基于SPI的辽宁省庄河市近35年干旱演变特性研究

庄河市是辽南地区的重要水源地,分析其干旱演变特性有利于该地区水资源的合理开发与利用,对于防旱减灾、合理布局产业结构具有重要的现实意义。本文根据庄河市1984年1月1日至2018年12月31日的逐日降水数据计算了1个月、3个月、6个月、12个月共4个不同时间尺度下的标准化降水指数(SPI),并基于SPI对该地区近35年的年际与季节干旱演变特征进行了分析。结果表明:在年际变化方面,庄河市的SPI在2000年以前波动幅度较小,说明该地区的降水在时间上分布较为均匀;但自2000年以来,SPI序列出现较大的波峰与波谷,说明降雨的分布不均匀性有所增强;SPI的时间序列趋势线斜率为负值,说明该地区整体上呈现偏旱趋势,因此其水资源压力不断增大。在季节演变特征方面,夏季干旱和秋季干旱呈现增强趋势;秋旱的发生频率最高,而夏旱的出现频率最低。本研究结果可为庄河地区的抗旱减灾与水资源规划提供科学依据。     

A band selection approach based on wavelet support vector machine ensemble model and membrane whale optimization algorithm for hyperspectral image

Applied Intelligence - Hyperspectral Image (HSI) has become one of the important remote sensing sources for object interpretation by its abundant band information. Among them, band selection is...     

Short-term load forecasting using bayesian neural networks learned by Hybrid Monte Carlo algorithm

This paper presents a short term load forecasting model based on Bayesian neural network (shorted as BNN) learned by the Hybrid Monte Carlo (shorted as HMC) algorithm. The weight vector parameter of the Bayesian neural network is a multi-dimensional random variable. In learning process, the Bayesian neural network is considered as a special Hamiltonian dynamical system, and the weights vector as the system position variable. The HMC algorithm is used to learn the weight vector parameter with respect to Normal prior distribution and Cauchy prior distribution, respectively. The Bayesian neural networks learned by Laplace algorithm and HMC algorithm and the artificial neural network (ANN) learned by the BP algorithm were used to forecast the hourly load of 25 days of April (Spring), August (Summer), October (Autumn) and January (Winter), respectively. The roots mean squared error (RMSE) and the mean absolute percent errors (MAPE) were used to measured the forecasting performance. The experimental result shows that the BNNs learned by HMC algorithm have far better performance than the BNN learned by Laplace algorithm and the neural network learned BP algorithm and the BNN learned by HMC has powerful generalizing capability, it can welly solve the overfitting problem.     

电网故障下光伏并网低压故障穿越控制策略的研究

电网发生故障或扰动可能造成光伏并网点电压跌落,严重影响电力系统的安全运行,光伏发电站有必要具备在电压跌落范围和时间内保证不脱网运行的能力。提出一种基于无功补偿的光伏并网低压穿越控制策略。该方法在检测到电压跌落时,通过断开外部电压环路将双闭环控制模式更改为单电流环路操作模式,采用改进后的无功补偿控制策略防止逆变器产生过电流,为电网电压提供无功补偿,较好实现不脱网运行。最后利用Matlab / Simulink软件比较和分析低压穿越控制策略前后的相关参数。仿真结果表明,改进的控制策略可以有效地抑制逆变器输出电流的增加,并且能提供无功功率来支持电网电压,以在电网电压骤降期间实现低压穿越。     

NDN数据提供者移动支持策略能耗建模与分析

基于数据提供者的移动节点在网络中的不同位置及不同移动速度,结合命名数据网络（NDN）请求者驱动的数据传输模式,提出了3类NDN移动支持策略的网络能耗解析表达式,为NDN移动网络的研究提供了形式化的建模方法.对这3类移动支持策略在不同场景下能耗的分析与比较结果表明,数据流行度分布、移动节点所处位置及移动速度对策略的能耗影响不尽相同,为后续探求最优能效的移动支持策略提供了理论参考.     

宁夏引黄灌区末段区域农业水资源配置研究

为探索农业水资源配置的新模式,以"自然—人工"二元水循环理论为基础,以宁夏引黄灌区末段区域(石嘴山市)为研究区,分析农田水循环过程,提出"引沟济渠"农田灌溉水源调配新思路。以农田灌溉水质要求、灌溉水量需求以及水资源管理用水红线为约束,借助MATLAB优化工具进行模拟计算,结果表明:在满足农田灌溉水质的前提下,取用惠农渠水4.83亿m~3、第五排水沟水0.92亿m~3、唐徕渠水0.82亿m~3、第三排水沟水0.31亿m~3,可节约黄河水1.23亿m~3;在上游排水沟水质较差情况下水质是主要约束条件,在上游排水沟水质较好情况下排水量是主要约束条件;实施"引沟济渠"模式应本着"差水控量,好水用尽"的原则,充分利用上游排水沟的来水。该农业水资源利用模式可节约渠灌水,做到"一水多用、多水联用",为干旱区灌区水资源配置提供借鉴。     

基于等幅周期荷载下软岩Bingham本构模型的研究

为建立一种描述等幅周期荷载下软岩应变特性的本构模型,将Bingham模型中的常值黏壶元件替换成与循环次数相关的变系数黏壶元件,常值弹性元件替换为随周期荷载循环次数的增大而衰减的非定值弹性元件,即可得到反映周期荷载下软岩各种应变规律的Bingham本构模型。当循环荷载应力上限值σmax大于岩石的临界强度σs时,模型为可反映软岩3个应变阶段的改进4参数Bingam模型;反之则为反映软岩前两个应变阶段的非常值2参数Hook模型。将周期荷载分解为一个定值荷载和一个平均应力值为零的周期荷载,基于流变力学理论给出了定值荷载作用下基于该模型的本构方程式,再根据黏弹性力学理论,给出了平均应力值为零的周期荷载下基于该模型的本构方程式,最后将已获得的本构方程式叠加即得到周期荷载下软岩本构方程式。结果表明:所建立的本构方程式可较好描述周期荷载下不同软岩各类应变特征,对各类软岩的应变曲线拟合的相关系数均在0.868以上,且同一种岩石模型中弹性元件系数初始值随周期荷载的动应力幅值的增大而减小,岩石加速应变速率参数随动应力幅值的增大而增大。     

Simulation of Nonstationary Spring Discharge Using Time Series Models

We present a detailed analysis and comparison of two time series models, i.e., ARIMA and ARIMA-GARCH, to simulate the discharge of a karst spring (Niangziguan Springs (NS) complex) in the northern China. Statistical tests for the residuals are applied to examine the reasonability of the models. Statistically, both models are reasonably good to simulate the mean value of the discharge of the NS complex. The statistical test shows that the residual discharge data have conditional time-varying variance and volatility clustering, known as heteroscedasticity of the data. Calibration test shows that the ARIMA-GARCH model gives a varying confidence interval, which can more effectively capture the heteroscedasticity of the data, comparing with a constant confidence interval in the ARIMA model. In the validation and application process, we applied two approaches to simulate the discharge data: (1) fixed models, and (2) evolving models. The confidence interval width monotonically increases in both fixed models, and the fixed ARIMA-GARCH model has faster increasing confidence interval width than the fixed ARIMA model. This suggests that the fixed time series models are only suitable for short-term prediction. However, we found that this drawback can be compensated by updating the model once new data become available. Our evolving models show more reasonable confidence interval width for both models. In addition, the application shows that the ARIMA-GARCH model is very sensitive to the data fluctuation. We also found the evolving ARIMA-GARCH model was able to return to the narrow confidence interval width once the fluctuation diminished. Hence, we conclude that the ARIMA-GARCH model is more suitable for the sequences with strong heteroscedasticity.     

陕北无定河流域土壤侵蚀与植被覆盖和降雨关系研究

基于2000—2014年陕北无定河流域日降水、DEM、土壤类型、MODIS NDVI等数据,利用修正通用土壤流失方程(RUSLE)估算了流域土壤侵蚀情况,定量分析了植被覆盖和降雨在土壤侵蚀中的作用,结果表明:(1)无定河流域土壤侵蚀以微度侵蚀为主,平均占流域面积的88.35%,各土壤侵蚀等级面积比例随等级的升高而降低。2000—2014年微度侵蚀面积比例为下降趋势,轻度及以上等级面积比例均为上升趋势。(2)不考虑降雨因子影响时,无定河流域仍以微度侵蚀为主,且为增加趋势,而其他土壤侵蚀等级均为下降趋势。不考虑植被覆盖因子时,流域土壤侵蚀与模型计算结果的变化趋势基本一致。(3)植被覆盖对土壤侵蚀面积比例和变化速率的贡献率分别为13.67%和24.55%,而降雨作用达到86.33%和75.44%,表明降雨是流域土壤侵蚀的主要动力和控制因子,降雨变化主导着流域土壤侵蚀变化过程。     

安徽省农业旱灾脆弱性综合分析与评价

旱灾是影响安徽省农业特别是粮食生产可持续发展的重要制约因素。在相同致灾强度下,灾情会随灾害脆弱性的增强而加重,因此,研究农业旱灾脆弱性对安徽省防灾减灾和农业可持续发展具有特别重要意义。根据影响旱灾脆弱性的因素和指标体系选取的原则,选择了旱灾面积比、森林覆盖率、农村人均收入等9个指标,采用加权综合评价方法对各市旱灾脆弱性进行分析,并利用聚类分析方法进行旱灾脆弱性区划。结果表明,除合肥市、阜阳市、池州市、宿州市和黄山市5个市需要采取措施降低旱灾脆弱度外,安徽省总体上农业旱灾脆弱性较弱,有较强的抗旱承灾能力。