Work

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

Year: 2025

Type: article

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.

Source: Journal of Hydrology Regional Studies

DOI: 10.1016/j.ejrh.2025.102505

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

Institutions: China Institute of Water Resources and Hydropower Research, State Key Laboratory of Basin Water Cycle Simulation and Regulation, Ministry of Water Resources of the People's Republic of China, China Three Gorges University

Language: English

Topic: Climate variability and models

Subfield: Global and Planetary Change

Field: Environmental Science

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $2,600