人工ニューラルネットワークと多重線形回帰を比較して,米のカドミウム濃度を予測する:中国の広西でのフィールド研究
Junyang Zhao1, Fuhai Zheng2, Baoshan Yu1
1Guangxi Key Laboratory of Agro-Environment and Agric-Products Safety, College of Agriculture, Guangxi University, Nanning 530004, China.
Toxics
|August 27, 2025
まとめ
米中のカドミウム (Cd) を予測する2つのモデルを開発しました バックプロパガンダ人工ニューラルネットワーク (BP-ANN) モデルは,米のカドミウム含有量を予測する多重線形回帰 (MLR) モデルよりも優れた性能を示した.
科学分野:
- 環境科学
- 農業科学
- 土壌科学
背景:
- 土壌と米のシステムにおけるカドミウム (Cd) の転移は複雑で,現在の土壌と植物のモデルの有効性を制限しています.
- 米のCd含有量の正確な予測は,食品安全とリスク評価に不可欠です.
研究 の 目的:
- 米粒のカドミウム含有量を推定するために,逆伝播人工ニューラルネットワーク (BP-ANN) モデルと多重線形回帰 (MLR) モデルの予測性能を比較する.
- 米のカドミウム蓄積に影響を与える土壌の主要パラメータを特定する.
主な方法:
- 訓練と検証のために486個のペアされた土壌と米粒のサンプルを使用し,さらに30個のサンプルを広西省からテストするために使用しました.
- 土壌利用可能なカドミウム (ACd),土壌全体のカドミウム (TCd),土壌の有機物質 (SOM) およびpHを予測変数として使用した.
- ルート・ミーン・スクエア・エラー (RMSE),相対パーセント差 (RPD),および相関係数 (R2) を用いてモデルのパフォーマンスを評価した.
主要な成果:
- pH,TCd,ACdを使用したMLRモデルは,米カドミウム (RCd) のR2,RPD>2.398,RMSE>0.049を達成した.
- BP-ANNモデルは,同じ変数を用いて,RCdのR2とRMSEの0.104を得ました.
- BP-ANNモデルは,MLRモデルと比較して優れた予測精度を示した.
結論:
- バックプロパガンダ人工ニューラルネットワーク (BP-ANN) モデルは,米のカドミウム含有量を予測するのに有効です.
- BP-ANNは,土壌-米系におけるカドミウム予測のための従来の多重線形回帰 (MLR) よりも優れた性能を提供します.
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