型。结果表明：（1）不同沙丘类型和不同灌木种类的最优冠幅预测模型不同,且固定沙丘和半固定沙丘模型优于流动沙丘。3种沙丘类型最优拟合为M2(Quadratic Model)模型;（2）白梭梭（Haloxylon persicum）在半固定沙丘和流动沙丘上拟合的最优模型分别为M2、M7(Gompertz),沙拐枣（Calligonum mongolicum）最优模型为M2,蛇麻黄（Ephedra distachya）和油蒿（Artemisia ordosica）在...