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| 1 | Self-adaptive Processing and Forecasting Algorithm for Univariate Linear Time Series显示文摘As the Box-Jenkins method could not grasp the non-stationary characteristics of time series exactly, nor identify the optimal forecasting model order quickly and precisely, a self-adaptive processing and forecasting algorithm for univariate linear time series is proposed. A self-adaptive series characteristic test framework which employs varieties of statistic tests is constructed to solve the problem of inaccurate identification and inadequate processing for non-stationary characteristics of time series. To achieve favorable forecasts, an optimal forecasting model building algorithm combined with model filter and candidate model pool is proposed, in which a univariate linear time series forecasting model is built. Experimental data demonstrates that the proposed algorithm outperforms the comparative method in all forecasting performance statistics. | LIU Shufen GU Songyuan PENG Jun | 2017 | Chinese Journal of Electronics2017，26，6： | 2 |
| 2 | 基于多源遥感数据的马铃薯收入保险应用研究显示文摘[目的]研究面向创新型农业保险业务中缺少及时准确的第三方作物产量结果用于灾损理赔的问题。引入多源卫星遥感测产技术,识别测产关键因子,构建产量模型。[方法]文章运用多元线性回归分析方法,选取山西省马铃薯主产县岚县为研究区,计算基于Sentinel2影像的植被指数,结合气象卫星数据与实测单产数据,筛选关键因子,建立马铃薯单产遥感测产经验模型。[结果]采用GF-2影像分割与Sentinel2长势时序识别岚县马铃薯种植面积为8477.65hm^(2),精度检验Kappa值为0.72。保险公司岚县承保马铃薯面积2476.37hm^(2),承保覆盖率为29.21%。测产结果显示,马铃薯单产与区域关键期地表温度参数相关性较好,岚县遥感测产获得平均单产为13.76 t/hm^(2),实地测产获得平均单产为14.06 t/hm^(2),误差百分比为2.13%,分乡镇平均误差百分比为22.97%,基本满足理赔业务需求。在2018年保险期结束后一周内,保险公司启动快速赔付,支付赔款125.29万元,赔付率48.46%。[结论]遥感测产具有大范围、时效性好、可靠性高等特点,能够迅速为创新型保险产品提供测产理赔结果,提高理赔效率,保障农民收入。 | 朱玉霞 牛国芬 陈爱莲 孙伟 张峭 赵思健 | 2021 | 中国农业资源与区划2021，42，10： | 2 |
| 3 | Projections of temperature extremes based on preferred CMIP5 models:a case study in the Kaidu-Kongqi River basin in Northwest China显示文摘The extreme temperature has more outstanding impact on ecology and water resources in arid regions than the average temperature.Using the downscaled daily temperature data from 21 Coupled Model Inter-comparison Project(CMIP)models of NASA Earth Exchange Global Daily Downscaled Projections(NEX-GDDP)and the observation data,this paper analyzed the changes in temporal and spatiotemporal variation of temperature extremes,i.e.,the maximum temperature(Tmax)and minimum temperature(Tmin),in the Kaidu-Kongqi River basin in Northwest China over the period 2020–2050 based on the evaluation of preferred Multi-Model Ensemble(MME).Results showed that the Partial Least Square ensemble mean participated by Preferred Models(PM-PLS)was better representing the temporal change and spatial distribution of temperature extremes during 1961–2005 and was chosen to project the future change.In 2020–2050,the increasing rate of Tmax(Tmin)under RCP(Representative Concentration Pathway)8.5 will be 2.0(1.6)times that under RCP4.5,and that of Tmin will be larger than that of Tmax under each corresponding RCP.Tmin will keep contributing more to global warming than Tmax.The spatial distribution characteristics of Tmax and Tmin under the two RCPs will overall the same;but compared to the baseline period(1986–2005),the increments of Tmax and Tmin in plain area will be larger than those in mountainous area.With the emission concentration increased,however,the response of Tmax in mountainous area will be more sensitive than that in plain area,and that of Tmin will be equivalently sensitive in mountainous area and plain area.The impacts induced by Tmin will be universal and farreaching.Results of spatiotemporal variation of temperature extremes indicate that large increases in the magnitude of warming in the basin may occur in the future.The projections can provide the scientific basis for water and land plan management and disaster prevention and mitigation in the inland river basin. | CHEN Li XU Changchun LI Xiaofei | 2021 | Journal of Arid Land2021，13，6： | 0 |
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