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Updated: Feb 22, 2026

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

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波を起こす: 自動相関とベースラインモデルの見過ごされた役割を通して,廃水流水質の予測における機械学習を再考する

Yijie Wang1, Damien Batstone2, Zhenju Sun3

  • 1School of Civil and Environmental Engineering, Nanyang Technological University, 50 Nanyang Avenue 639798, Singapore; Nanyang Environment & Water Research Institute, Nanyang Technological University, 1 Cleantech Loop 637141, Singapore.

Water research
|February 20, 2026
PubMed
まとめ

排水処理所の排水質の予測のための機械学習 (ML) モデルは,標的の自己相関性により,精度を過大評価することがあります. 持続性モデルは,特に低揮発性条件では,しばしばMLを上回り,より良いベースラインの必要性を強調します.

キーワード:
自動相関は,自己相関である.ベンチマーキング (Benchmarking) とは機械学習 (Machine Learning) とは,機械学習 (Machine Learning) というものです.持続性モデル (persistence model) とは,持続性モデル (persistence model) とは,持続性モデル (persistence model) とは,持続性モデル (persistence model) とは,持続性モデル (persistence model) とは,持続性モデル (persistence model) とは,持続性のモデル (persistence model) とは,持続性のモデル (persistence model) とは,持続性のモデル (persistence model) とは,持続性のモデル (persistence model) とは,持続性のモデル (persistence model) とは,持続性のモデル (persistence model) とは,タイムシリーズのモデルモデル排水処理施設は,排水処理施設として利用できます.

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