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The area of Arctic sea ice has dramatically decreased, and the length of the open water season has increased;these patterns have been observed by satellite remote sensing since the 1970 s. In this paper, we calculate the net primary productivity(NPP, calculated by carbon) from 2003 to 2016 based on sea ice concentration products,chlorophyll a(Chl a) concentration, photosynthetically active radiation(PAR), sea surface temperature(SST), and sunshine duration data. We then analyse the spatiotemporal changes in the Chl a concentration and NPP and further investigate the relations among NPP, the open water area, and the length of the open water season. The results indicate that(1) the Chl a concentration increased by 0.025 mg/m~3 per year;(2) the NPP increased by 4.29 mg/(m~2·d) per year, reaching a maximum of 525.74 mg/(m~2·d) in 2016; and(3) the Arctic open water area increased by 57.23×10~3 km~2/a, with a growth rate of 1.53 d/a for the length of the open water season. The annual NPP was significantly positively related to the open water area, the length of the open water season and the SST.The daily NPP was also found to have a lag correlation with the open water area, with a lag time of two months.With global warming, NPP has maintained an increasing trend, with the most significant increase occurring in the Kara Sea. In summary, this study provides a macroscopic understanding of the distribution of phytoplankton in the Arctic, which is valuable information for the evaluation and management of marine ecological environments. 相似文献
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边坡稳定性分析中,模糊点估计法能同时考虑模糊不确定性和随机不确定性因素。针对传统模糊点估计法计算工作量大的缺点,提出一种神经网络改进模糊点估计法。利用拉丁超立方抽样法和径向基函数神经网络（RBF）建立边坡安全系数的预测模型;对黏聚力和内摩擦角等模糊随机变量取λ截集,并在各截集水平对参数进行组合;利用建立的预测模型对各参数组合的安全系数进行预测;最后由统计矩点估计法计算边坡的可靠度指标。实例分析表明：改进模糊点估计法使用方便、结果可靠,且能通过增加λ截集水平的数目来提高计算精度。对于含有2～4个模糊随机变量的边坡,采用改进模糊点估计法计算可靠度时λ截集水平的数目可近似取25。 相似文献
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This paper presents an artificial neural network (ANN)-based response surface method that can be used to predict the failure probability of c-φ slopes with spatially variable soil. In this method, the Latin hypercube sampling technique is adopted to generate input datasets for establishing an ANN model; the random finite element method is then utilized to calculate the corresponding output datasets considering the spatial variability of soil properties; and finally, an ANN model is trained to construct the response surface of failure probability and obtain an approximate function that incorporates the relevant variables. The results of the illustrated example indicate that the proposed method provides credible and accurate estimations of failure probability. As a result, the obtained approximate function can be used as an alternative to the specific analysis process in c-φ slope reliability analyses. 相似文献
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考虑崩塌堆积体边坡岩土体参数随机性和模糊性,以及地震力双向性,建立一种边坡地震动力模糊可靠度计算方法,针对竖向地震力对崩塌堆积体边坡稳定可靠性的影响进行进一步研究。首先,选用动力有限元时程分析法计算出双向地震工况下崩塌堆积体边坡的响应特征,并运用模糊理论对强度参数进行模糊性处理;然后,根据Mohr-Coulumb强度准则构建边坡安全系数与可靠度的时程计算模型;最后,采用边坡地震可靠性评价新方法,通过MATLAB编写相应程序,实现计算和分析结果的快速输出。案例结果表明:新方法计算结果更加合理,对工程而言也更加安全;竖向地震作用均对崩塌堆积体边坡整体可靠性存在影响,但影响程度需根据工程实际情况进行分析。在算例工况下,竖向地震对崩塌堆积体边坡的可靠性影响很小,仅使得可靠度降低3.55%,因此,可仅考虑水平地震的影响。 相似文献
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An Artificial Neural Network-Based Response Surface Method for Reliability Analyses of c-? Slopes with Spatially Variable Soil 
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下载免费PDF全文 This paper presents an artificial neural network (ANN)-based response surface method that can be used to predict the failure probability of c-? slopes with spatially variable soil. In this method, the Latin hypercube sampling technique is adopted to generate input datasets for establishing an ANN model; the random finite element method is then utilized to calculate the corresponding output datasets considering the spatial variability of soil properties; and finally, an ANN model is trained to construct the response surface of failure probability and obtain an approximate function that incorporates the relevant variables. The results of the illustrated example indicate that the proposed method provides credible and accurate estimations of failure probability. As a result, the obtained approximate function can be used as an alternative to the specific analysis process in c-? slope reliability analyses. 相似文献
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岩土参数的随机性会直接影响边坡稳定性评价结果的精度。首先,依据边坡参数的常用分布特征,利用拉丁超立方抽样法生成若干组边坡土性参数和几何参数的随机样本,用有限元强度折减法求解各组样本对应的边坡安全系数。再考虑土性参数的空间变异性,在二维随机场模型下将蒙特卡罗模拟和有限元强度折减法相结合求解各组样本对应的边坡失效概率。然后,利用样本数据及其安全系数和失效概率对径向基函数（RBF）神经网络进行训练和测试,从而建立边坡安全系数和失效概率的预测模型。算例表明,二维随机场模型能相对精确地考虑参数的空间变异性;在此基础上建立的神经网络模型对边坡的安全系数和失效概率具有较高的预测精度,且能极大地节省边坡稳定性分析的时间。 相似文献
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利用威德尔海区域2016年的Sentinel-1ASAR影像数据,采用"双冒泡法"的sigma-on-mu探测器探测冰山边缘区域,并通过对边缘像元进行交换排序和凸显最大像元的方式识别冰山。以人工识别法为基础,通过与自动识别法的对比,定量地分析了"双冒泡法"的识别偏差。研究结果表明,"双冒泡法"识别的冰山线性尺寸和面积等信息中纵横向最大长度分别为24.52km和11.16km;面积为220.833 6km2;单体识别偏差率为2.87%,低于自动识别法(7.5%);平均偏差率为2.48%,亦低于自动识别法(7.27%)。同时,基于"双冒泡法",提出了较小冰山边界的手动分离法(像元≤100),与自动识别方法相比,该方法的手动分离以具体的像元边界为基准,提高了对较小冰山的识别精度。 相似文献
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