用水量预测和水价是水资源管理的两个重要内容。
Water demand forecasting and water price are two important parts of water resources management.
通过实例证明该模型是一种行之有效的用水量预测模型。
Typical examples proved that the model is a very accurate water demand forecast model.
工业用水量预测对工业企业的规划、运行具有非常重要的作用。
The predictive amount of water is very important to the program and option of the industrial enterprise.
将偏最小二乘回归与神经网络耦合，建立了城市生活用水量预测模型。
The paper establishes the model for the urban life-water quantity prediction by means of combining neural network with the partial least squares method.
对以往用水量预测结果表明该模型有较高预测精度、通用性和客观性。
The prediction results of water demand for the previous years indicate that the model has high precision of prediction and definite versatility and objectivity.
用水量预测是水资源合理开发、管理、水污染控制及综合利用规划的基础。
Urban water consumption forecasting is the basis of the rational exploitation, management, water pollution control, integrated using and programming in water resource.
对工业用水量预测方法进行分析比较，指出了每种方法的适用条件和优缺点。
This paper analyzed and compared the method in predicting water-use quantity of industry, and put forword fitting condition of each method and merits and demerits.
首先简要分析并总结了现有灌溉用水量预测方法，指出了现有方法的局限性。
The methods used for the forecast of irrigation water use were first reviewed and the limits were analyzed.
需要说明的是：如何充实用水量预测的标准、依据是下一步研究的主要方向。
It needs to be explained that how to enrich the standards and basis of the water consumption is the main direction for the next research step.
城市生活用水量预测在城市水资源利用和节约用水规划管理中起着非常重要的作用。
The prediction of city municipal and domestic water consumption plays an important role in utilization of urban water resources.
时间序列法是用水量预测的常用方法，其中预测模型的选择是提高预测精度的关键。
The time series method is one of common methods for forecasting water consumption. The prediction accuracy on water consumption can be guaranteed by the selection of forecast models.
采用自适应变步长的后向传播算法（ABPM）构建了一个人工神经网络用水量预测模型。
The following paper constructs a artificial neural network - named water quantity predicting model, using automatically adapting and step-self-changing back propagation method(ABPM).
结果表明：分类用水量预测比总体用水量预测具有精度高，结果稳定的特点，可用于年用水量预测。
The result indicated that the classification forecast have the higher accuracy and steady than total forecast, it can used for the city. s annual water consumption forecast.
结果表明：分类用水量预测比总体用水量预测具有精度高，结果稳定的特点，可用于该市的年用水量预测。
The result indicated that the classification forecast have the higher accuracy and steady than total forecast, it can be used for this city's annual water consumption forecast.
通过分析验证的结果，证明了本文提出的城市日用水量预测模型可行，采用BP、R BF和SVM法求解方法均能得到满意的效果。
Analysis of the experimental results proved that the model of urban water consumption prediction is feasible, the BP network, the RBF network and SVM all can get the satisfied result.
利用随机过程及时间序列分析手段，根据用水量序列季节性、趋势性及随机扰动性的特点，建立了用水量预测的自适应组合平滑模型。
Based on random process theory and time series analysis, the paper advanced the adaptive combined smoothing model suiting to seasonality, trend and randomness of water consumption series.
针对近年来西安市用水量变化的特点，采用改进的GM(1, 1)模型用于用水量预测，并与传统的GM(1, 1)预测模型进行了比较。
The improved Grey System GM (1, 1) is adapted to predict water requirement of Xian City in view of its characteristics of water requirement and compared with the traditional Grey System GM (1, 1).
指出每种方法的优缺点，城市用水量预测应根据实际情况选取预测方法。以郑州市为例，选取不同的方法进行预测及分析，结果表明郑州市2020年之前的用水量呈现非线性递增的趋势。
Taking Zhengzhou as an example, choosing different methods to predict and analyze, the results indicate the increasing trend of the water consumption before 2020 in the city.
同时利用改进的季节性指数平滑法完成了对郑州市城市用水量的预测。
Using the improved seasonal exponential smoothing to finish the prediction of water consumption of Zhengzhou at the same time.
通过对城市用水量短期预测的实例研究，将改进算法与传统算法进行比较。
The improved method was compared with the traditional method in the case of short-term forecasting for urban water consumption.
分项预测了山西未来若干年的工农业及城市生活用水量，并分析了有关的计算误差。
A current computer programme will be used to predict the water consumption oF Shanxi Province on all water using issues such as agriculture, industry and daily living in the future years.
应用灰色理论建立了GM(1,1)模型，对华北地区某典型区农业用水量进行了预测。
This article applies a GM (1, 1) model of Grey System Theory established to forecasts the agricultural water demand of an area in northern China.
本文应用多元回归的方法，对城市用水量进行预测。
This article applies the multiplelinearregression to forecast city water consumption.
灌溉用水量的预测影响因素很多，属于灰色系统问题。
The forecasting of irrigation water use is influenced by many factors and it belongs to the gray system.
城市用水量需求预测常见的方法有经验预测法、统计分析法、规划估算法、灰色预测法。
Common methods of urban water needs prediction are experience prediction, statistic analysis, planning estimate, and grey prediction.
对该系列近5年的用水量进行了预测，预测误差也以幂函数最小。
The series amount of water data in the nearly 5 years was predicted. Power function has the minimum prediction error.
对该系列近5年的用水量进行了预测，预测误差也以幂函数最小。
The series amount of water data in the nearly 5 years was predicted. Power function has the minimum prediction error.
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