時系列におけるラドン変動の予測モデリング ウェーブレット,多重線形回帰,ARIMA
Nadeem Bashir1, Awais Rasheed1, Muhammad Osama2
1Department of Physics, King Abdullah Campus, University of Azad Jammu and Kashmir, Muzaffarabad, Pakistan.
Isotopes in environmental and health studies
|August 28, 2025
まとめ
ラドンガスの異常は 地震前に発生する ARIMAのような高度なモデルは ラドン濃度を効果的に予測し,災害リスク削減と環境衛生研究に役立ちます.
科学分野:
- 地理学
- 環境科学
- 地震学
背景:
- ラドン (222Rn) は,ウランの腐敗から自然に発生する放射性ガスである.
- ラドンは地質学的なトレーサーとして使用され,その時間系データ (RTS) は地震の発生を示します.
- 地質学と地震学の研究において,ラドンと気候学的要因の振る舞いを理解することは極めて重要です.
研究 の 目的:
- 気候的要因 (温度,圧力,湿度) と並行して複雑なラドン時系列 (RTS) データを分析する.
- シミュレーション技術を使用してRTSデータから有意義な物理情報を抽出します.
- ラドン濃度と地震の前駆体としての可能性を予測するための最適なモデルを特定する.
主な方法:
- 温度,圧力,湿度でラドンの振る舞いを分析するための波紋ベースの回帰 (WBR).
- ラドンと気象学的要因の関係を評価する多重線形回帰 (MLR)
- AICとBICを使用して最適化されたタイムシリーズ分析,パターン識別および予測のための自動回帰集積移平均 (ARIMA) モデル.
主要な成果:
- ラドンは温度と線形な振る舞いをし,圧力と湿度との非線形な振る舞いをWBRによって示します.
- MLRはラドン,圧力,湿度の間の正の関係を確認しています.
- RTSデータにおける異常は地震発生前に観察された.
- ARIMAモデルは長期間ラドン濃度を予測する上で優れた性能を示した.
結論:
- 気候学的な要因の影響を受けたラドン時系列データは,地震の前駆体としての可能性を示している.
- アリマモデルはラドン濃度を予測するのに非常に有効で,災害リスクの軽減に貢献しています.
- この研究は,環境衛生,インフラ安全,災害の回復力に関する国連の持続可能な開発目標を支援します.
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