用人工智能预测和分析五种地形类型中的大气可见度
Chen-Wei Liang1,2, Chia-Chun Chang3, Chun-Yun Hsiao3
1Department of Biomechatronic Engineering, National Ilan University, Yilan, Taiwan.
Heliyon
|September 4, 2023
概括
这项研究开发了人工智能模型,使用空气污染和天气数据准确预测大气可见性. 调整后的AI预测显示,在不同地形地区具有很高的可靠性,改善了可见性预测.
科学领域:
- 环境科学 环境科学
- 大气科学 大气科学
- 人工智能的人工智能
背景情况:
- 由于灭绝系数的指数衰减 (科斯密德定律),准确地测量大气可见度具有挑战性.
- 现有的可见度预测模型由于缺乏准确的观测数据来验证而受到限制.
- 地形变化显著影响大气可见性.
研究的目的:
- 制定一种人工智能 (AI) 方法,用于在五个不同的地形区域测量大气可见性.
- 评估支持矢量机 (SVM),多层感知器 (MLP) 和极端梯度增强 (XGBoost) 模型在预测可见性方面的性能.
- 为特定的地形和区域条件量身定制的可见度预测确定最佳的AI模型.
主要方法:
- 选择了四种空气污染和五种气象因素作为AI模型的独立变量.
- 采用SVM,MLP和XGBoost模型,并使用GridSearchCV进行超参数优化.
- 应用尺度比和下移公式来调整人工智能模型预测以提高准确性.
主要成果:
- 最初的AI模型预测显示,与观察到的值相比,规模缩小,可能是由于训练数据的限制.
- 使用尺度比率和下移调整后的预测显示,在所有五个地区,与观测数据有很强的一致性.
- 模型性能因地区而异:MLP在台湾西部的山丘中脱而出,XGBoost在台湾西部的盆地/平原,SVM在台湾东部的流平原/裂谷中.
结论:
- 调整后的AI预测方法为大气可见度预测提供了高可靠性.
- 最佳可见度预测需要根据特定的区域地形,天气和空气质量选择最合适的AI模型.
- 这些发现可以增强决策和改善局部可见性预测.
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