water demand forecast

基本解释

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。
Case study shows that LSSVM-based hourly water demand forecast model has better generalization ability than BP neural network-based forecast model.
实例分析表明，与基于BP网络的时用水量模型相比，基于最小二乘支持向量机的时用水量模型具有更强的预测能力。
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。