In the first chapter, methods of water demand prediction and questions and causations of water demand prediction are introduced. Important factors related to water demand are found by analyzing our country’s water consumption datas.
第一章介绍了需水量预测方法和需水预测中存在的问题和原因，对我国历年来用水量数据进行了分析，找出了与用水量相关的主要因素。
参考来源 - 基于人工神经网络需水量预测方法的综合评价与分析The scientific water demand forecast is an important basis for water resources planning and water supply project construction.
科学的需水预测是水资源规划和供水工程建设的重要依据。
参考来源 - 人均综合用水量方法预测需水量—观察未来社会用水的有效途径·2,447,543篇论文数据，部分数据来源于NoteExpress
其中需水预测最终为多维临界调控模型提供数据基础和边界条件；
Thereinto forecasting of water requirement is the datum basic and critical condition of multi-dimension critical adjustment model;
预测结果表明，径向基函数神经网络需水预测模型运算速度快，有较高的预测精度。
Abundant water demand predicting factors were used as the input data of the model, and the RBF neural network output the water demand predicting values.
但由于问题本身的复杂性、影响因素的不确切性、特别是预测方法的局限性，使得目前需水预测误差较大。
But the complexity of the problem, the indefinite of the factors and especially the limits of the predicting method made large error between the prediction value and the reality.
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