アフリカの都市における都市熱島強度の予測のためのハイブリッドベイジアンディープラーニングモデル
D Lynda1, G Logeswari2, K Tamilarasi1
1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, 600127, India.
Scientific reports
|August 25, 2025
まとめ
新しいハイブリッドAIモデルにより,アフリカの地表都市熱島 (SUHI) の強度が正確に予測され,気候変動への適応が促進されます. このツールは都市計画者が 急速に成長する都市で 熱のリスクを軽減し 回復力を高めるのに役立ちます
科学分野:
- 環境科学
- 気候科学
- 都市計画
背景:
- 都市熱島 (UHI) は,エネルギー需要,空気質の問題,公衆衛生上のリスク,特に急速に都市化しているアフリカの地域を悪化させます.
- アフリカでは 気候に適応できるインフラが 限られているため 都市部の人々が 暑さストレスに 弱い状況に なっています
研究 の 目的:
- アフリカ全土で地表都市熱島 (SUHI) の強度を予測するためのハイブリッド予測モデルを開発し,検証する.
- 都市計画者たちに,急速に成長するアフリカの都市における熱負荷を軽減するためのツールを提供すること.
主な方法:
- ベイジアン・ニューラル・ネットワーク (BNNs) と注意力メカニズムとグラデント・ブースト・リグレッサー (GBRs) を組み合わせたハイブリッド・アンサンブルモデル.
- 土地の表面温度,土地使用,人口密度を含む1万以上の都市からのグローバル気候と都市データセットを使用しました.
- モデルの検証に 10 回のクロス・バリデーションを使用した.
主要な成果:
- ハイブリッドモデルは,構造化された気候データに対して従来のディープラーニング方法 (CNN,LSTM) よりも優れたパフォーマンスを示しました.
- 平均絶対誤差 (MAE) は1.47°C,平均二乗誤差 (MSE) は3.56°C2で,R2スコアは0.84でした.
- 郊外でのモデルの精度が12.3%向上し,ベースラインアーキテクチャと比較してMAEが8.6%減少しました.
結論:
- 提案された不確実性認識と解釈可能な機械学習モデルは,気候適応戦略に価値があります.
- このモデルは,アフリカでより住みやすい,気候に耐える都市を建設するための都市計画活動を支援します.
- 都市化が公衆衛生やインフラに及ぼす熱影響の管理には,SUHIの効率的な予測が不可欠です.
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