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daily water中文是什么意思

  • 日用水
  • 洗涤水

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  • 例句与用法
  • There are rich fresh water resources in dinghu , the waterworks with a daily water supply of 50000 tons , the jiukenghe reservoir with a water storage of 49600000 cubic meter , and the never ending xijiang river provide richwater resources to the district
    鼎湖区拥有丰富的淡水资源,有日供水量5万吨的自来水厂,还有储水量达4960万立方米的中型九坑河水库及源源不断的西江水域,为我区提供充足的水源供应。
  • With its application in daily water demand forecast , daily water demand forecast is separated into domestic water consumption , industrial water consumption , commercial water consumption and common water consumption . it proved that the forecasting precision has been more accurate in a certain extent
    并通过对日需水量按照生活、工业、商业和公共用水分别进行预测,结果证明分类预测能使预测精度在一定程度上得到改善。
  • Based on the data of daily water withdrawal , rainfall and evaporation in 14 experimental paddy fields , which were varied in landform , soil type , drainage system , growth rule , and irrigation mode , with typical investigation , and analysis in the influence factors on water use , the research of water use regulation in paddy fields and its influence on water resources distribution was done . at the same time , the mathematical relation between the total water consumption and the growth days of paddy was founded
    以2001年为基准年,通过对杭州市不同地形、不同土壤、不同渠系、不同种植制度、不同灌溉方式的典型水稻田灌水的逐日记录,结合降雨、蒸发监测值,实测了不同水稻田的灌溉水量及水资源使用量,并结合典型调查,分析了影响本地水稻用水的各因素的影响程度,研究了本地水稻的用水规律对本地水资源配置的影响。
  • On the basis of analyzing historical water consumption in shenzhen , hourly water demand , daily water demand and annual water demand are studied using non - linear regression model , time series model , artificial neural network , gray model and compounding model , etc . by anglicizing merits and demerits of every model in different forecasts , time series model is appropriate to hourly water demand forecast ; compound forecasting model of time series and regress analysis is appropriate to daily water demand forecast ; gray model and regress analysis model is appropriate to annual water demand forecast
    本文通过分析深圳特区用水量的变化规律,采用非线性回归分析、时间序列、人工神经网络、灰色模型和组合预测模型分别对时需水量、日需水量、年需水量进行了研究。通过比较分析各种模型在不同预测类型中的优缺点,时需水量预测较适合采用时间序列模型;日需水量预测较适合采用时序?回归分析组合预测模型;年需水量预测较适合灰色模型、回归分析模型;提出了指导选择城市需水量预测模型的方法。
  • Urban water demand forecasting can be assorted into annual water demand forecasting and hourly water demand forecasting , daily water demand forecasting . they are efficient means of programming and managing of urban water resource , and they are important portion of optimizing dispatching management of water supply system
    城市需水量预测可分为中长期的年需水量预测以及短期的时需水量预测、日需水量预测,它们是城市进行水资源规划和管理的有效手段,也是供水系统优化调度管理的重要部分。
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