贝纳德效应和地震预报

本文根据海城地震前后重力变化的实测资料提出重力加速度随时间变化的一种简化表示。在重力发生扰动时对贝纳德问题的动力学方程进行求解。计算结果表明,在强地震的孕育期,贝纳德花样的周期变化在一定时间后将自动停止。这个结论的验证可以作为一种新的地震预测方法的理论基础。     

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。     

多级模糊决策在地震预报中的应用

本文把多级模糊决策方法应用于地震预报当中。文中详细论证和推导了多级模糊决策方法与模型。该方法使决策部门可以根据输入前兆的变化随时作出合理的对策。文中还给出了地震预报多级模糊决策数字解实例,并用回顾法讨论了1976年松潘7.2级地震的模糊预报决策。     

基于随机模拟的地下水污染物最优水力截获量

水力截获技术是净化或抑制地下水污染最为广泛使用的一种方法，而该技术实施过程中，如何确定最优水力截获量是其需要重点解决的关键问题.本文针对传统确定性方法计算最优水力截获量不合理的问题，从水文地质参数的随机性出发，应用基于随机理论的蒙特卡罗方法，通过实例来研究渗透系数的空间变异性对地下水污染物水力截获系统的影响，并寻求估算最优水力截获量的新方法.通过研究表明：基于确定性方法计算出最优水力截获量为110m³/d时恰好能完全截获污染区的污染物；应用随机模拟研究含水层渗透系数的空间变异性对水力截获系统的影响，发现当以传统确定性方法所计算的最优水力截获量（110m³/d）抽水时，并不能总是完全截获地下水污染物，其面临的稳定平均风险率高达24%；充分考虑了含水层渗透系数空间变异的Monte Carlo方法较以往传统确定性方法更为可靠，为此本文提出利用随机方法从截获系统可接受风险角度确定最优截获量的新思路.     

基于灰色-马尔科夫模型的我国能源消费结构优化调整

本文以我国1997—2010年的消费结构数据为基础,运用灰色-马尔科夫链模型对数据进行了分析和预测,然后通过MATLAB预测了我国未来十年的能源消费结构,结合我国政府承诺的非化石能源目标对模型预测结果做了进一步的修正,最后根据能源发展趋势给出了能源消费结构的优化策略。     

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。     

Spatial allocation of material flow analysis in residential developments: a case study of Kildare County,Ireland

Studies of urban metabolism provide important insights for environmental management of cities, but are not widely used in planning practice due to a mismatch of data scale and coverage. This paper introduces the Spatial Allocation of Material Flow Analysis (SAMFA) model as a potential decision support tool aimed as a contribution to overcome some of these difficulties and describes its pilot use at the county level in the Republic of Ireland. The results suggest that SAMFA is capable of identifying hotspots of higher material and energy use to support targeted planning initiatives, while its ability to visualise different policy scenarios supports more effective multi-stakeholder engagement. The paper evaluates this pilot use and sets out how this model can act as an analytical platform for the industrial ecology–spatial planning nexus.     

回采工作面瓦斯涌出量遗传投影寻踪回归预测

为快速、准确预测回采工作面瓦斯涌出量,基于投影降维思想,建立一种遗传算法(GA)投影寻踪回归预测方法。选取煤层瓦斯原始含量、埋藏深度、煤层厚度、煤层倾角、工作面长度、推进速度、采出率、临近层瓦斯含量、临近层厚度、临近层层间距、岩层岩性、开采深度作为评价因子,对某矿15个学习样本进行训练,建立GA投影寻踪回归预测模型。利用该矿3个实测样本对模型进行检验,并与主成分分析和BP神经网络方法结果进行对比。研究表明:利用GA投影寻踪回归预测回采工作面瓦斯涌出量,平均误差为3.43%,最大误差为5.7%,精度优于其他2种方法。     

Development and Operational Testing of a Super‐Ensemble Artificial Intelligence Flood‐Forecast Model for a Pacific Northwest River

Coastal catchments in British Columbia, Canada, experience a complex mixture of rainfall‐ and snowmelt‐driven contributions to flood events. Few operational flood‐forecast models are available in the region. Here, we integrated a number of proven technologies in a novel way to produce a super‐ensemble forecast system for the Englishman River, a flood‐prone stream on Vancouver Island. This three‐day‐ahead modeling system utilizes up to 42 numerical weather prediction model outputs from the North American Ensemble Forecast System, combined with six artificial neural network‐based streamflow models representing various slightly different system conceptualizations, all of which were trained exclusively on historical high‐flow data. As such, the system combines relatively low model development times and costs with the generation of fully probabilistic forecasts reflecting uncertainty in the simulation of both atmospheric and terrestrial hydrologic dynamics. Results from operational testing by British Columbia's flood forecasting agency during the 2013‐2014 storm season suggest that the prediction system is operationally useful and robust.     

Development of the Spatial Rainfall Generator (SRGEN) for the Agricultural Policy/Environmental Extender Model

Accurate spatial representation of climatic patterns is often a challenge in modeling biophysical processes at the watershed scale, especially where the representation of a spatial gradient in rainfall is not sufficiently captured by the number of weather stations. The spatial rainfall generator (SRGEN) is developed as an extension of the “weather generator” (WXGEN), a component of the Agricultural Policy/Environmental eXtender (APEX) model. SRGEN generates spatially distributed daily rainfall using monthly weather statistics available at multiple locations in a watershed. The spatial rainfall generator as incorporated in APEX is tested on the Cowhouse watershed (1,178 km²) in central Texas. The watershed presented a significant spatial rainfall gradient of 2.9 mm/km in the lateral (north‐south) directions based on four rainfall gages. A comparative analysis between SRGEN and WXGEN indicates that SRGEN performs well (PBIAS = 2.40%). Good results were obtained from APEX for streamflow (NSE = 0.99, PBIAS = 8.34%) and NO₃‐N and soluble P loads (PBIAS ≈ 6.00% for each, respectively). However, APEX underpredicted sediment yield and organic N and P loads (PBIAS: 24.75‐27.90%) with SRGEN, although its uncertainty in output was lower than WXGEN results (PBIAS: ?13.02 to ?46.13%). The overall improvement achieved in rainfall generation by SRGEN is demonstrated to be effective in the improving model performance on flow and water quality output.