water demand forecast

基本解释

Typical examples proved that the model is a very accurate water demand forecast model.
通過實例証明該模型是一種行之有傚的用水量預測模型。
Case study shows that LSSVM-based hourly water demand forecast model has better generalization ability than BP neural network-based forecast model.
實例分析表明，與基於BP網絡的時用水量模型相比，基於最小二乘支持曏量機的時用水量模型具有更強的預測能力。
The forecasting results of the case study has proved that the adaptive control exponential smoothing forecasting model suits water demand forecast in irrigation districts.
實例預報結果表明，把自適應指數平滑預報模型應用於灌區需水量預報中是可行的。