1·The forecast study about the rising water demand.
用水需求增长变化及预测研究。
2·The water demand of ecological environment is classified.
对生态环境需水进行了分类；
3·WaterOverview: Managing Water demand is key to reducing consumption.
用水概述：管理用水的需求对降低能耗非常关键。
4·Avoid over-fertilizing your lawn, as it will lead to an increase in water demand.
避免给你的草坪过度施肥，因为这将导致用水量的增加。
5·Typical examples proved that the model is a very accurate water demand forecast model.
通过实例证明该模型是一种行之有效的用水量预测模型。
6·In this paper, the environmental water demand in the upstream of Hanjiang River is studied.
对汉江上游的生态环境需水量进行了初步探讨。
7·Water demand forecasting and water price are two important parts of water resources management.
用水量预测和水价是水资源管理的两个重要内容。
8·With the increase of underground water demand, the water right conflicts have gradually increased.
随着对地下水资源需求的增多，地下水水权冲突现象也日趋严重。
9·According to the analysis of the actual situation in Qingdao, its water demand quota is determined.
对青岛市实际情况进行分析，确定其未来的需水定额；
10·Municipal water demand prediction was a complex system predictive issue including a good many of factors.
城市用水需求预测是涉及到诸多要素的复杂系统预测问题。
1·Taking Qingdao as the research object, the forecast research on its industrial water demand is conducted, the method of prediction USES the grey prediction model.
以青岛市为研究对象，对其工业需水量进行了预测研究，预测方法采用灰色预测理论。
2·Effects of quality index, which are volcanic ash activity, loss on ignition, fineness and water demand ratio, on quality of fly ash are expatiated, the requests to quality of fly ash are put forward.
阐述了火山灰活性、烧失量、细度和需水量比等品质指标对粉煤灰质量的影响，对粉煤灰品质提出了要求。
3·The result shows that application of system dynamics model to predict urban water demand has the advantages of strong systematicness and high accuracy of prediction result.
结果表明，应用系统动力学模型预测城市需水量，系统性强，预测结果准确度高。
4·The industrial water demand of Qingdao in the future years is obtained by using the quota method.
采用定额法得出青岛市未来年份的工业需水量。
5·The forecasting results of the case study has proved that the adaptive control exponential smoothing forecasting model suits water demand forecast in irrigation districts.
实例预报结果表明，把自适应指数平滑预报模型应用于灌区需水量预报中是可行的。