water demand forecast

基本解釋

Typical examples proved that the model is a very accurate water demand forecast model.
通過實例證明該模型是一種行之有效的用水量預測模型。
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，與基于BP網(wǎng)絡的時用水量模型相比，基于最小二乘支持向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(shù)平滑預報模型應用于灌區(qū)需水量預報中是可行的。