A hybrid machine learning approach to unravel monsoon variability and meteorological dynamics of Pakistan's 2010 and 2022 historic floods

IF 4.7 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies Pub Date : 2025-06-07 DOI:10.1016/j.ejrh.2025.102505

Sana Nazli , Jiahong Liu , Tianxu Song , Shan-e-hyder Soomro , Haibin Wang

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引用次数: 0

Abstract

Study region

Pakistan.

Study focus

While large-scale climatic conditions have been studied, localized meteorological dynamics and their role in flooding are not fully understood. This study examined the factors associated with the flood events of 2010 and 2022. The research employed Random Forest Regression (RFR) to evaluate how key climate variables influence different pressure levels, achieving notable predictive accuracy for both years (2010: R² = 0.72, MAE = 0.29, RMSE = 0.41; 2022: R² = 0.68, MAE = 0.24, RMSE = 0.36). Principal Component Analysis (PCA) was employed to simplify the data and highlight important components to enhance the understanding of atmospheric variability. Subsequently, K-means clustering classified monsoon regimes into Active and Break phases, assisting in recognizing time-based patterns in the monsoon cycle.

New hydrological insights for the region

The study presented new perspectives on the localized atmospheric dynamics affecting flood events, even under comparable large-scale climatic conditions. It highlighted the influence of specific meteorological variables such as moisture flux and jet stream patterns. These insights are crucial for refining flood forecasting models, improving regional flood management practices, and providing actionable information to policymakers regarding climate change.

查看原文本刊更多论文

一种混合机器学习方法来揭示巴基斯坦2010年和2022年历史性洪水的季风变化和气象动态

研究regionPakistan。虽然已经研究了大尺度气候条件，但局部气象动力学及其在洪水中的作用尚未完全了解。本研究考察了与2010年和2022年洪水事件相关的因素。研究采用随机森林回归（RFR）来评估关键气候变量对不同压力水平的影响，两年的预测精度均显著(2010年：R²= 0.72,MAE = 0.29, RMSE = 0.41；2022年：r²= 0.68,mae = 0.24, rmse = 0.36)。采用主成分分析（PCA）对数据进行简化，突出重要成分，提高对大气变率的认识。随后，K-means聚类将季风状态分为活跃阶段和中断阶段，有助于识别季风周期中基于时间的模式。该研究为影响洪水事件的局部大气动力学提供了新的视角，即使在可比的大尺度气候条件下也是如此。它强调了特定气象变量的影响，如湿度通量和急流模式。这些见解对于完善洪水预报模型、改进区域洪水管理实践以及向决策者提供有关气候变化的可操作信息至关重要。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Hydrology-Regional Studies Earth and Planetary Sciences-Earth and Planetary Sciences (miscellaneous)

CiteScore

6.70

自引率

8.50%

发文量

284

审稿时长

60 days

期刊介绍： Journal of Hydrology: Regional Studies publishes original research papers enhancing the science of hydrology and aiming at region-specific problems, past and future conditions, analysis, review and solutions. The journal particularly welcomes research papers that deliver new insights into region-specific hydrological processes and responses to changing conditions, as well as contributions that incorporate interdisciplinarity and translational science.