种植作物条件下水盐在土壤中的转化和运移构成了一个非常复杂的物理-化学-生物系统。研究种植作物条件下土壤水盐运移的动态规律和运行特征,建立水盐运移的数学模型对于指导盐渍土的灌溉管理及劣质水利用、土壤盐渍化与持续农业和生态...种植作物条件下水盐在土壤中的转化和运移构成了一个非常复杂的物理-化学-生物系统。研究种植作物条件下土壤水盐运移的动态规律和运行特征,建立水盐运移的数学模型对于指导盐渍土的灌溉管理及劣质水利用、土壤盐渍化与持续农业和生态环境之间的相互作用及土壤盐渍化预测等方面具有重要的意义。本研究首先提出了自主开发的土壤水盐运移的数学模型SWSTM(Soil Water and Salt Transport Mod-el),然后对冬小麦种植条件下土壤水盐的运动规律和特征进行了数值模拟,最后对不同地下水位和不同气象条件下的土壤水盐运移规律进行了数值预测,以期从土壤水盐运动的规律出发,提出一种应用数值模拟方法来预报土壤水盐动态的途径,同时为种植作物条件下田间大面积土壤水盐动态预测预报提供参考。展开更多
The impacts of outlying shocks on wind power time series are explored by considering the outlier effect in the volatility of wind power time series. A novel short term wind power forecasting method based on outlier sm...The impacts of outlying shocks on wind power time series are explored by considering the outlier effect in the volatility of wind power time series. A novel short term wind power forecasting method based on outlier smooth transition autoregressive(OSTAR) structure is advanced, then, combined with the generalized autoregressive conditional heteroskedasticity(GARCH) model, the OSTAR-GARCH model is proposed for wind power forecasting. The proposed model is further generalized to be with fat-tail distribution.Consequently, the mechanisms of regimes against different magnitude of shocks are investigated owing to the outlier effect parameters in the proposed models. Furthermore, the outlier effect is depicted by news impact curve(NIC) and a novel proposed regime switching index(RSI). Case studies based on practical data validate the feasibility of the proposed wind power forecasting method. From the forecast performance comparison of the OSTAR-GARCH models, the OSTAR-GARCH model with fat-tail distribution proves to be promising for wind power forecasting.展开更多
文摘种植作物条件下水盐在土壤中的转化和运移构成了一个非常复杂的物理-化学-生物系统。研究种植作物条件下土壤水盐运移的动态规律和运行特征,建立水盐运移的数学模型对于指导盐渍土的灌溉管理及劣质水利用、土壤盐渍化与持续农业和生态环境之间的相互作用及土壤盐渍化预测等方面具有重要的意义。本研究首先提出了自主开发的土壤水盐运移的数学模型SWSTM(Soil Water and Salt Transport Mod-el),然后对冬小麦种植条件下土壤水盐的运动规律和特征进行了数值模拟,最后对不同地下水位和不同气象条件下的土壤水盐运移规律进行了数值预测,以期从土壤水盐运动的规律出发,提出一种应用数值模拟方法来预报土壤水盐动态的途径,同时为种植作物条件下田间大面积土壤水盐动态预测预报提供参考。
基金supported by National Natural Science Foundation of China(No.51507031,No.51577025)
文摘The impacts of outlying shocks on wind power time series are explored by considering the outlier effect in the volatility of wind power time series. A novel short term wind power forecasting method based on outlier smooth transition autoregressive(OSTAR) structure is advanced, then, combined with the generalized autoregressive conditional heteroskedasticity(GARCH) model, the OSTAR-GARCH model is proposed for wind power forecasting. The proposed model is further generalized to be with fat-tail distribution.Consequently, the mechanisms of regimes against different magnitude of shocks are investigated owing to the outlier effect parameters in the proposed models. Furthermore, the outlier effect is depicted by news impact curve(NIC) and a novel proposed regime switching index(RSI). Case studies based on practical data validate the feasibility of the proposed wind power forecasting method. From the forecast performance comparison of the OSTAR-GARCH models, the OSTAR-GARCH model with fat-tail distribution proves to be promising for wind power forecasting.
