溢油对地表水体影响预测模型的搭建

以M原油管道在穿越N江处发生溢油事故为例,探讨了溢油对地表水体的影响、以及预测该影响所使用的预测模型。通过对石油类物质与水动力条件的作用分析,分析了一维水动力模型、污染物对流扩散模型的原理,探讨了两种模型的搭建。使用MIKE 11软件在河流水动力与污染物迁移两方面建立了预测模型,为后续模拟预测工作奠定了理论与模型基础。     

主成分回归模型在农业需水量预测中的应用

农业需水量的准确预测对区域发展具有十分重要的意义。农业需水量受多重因素的影响，且这些因素大多存在较强的相关性。通过介绍主成分分析法的原理和计算分析，以实例（z市1998--2010年农业用水资料）建立回归模型对需水量进行预测。结果表明，该模型应用于农业用水量预测，其结果与当地实际情况较吻合，模型的拟合程度和预测准确度均较好。     

灰色理论及改良方法对我国垃圾产量的预测

为了给城市环境规划提供理论依据,需要对城市生活垃圾产量进行预测,以期揭示其变化规律和发展趋势。本文对三种预测模型进行对比分析研究,并通过灰色关联度分析,选取与垃圾产量最为相关的5个因素,建立了包括多个因素指标的GM(1,1)预测方程的多元线性回归综合模型。该模型考虑了城市生活垃圾产量的主要影响因素,得到的拟合数据比较理想,预测模型和结果也更为合理可信。     

秦皇岛市地面塌陷模糊层次综合预测

通过对大量实际数据的分析,得到了影响秦皇岛市地面塌陷的主要因素有历史条件、地质条件、人为条件。在此基础上,建立了秦皇岛市地面塌陷模糊层次综合预测模型,模型包括3个层次、3个条件,对研究区划分了8 070个单元格并进行计算,得到地面塌陷危险性分区结果。该地面塌陷模糊层次综合预测模型的预测结果符合研究区实际情况,具有较高的可信度。     

多元线性回归预测模型在城市垃圾产量预测中的应用

分析了上海1995-2004年度每年垃圾产生量的数据,由此提出了可应用于城市垃圾预测的多元线性回归预测模型.通过与指数法、GM（1,1）模型、GM（1,1）SSODMM等模型的预测结果进行比较,精度达一级.表明该模型在城市垃圾产量的预测中具有一定的优越性和广泛的应用前景.     

粮食产量预测理论、方法与应用Ⅲ.粮食生产潜力中、长期预测理论、模型及其应用

粮食生产潜力中、长期预测的目的是为国家中、长期粮食生产规划提供科学依据。粮食生产潜力中、长期预测的“双向预测理论”：从若干个预测模型中选择出2个模型,一个模型预测的未来产量是持续增加的,体现产量持续增加的科技进步力量;另一个模型预测的未来产量是先增加后减少或持续减少的,体现影响产量持续增加的负面综合因素力量。应用结果表明：模型可预测未来1~10年的粮食生产潜力,平均预测误差在5％以内。大量案例证明粮食生产潜力中、长期预测的“双向预测理论”是科学的、方法是通用的、结果是实用的。     

基于Adaboost集成BP网络原煤生产成本预测研究

为了提高传统BP神经网络预测模型精度,避免BP网络容易陷入局部极值、收敛速度慢等问题,将BP神经网络与Ada-boost算法相结合,提出了一种Adaboost集成BP神经网络模型.结合磁县观台煤矿原煤生产成本相关数据,建立了原煤生产成本预测的Adaboost集成BP神经网络模型,将该模型用于实际的原煤成本预测.结果表明：该模型预测精度高于传统的BP神经网络,收敛速度快,具有较强的鲁棒性,预测精度能满足实际预测需要,为原煤生产成本预测提供了一种新的途径,也为原煤生产成本控制提供了重要依据.     

利用二维模型求解太湖水质CODMn的研究

在二维黎曼近似解模型的基础上建立了太湖水质预测模型，并运用该模型对太湖的水质指标CODMn了模拟。模拟的结果跟太湖各监测站的测量值相接近，表明该模型能较好的运用于太湖的水质预测。     

基于改进的灰色模型的成都市用水效率分析预测

针对等维动态递补灰色模型对波动性数据预测的局限性,提出基于缓冲算子的等维动态递补灰色模型,改进原模型对波动性数据预测误差较大的问题。利用成都市2008年~2013年人均用水量、万元GDP用水量、万元工业增加值用水量以及农田灌溉亩均用水量4个指标的统计数据,在验证改进模型的有效性后,分别对成都市2016年、2020年、2025年各指标耗水情况进行预测分析。结果表明,成都市用水效率总体呈现上涨的趋势,其中,工业用水效率较高,生活用水效率以及农田灌溉用水效率偏低。     

基于BP-Adaboost强预测器的瓦斯涌出量预测

为了提高传统BP神经网络瓦斯涌出量预测模型精度,避免BP网络容易陷入局部极值、收敛速度慢等问题,将BP神经网络和Adaboost算法相结合,提出了一种BP-Adaboost强预测器模型.将该模型用于实际瓦斯涌出量预测,并进行了40次仿真实验.结果表明:该模型预测精度高于传统的BP神经网络,且收敛速度快,具有较强的鲁棒性,预测精度能满足实际工程需要,为瓦斯涌出量预测提供了一种新的途径.     

EFFECT OF PRICE ON THE RESIDENTIAL DEMAND FOR WATER WITHIN AN AGENCY1

ABSTRACT: This paper develops a model that can be used to forecast the residential elasticity of demand for water within a district. Long-term water conservation programs and revenue and cost decisions hinge crucially on a determination of this elasticity. This study then pools cross-sectional (census) and time series data to generate elasticity forecasts for the Oakland urban area.     

DETERMINISTIC MODELS FOR PREDICTING RESIDENTIAL WATER CONSUMPTION1

ABSTRACT: Forecasts of future urban water demand traditionally have been made by the projection of historic trends in per capita consumption and population. This paper outlines the use of two deterministic models to forecast the residential component of urban water demand. The models incorporate specific representation of the activities which result in water consumption at each residence. Predictions of water use can then be made by modeling the changes expected in the number of these activities and the consumption for each such activity.     

基于因子分析的BP神经网络对成都市需水量的预测

在对四川省成都市的水资源进行综合规划与评价的过程中,通过主成分因子分析可知人口、GDP、给排水管道长度等因素对需水量预测有较大影响,如何建立这些因素与需水量之间的数学关系是预测工作的重点。本文将介绍通过MATLAB数学分析软件建立BP神经网络预测模型,并对模型的预测结果进行评价。     

A MONTHLY TIME SERIES MODEL OF MUNICIPAL WATER DEMAND1

A multivariate time series model is formulated to study monthly variations in municipal water demand. The left hand side variable in the multivariate regression model is municipal water demand (gallons per connection per day) and the right hand side contains (explanatory) variables which include price (constant dollars), average temperature, total precipitation, and percentage of daylight hours. The application of the regression model to Salt Lake City Water Department data produced a high multiple correlation coefficient and F-statistic. The regression coefficients for the right hand side variables all have the appropriate sign. In an ex post forecast, the model accurately predicts monthly variations in municipal water demand. The proposed monthly multivariate model is not only found useful for forecasting water demand, but also useful for predicting and studying the impact of nonstructural management decisions such as the effect of price changes, peak load pricing methods, and other water conservation programs.     

A LINEAR ANALYSIS OF AN URBAN WATER SUPPLY SYSTEM1

The use of linear programming as a planning tool for determining the optimal long-range development of an urban water supply system was explored. A stochastic trace of water demand was synthesized and used as an input to the model. This permitted evaluating the feasibility of imposing demand restrictions as an effective cost reduction mechanism. The City of Lincoln, Nebraska, was used as the urban model. The fundamental problem was to allocate limited water supplies from several sources to an urban load center to minimize costs and comply with system constraints. The study period covered twenty years, and findings indicate the planning direction for stage development during this period. Sensitivity analyses were performed on cost coefficients and demands. Thirteen sources were included in the initial computations. Conclusions were that linear programming and generated demand traces are useful tools for both short- and long-term urban water supply planning. Lowering peak demands results in long-range development of fewer sources of supply and more economic and efficient use of the supplies developed.     

Review of Urban Stormwater Quality Models: Deterministic,Stochastic, and Hybrid Approaches1

Abstract: The growing impact of urban stormwater on surface‐water quality has illuminated the need for more accurate modeling of stormwater pollution. Water quality based regulation and the movement towards integrated urban water management place a similar demand for improved stormwater quality model predictions. The physical, chemical, and biological processes that affect stormwater quality need to be better understood and simulated, while acknowledging the costs and benefits that such complex modeling entails. This paper reviews three approaches to stormwater quality modeling: deterministic, stochastic, and hybrid. Six deterministic, three stochastic, and three hybrid models are reviewed in detail. Hybrid approaches show strong potential for reducing stormwater quality model prediction error and uncertainty. Improved stormwater quality models will have wide ranging benefits for combined sewer overflow management, total maximum daily load development, best management practice design, land use change impact assessment, water quality trading, and integrated modeling.     

System dynamics modeling for municipal water demand estimation in an urban region under uncertain economic impacts

Accurate prediction of municipal water demand is critically important to water utilities in fast-growing urban regions for drinking water system planning, design, and water utility asset management. Achieving the desired prediction accuracy is challenging, however, because the forecasting model must simultaneously consider a variety of factors associated with climate changes, economic development, population growth and migration, and even consumer behavioral patterns. Traditional forecasting models such as multivariate regression and time series analysis, as well as advanced modeling techniques (e.g., expert systems and artificial neural networks), are often applied for either short- or long-term water demand projections, yet few can adequately manage the dynamics of a water supply system because of the limitations in modeling structures. Potential challenges also arise from a lack of long and continuous historical records of water demand and its dependent variables. The objectives of this study were to (1) thoroughly review water demand forecasting models over the past five decades, and (2) propose a new system dynamics model to reflect the intrinsic relationship between water demand and macroeconomic environment using out-of-sample estimation for long-term municipal water demand forecasts in a fast-growing urban region. This system dynamics model is based on a coupled modeling structure that takes into account the interactions among economic and social dimensions, offering a realistic platform for practical use. Practical implementation of this water demand forecasting tool was assessed by using a case study under the most recent alternate fluctuations of economic boom and downturn environments.     

THE ACCURACY OF WATER USE FORECASTS: EVALUATION AND IMPLICATIONS1

ABSTRACT: Forecasts of 1980 river basin water use presented in the reports of the 1960 Senate Select Committee on National Water Resources and in the Water Resources Council's First National Water Assessment of 1968 were compared to estimates of actual use in 1980 to assess the accuracy of efforts to forecast future water use. Results show that the majority of the forecasts were substantially in error. In general, the First National Assessment forecasts erred by a smaller margin, but tended to repeat the regional patterns of overestimation (underestimation) exhibited in the Senate Select Committee forecasts. Moreover, forecasts of the two groups that came within 20 percent of the 1980 withdrawals, in general were accurate, not because of superior prediction, but because of offsetting errors in forecast components. This performance leads us to conclude that water use forecasts, regardless of the time-frame or the forecast method employed, are likely to always be highly inaccurate. Accordingly, if such forecasting efforts are to be of value in contemporary water resources planning, forecasters should direct their attention toward methods which will illuminate the determinants of the demand for water.