AN EVALUATION OF WEEKLY AND MONTHLY TIME SERIES FORECASTS OF MUNICIPAL WATER USE1

ABSTRACT: A cascade model for forecasting municipal water use one week or one month ahead, conditioned on rainfall estimates, is presented and evaluated. The model comprises four components: long term trend, seasonal cycle, autocorrelation and correlation with rainfall. The increased forecast accuracy obtained by the addition of each component is evaluated. The City of Deerfield Beach, Florida, is used as the application example with the calibration period from 1976–1980 and the forecast period the drought year of 1981. Forecast accuracy is measured by the average absolute relative error (AARE, the average absolute value of the difference between actual and forecasted use, divided by the actual use). A benchmark forecast is calculated by assuming that water use for a given week or month in 1981 is the same as the average for the corresponding period from 1976 to 1980. This method produces an AARE of 14.6 percent for one step ahead forecasts of monthly data and 15.8 percent for weekly data. A cascade model using trend, seasonality and autocorrelation produces forecasts with AARE of about 12 percent for both monthly and weekly data while adding a linear relationship of water use and rainfall reduces the AARE to 8 percent in both cases if it is assumed that rainfall is known during the forecast period. Simple rainfall predictions do not increase the forecast accuracy for water use so the major utility of relating water use and rainfall lies in forecasting various possible water use sequences conditioned on sequences of historical rainfall data.     

SHORT-TERM FORECASTING OF SNOWMELT RUNOFF USING ARMAX MODELS1

ABSTRACT: Time series models of the ARMAX class were investigated for use in forecasting daily riverflow resulting from combined snowmelt/rainfall. The Snowmelt Runoff Model (Martinec-Rango Model) is shown to have a form similar to the ARMAX model. The advantage of the ARMAX approach is that analytical model identification and parameter estimation techniques are available. In addition, previous forecast errors can be included to improve forecasts and confidence limits can be estimated for the forecasts. Diagnostic checks are available to determine if the model is performing properly. Finally, Kalman filtering can be used to allow the model parameters to vary continuously to reflect changing basin runoff conditions. The above advantages result in improved flow forecasts with fewer model parameters.     

高温堆肥法处理污泥的探讨

对于污水处理后所产生的污泥,较好的方法是采用无害化、资源化农用技术,利用污泥为原料,制成有机复合肥,不仅解决了污水污泥的处理问题,还可推动环保产业的发展,促进农业生产,具有明显的经济、社会和环境效益。     

紫外光谱结合正交试验设计同时测定水中苯系物

利用正交试验设计,以光谱预处理、特征筛选及多元校正方法为考察因素,每个因素的4种不同方法为水平,确定了水中3种苯系物（苯酚、苯胺及苯甲酸）紫外光谱数据的最佳分析方法,从而建立了其定量校正模型。对于苯酚、苯胺及苯甲酸,其光谱预处理、特征筛选及多元校正分别采用一阶导数+无信息变量消除法（UVE）+偏最小二乘回归（PLSR）,Savitzky-Golay平滑+变量结合种群分析法（VCPA）+PLSR,Savitzky-Golay平滑+移动窗口偏最小二乘法（MWPLS）+PLSR。在独立测试集上3组分的预测误差均方根（RMSEP）分别为0.809 4、0.796 3和0.945 4。水样加标回收实验的回收率为97.79%~103.84%,相对标准偏差（RSD）小于3%。该方法可作为一种同时测定水中苯系物的简便有效方法。     

泵串联水灭火系统在森林防火方面的应用

现阶段为了适应国际及国内防火形势,出现了以水灭火为主要形式的灭火方式,并且在实际灭火中体现出巨大的优势.其中泵串联水灭火就是一种很好的形式.林海集团设计了一套远距离接力水泵串联供水灭火系统,尝试以手抬水泵(消防泵)接力供水的方式,从有水源的地方经过消防泵的增压,通过消防水带输送到火场,为水灭火工作提供条件,以实现水灭火.     

Time series in M dimensions: The one-dimensional case: Pollution in the Chicago Sanitary and Ship Canal

A nonstationary time-series model is used to examine the changes occurring at sampling stations on the Chicago Sanitary and Ship Canal. Using data from upstream sampling sites, downstream levels of dissolved oxygen, total dissolved solids, nitrates and nitrites, and ammonia are accurately predicted. The method is simple, insensitive to extreme values, and responsive to changes in the system.     

焦化苯初馏份蒸馏釜发生爆炸的原因探讨

通过对某企业的一起典型爆炸事故案例进行分析,找出其产生的原因,提出了预防措施。     

人口与经济发展对植被的影响研究

植被是陆地表面主要的覆盖物，也是许多地球系统过程的重要变量。人类活动对植被的影响在区域尺度上是显著的。以重庆市为例，使用滑动平均和SG滤波对SPOT/VGT NDVI时间序列数据进行了平滑处理，结合GDP和人口栅格化数据，从时间序列和空间相关场两个方面分析了植被与人类活动因子之间的时空相关性。结果表明，从1998到2005年，植被的分布和生长状况并没有随GDP和人口的持续增加而增加。在时间序列上，植被覆盖与GDP和人口之间总体上呈显著的负相关关系；在空间上，这种相关关系表现出了较强的异质性。空间相关场表明：较强的负相关主要发生在相对发达的主城区周围，反映了快速的经济发展和城市化使得植被面积和生产力降低；然而在偏远的山区，由于经济落后同时植被覆盖较少，出现了正相关。     

基于BP神经网络的苯储罐泄漏事故风险评价模型研究

为了在事故发生之前对苯储罐进行风险评价，提出1种基于BP神经网络的泄漏事故风险评价方法，利用该方法构建了苯储罐的风险评价模型，并对模型进行了训练及验证。研究结果表明:BP神经网络成功完成了建模任务，且模型训练结果较好，可利用基于BP神经网络所构建的苯泄漏事故风险评价模型对苯储罐发生泄漏事故的风险进行评价。     

基于ARIMA的安全生产事故预测与实证研究

为了减少企业安全管理者在生产作业中由于不确定性导致错误决策所产生的风险,在企业历年安全生产事故数据基础上进行预测具有一定的现实意义。以某企业2008年至2011年的安全生产事故次数时序数据,采用EViews 5统计分析软件,基于ARIMA时间序列预测模型更加关注对事故发生是否平稳而相对于其他预测模型更关注于趋势研究的良好特点,建立安全生产事故ARIMA时序预测模型,并对2012年的安全生产事故发生次数进行预测,通过效果检验发现该模型预测结果基本上能够反映该企业安全生产事故发生的实际情况。通过ARIMA方法在某企业安全生产事故预测具体案例的实现,是对现有安全生产事故预测方法的补充和完善,可为企业安全管理和决策提供一定的指导。