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在气候场景下通过机器学习技术预测孟加拉国沿海侵蚀易感性

Sakib Hosan¹, Sondipon Dey Pranta¹, Tahdia Tahmid¹

¹Department of Urban and Regional Planning, Khulna University of Engineering & Technology (KUET), Khulna, Bangladesh.

|November 5, 2025

概括

预计孟加拉国的海岸侵蚀将随着气温的上升而恶化. 机器学习模型确定了关键的风险因素,XGBoost在预测脆弱区域方面表现出高准确度. 未来的预测表明,到2100年,高风险区域将增加.

科学领域:

环境科学环境科学
地理空间分析是什么
气候变化建模模型

背景情况:

沿海侵蚀对孟加拉国低的沿海地区构成重大威胁.
了解导致侵蚀的因素对于有效的沿海管理和适应气候变化至关重要.

研究的目的:

通过先进的机器学习和地理空间数据,确定孟加拉国容易受到沿海侵蚀的地区.
评估气候变化,特别是气温上升对未来沿海侵蚀风险的影响.

主要方法:

采用先进的机器学习技术,包括XGBoost,LightGBM和Random Forest等组合方法.
利用了地理空间数据和气候估计,考虑了20个变量 (气象,地理,水文,土地利用).
使用曲线下面面积 (AUC) 和接收器操作特征 (ROC) 值验证模型性能,XGBoost实现了0.95.95的AUC.

主要成果:

确定了规范差异植被指数 (NDVI) 作为影响沿海侵蚀的最关键因素.
大多数沿海地区被归类为中等风险 (71.82%79.36%),博拉,考克斯巴扎尔和帕图哈利被确定为高风险地区.
根据RCP 8.5的未来预测,到2080年和2100年,高风险地区的数量将大幅增加.

结论:

机器学习模型有效地预测了孟加拉国的沿海侵蚀易感性.
预计全球气温上升将加剧沿海侵蚀,迫切需要采取适应战略.
综合沿海区管理和适应气候变化的规划对于减轻未来侵蚀影响至关重要.

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