基于集对分析原理的城市需水量预测模型及其应用

城市需水量预测对合理分配水资源起着重要作用,但城市需水量本身的影响因素众多,其预测是典型的不确定性问题。集对分析是处理不确定问题的新方法,它通过联系度展示了研究对象间关系的详细结构,对处理不确定性问题十分有效。采用集对分析相似预测法,结合集对分析同异反模式中的"择近原则"和相似分析法中相似的历史原因产生相似结果的原则,在预测过程中将需水量时间序列作为需水量的影响因子,利用一定时长的历史数据来建立基于集对分析原理的相似预测法模型。通过对克拉玛依市三坪地区需水量的预测发现,集对分析相似预测法不但计算简单,而且精度优于传统的ARIMA模型。     

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。     

基于遥感技术的艾比湖储水量和需水量研究

艾比湖湖水很浅,湖底平坦,沉积着巨厚的细沙和淤泥。依据自身特征及其他因素,针对艾比湖水量的收支情况,建立艾比湖的储水量和需水量的数学模型。其中艾比湖湖面面积数据是重要参数之一,利用遥感技术,采用资源卫星影像,结合Modis数据,解译提取获得。通过艾比湖储水量和需水量数据可以进一步预测艾比湖湖面面积的变化趋势,对本地区的生态及农业生产具有指征意义,为决策层制定防治对策提供科学依据。     

秦皇岛市工业企业需水量零增长预期分析

本文在对“九五”期间秦皇岛市工业用水情况认真分析的基础上,按照秦皇岛市“十五”计划和2010年远景目标对秦皇岛市2005年和2010年未来工业企业需水量进行了预测;同时对2020年和2030年进行了展望,综合分析预测的结果,提出了节水措施的建议,并对工业需水量的零增长进行了预期分析。     

复杂下垫面条件下冬小麦干旱机制辨析研究进展

对冬小麦干旱机制辨析研究目前主要有:冬小麦干旱灾害时空特征研究、冬小麦生长需水量研究、冬小麦生长有效降水量研究、冬小麦干旱过程的缺水环节和干旱形成机制研究、冬小麦干旱灾害风险研究。为了进一步开展冬小麦干旱研究,特别是冬小麦干旱形成机制,亟需加强的方面:一是细化需水量和有效降水量的空间格局研究,可尝试以田间试验数据为基础,综合考虑气候、地形地貌、土壤特性、植被等格网化的下垫面因素,研发中观尺度冬小麦需水量、有效降水量的新模式,同时借助格网概念,尝试将冬小麦干旱精准定位于空间格网上,为冬小麦干旱防灾减灾等提供技术支撑;二是确定冬小麦干旱的缺水环节,研究掌握冬小麦的干旱机制,可从"气候—土壤—冬小麦"系统角度出发构建缺水量空间分布模型。结合气候模式数据,对冬小麦缺水量进行深入的研究并探讨冬小麦干旱形成机制,开展不同气候变化情景下冬小麦需水量和缺水量的时空变异及模拟预测研究,识别冬小麦干旱时空演变特征,研判冬小麦干旱风险,为保障冬小麦生产提供理论依据。     

秦皇岛市居民生活和公用事业用水量预测

本文在对“九五”期间秦皇岛市城市生活、公用事业水情况认真分析的基础上,按秦皇岛市“十五”计划和2010年远景目标对秦皇岛市2005年和2010年未来城市生活需水量和城市公用事业用水量进行了预测;同时对2020年和2030年进行了展望;综合分析预测的结果,提出了节水的建议,并对需水量的零增长期进行了预期分析。本文的研究结果对秦皇岛市水资源可持续利用及国民经济和社会发展规划的制订有积极的意义。     

天堂河河道治理工程水质改善预测分析

河道治理工程重在改善河道的水质，以恢复河流生态系统。本文以北京市大兴区天堂河河道治理工程为例，介绍了河道治理工程环境影响评价中的水质改善预测分析，首先分析生态需水量和供氧量是否满足河流生态需水要求，后又利用地面水环评助手软件预测了河流水质，预测分析了天堂河水质目标的可达性，旨在为以后的河道治理类工程环境影响评价提供参考。     

成都市天府新区生态环境需水量预测

将以成都市天府新区生态环境需水量预测为例,研究基于城市新区规划发展目标、确保水质达标情况下的生态环境用水需求。成都市天府新区生态环境需水量主要是维持河流稀释净化能力需水,经预测,近期2020年为14.61亿m3/年、远期2030年为18.58亿m3/年。要保障如此大的生态环境需水量,必须采取控制发展规模、提高用水效率、节水降耗,加快污水设施建设、提高排水标准,加快再生水和雨水综合利用,优化区域水资源配置,实施"引青济岷"跨流域调水等措施。     

多元线性回归和Bp神经网络预测水资源承载力--以济南市为例

水资源承载力的预测对发展地区经济具有重要意义，利用主成分分析方法对济南市水资源承载力变化的驱动力进行了分析，人口和GDP是影响水资源承载力变化的最主要的驱动因素。通过水资源承载变化驱动因子的多元线性回归模型和人工神经网络模型，分别预测出2010年和2020年济南市水资源的需求状况，并探讨了将线性和非线性相结合的方法用于水资源预测。     

安徽省耕地资源动态变化及驱动机制研究

根据安徽省多年的统计资料，运用主成分分析法研究耕地的动态变化特点及动力机制。结果表明，影响安徽省耕地变化的因素有两个方面：人口社会经济因素和土地生产力要素。并在此基础上，建立多元线性回归模型，对安徽省耕地变化的趋势进行科学的预测。     

PREDICTIVE ASSESSMENT OF SEVERITY OF AGRICULTURAL DROUGHTS BASED ON AGRO-CLIMATIC FACTORS1

ABSTRACT: Past historical evidence indicates that droughts have had great impacts on human life. Drought (or scarcity of water) is assessed based on two key factors, namely, the estimated water demand, and the expected water supply. The formulation of these key factors for a region largely depends on the agro-climatic and economic conditions. Consideration of one such key factor is the relationship between the crop yield and water deficit in the assessment and prediction of agricultural droughts. The varying nature of this relationship from crop to crop adds to the complexity of agricultural drought analysis. To overcome this difficulty in analyzing agricultural droughts of a region, it is adequate to consider and place emphasis on a single crop (i.e., an index crop) grown homogeneously over the major area of the region. From one year to another year, the pattern of water requirement during the growing season of an index crop is rather stationary, and the water supply in arid and semi-arid area is mainly from seasonal random precipitation. In a region, grain yield of the index crop and, in turn, assessment of the severity of drought can reasonably be predicted as a function of the time of crop sowing and the distribution of rainfall, provided that temporal and spatial effects of other contributing factors (crop variety, soil fertility status, crop disease, pest control, cultivation practices etc.) on grain yield are considered to be uniformly distributed (i.e., stable). A predictive method of assessing agricultural droughts in an arid area of western India is presented. The major crop (Pearl Millet) of this region is grown from. July through September. The formulation of the proposed predictive method inherently implies that the grain yield of the main crop is a reliable indicator of agricultural drought. In the development of this predictive relationship (i.e., a regression type model) a number of potential yet simple variables affecting the grain yield in the region were investigated. The soil moisture index, although generally considered significant compared to the simple variables, has been found to account for insignificant variation in the grain yield. Results of our investigations suggest that it would be advisable to exclude the soil moisture index variable from the model. The proposed regression model can be used in the prediction of grain yield of the main crop several months ahead of crop harvesting operations and, in turn, the assessment of agricultural drought severity as mild, moderate, or severe. Such an assessment is expected to be helpful to planners for arranging appropriate measures to effectively combat agricultural drought situations.     

人工神经网络法预测城市用水量

城市用水量的预测结果,对于城市规划、供水系统的管理及改扩建有着重要的意义,寻求科学合理的预测模型是保障预测结果准确可靠的关键。针对这一问题,利用人工神经网络理论建立了BP（Back—Propagation,反向传播算法）网络预测模型,该模型考虑了反映社会、经济的两个影响因素人口和工业产值对用水量需求的影响,具备系统决策功能。通过实例证明该模型是一种行之有效的用水量预测模型。     

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.     

考虑“水—土—能—碳”关联的我国工农业碳排放效率及减排潜力研究

考虑“水—土—能—碳”关联，本文将水土资源要素纳入投入变量，构建了我国工农业碳排放效率投入产出测度指标，运用考虑非期望产出的SBM-undesirable模型计算我国29个省份2004—2017年农业、工业部门碳排放效率，利用乘法逆转法计算碳减排潜力并对影响碳排放的投入产出因素进行分析。结果表明：研究期内我国整体农业、工业碳排放效率均呈波动下降趋势，各年的农业碳排放效率均高于工业碳排放效率，江苏、山东等7省份农业碳排放效率以及北京、天津工业碳排放效率最优；各省份农业、工业减排潜力和规模具有显著差异，山西、甘肃的农业、工业碳减排均具有较大潜力；我国绝大多数省份均存在农业、工业的资源能源投入冗余和非期望产出冗余，土地资源投入过剩是影响农业碳排放效率的最重要因素，水资源投入过剩是影响工业碳排放效率的最重要因素。碳排放效率较低省份应积极开展技术创新，发展低碳技术，提高水土资源和能源利用效率，减少碳排放。     

贵港市土地利用动态变化分析及用地预测

以城市为中心的区域土地是土地资源中资产效益最高的一部分，是人类利用土地影响最为深刻的土地类型。在分析土地利用现状变更调查数据的基础上，应用各类土地利用动态变化模型对贵港市的土地利用动态变化进行分析，并运用灰色系统预测模型对该区域各地类的土地利用时空演变进行了预测，最后提出相应的优化土地利用结构的建议。     

Quantifying the influence of environmental and water conservation attitudes on household end use water consumption

Within the research field of urban water demand management, understanding the link between environmental and water conservation attitudes and observed end use water consumption has been limited. Through a mixed method research design incorporating field-based smart metering technology and questionnaire surveys, this paper reveals the relationship between environmental and water conservation attitudes and a domestic water end use break down for 132 detached households located in Gold Coast city, Australia. Using confirmatory factor analysis, attitudinal factors were developed and refined; households were then categorised based on these factors through cluster analysis technique. Results indicated that residents with very positive environmental and water conservation attitudes consumed significantly less water in total and across the behaviourally influenced end uses of shower, clothes washer, irrigation and tap, than those with moderately positive attitudinal concern. The paper concluded with implications for urban water demand management planning, policy and practice.     

SIMULATION OF URBAN NON-POINT SOURCE POLLUTION1

ABSTRACT: Public Law 92–00 has mandated the need for evaluating the impact of nonpoint source pollution on receiving water quality, primarily through Section 208 Areawide Planning. The Management of Urban Non-Point Pollution (MUNP) model was developed to estimate the accumulation of eight non-point pollutants on urban streets, their removal by both rainfall and street sweeping operations. The model can simulate the following pollutants: total solids or sediment-like material, volatile solids, five-day biochemical oxygen demand, chemical oxygen demand, Kjeldahl nitrogen, nitrates, phosphates, and total heavy metals. The simulated results can be used for investigation of non-point pollution management alternatives. The model is capable of reflecting variation in such diverse factors as physical and chemical characteristics of accumulated pollutants, land use characteristics, rainfall characteristics, street sweeper characteristics, roadway characteristics, and traffic conditions. By using mean estimates of many input variables for large segments of a city, the MUNP model could be used to quickly assess the magnitude of pollutants annually entering receiving waterways due to nonpoint source pollution alone. If the results indicate that non-point pollution loadings are sizeable and require futher analysis, the MUNP model could be used to define the specific nonpoint source pollution areas within a city. Hypothetical locations and actual rainfall data for Washigton D.C. were used to demonstrate some capabilities of the MUNP model.     

滇池水污染物浓度预测的人工神经网络模型

本文针对滇池日益严重的水污染现状,根据云南昆明西苑隧道断面2004年-2010年的监测资料,建立了基于BP神经网络的主要污染指标预测模型,并对其进行训练检验,研究结果表明：独立样本中pH、溶解氧、氨氮、高锰酸盐浓度的预测值与监测值的线性相关系数分别为0．952、0．967、0．945、0．936。结果证明该模型预测精度满足要求,通过准确地预测湖泊水污染物可以为治理湖泊营养化和综合利用水资源、规划管理、决策提供重要的科学依据。     

CLIMATE CHANGE IMPACTS ON WATER RESOURCES OF THE TSENGWEN CREEK WATERSHED IN TAIWAN1

ABSTRACT: This study presents a methodology to evaluate the vulnerability of water resources in the Tsengwen creek watershed, Taiwan. Tsengwen reservoir, located in the Tsengwen creek watershed, is a multipurpose reservoir with a primary function to supply water for the ChiaNan Irrigation District. A simulation procedure was developed to evaluate the impacts of climate change on the water resources system. The simulation procedure includes a streamflow model, a weather generation model, a sequent peak algorithm, and a risk assessment process. Three climate change scenarios were constructed based on the predictions of three General Circulation Models (CCCM, GFDL, and GISS). The impacts of climate change on streamflows were simulated, and, for each climate change scenario, the agricultural water demand was adjusted based on the change of potential evapotranspiration. Simulation results indicated that the climate change may increase the annual and seasonal streamflows in the Tsengwen creek watershed. The increase in streamflows during wet periods may result in serious flooding. In addition, despite the increase in streamflows, the risk of water deficit may still increase from between 4 and 7 percent to between 7 and 13 percent due to higher agricultural water demand. The simulation results suggest that the reservoir capacity may need to be expanded. In response to the climate change, four strategies are suggested: (1) strengthen flood mitigation measures, (2) enhance drought protection strategies, (3) develop new water resources technology, and (4) educate the public.     

Modeling biological oxygen demand of the Melen River in Turkey using an artificial neural network technique

Artificial neural networks (ANNs) are being used increasingly to predict and forecast water resources' variables. The feed-forward neural network modeling technique is the most widely used ANN type in water resources applications. The main purpose of the study is to investigate the abilities of an artificial neural networks' (ANNs) model to improve the accuracy of the biological oxygen demand (BOD) estimation. Many of the water quality variables (chemical oxygen demand, temperature, dissolved oxygen, water flow, chlorophyll a and nutrients, ammonia, nitrite, nitrate) that affect biological oxygen demand concentrations were collected at 11 sampling sites in the Melen River Basin during 2001-2002. To develop an ANN model for estimating BOD, the available data set was partitioned into a training set and a test set according to station. In order to reach an optimum amount of hidden layer nodes, nodes 2, 3, 5, 10 were tested. Within this range, the ANN architecture having 8 inputs and 1 hidden layer with 3 nodes gives the best choice. Comparison of results reveals that the ANN model gives reasonable estimates for the BOD prediction.