Principal Component Analysis of Climatic Parameters in the Mat River Basin, Northern Albania

By Albana HASIMI, Blerina PAPAJANI, Elvin ÇOMO, Idajet SELMANI, Gazmir ÇELA, Mirela DVORANI

Abstract
Principal Component Analysis (PCA) is widely applied across diverse scientific fields, including meteorology, agriculture, tourism, and environmental studies. In this work, we provide a systematic examination of the capacity of PCA to explain variability and reduce dimensionality in multivariate datasets. Our findings confirm the methodological effectiveness of PCA while demonstrating that data standardization can substantially influence the resulting component structure. These insights are particularly relevant for researchers aiming to use and interpret PCA within climatology and related disciplines. The present study offers a critical evaluation of how one of the oldest and most broadly utilized statistical techniques—PCA—is applied to a set of meteorological variables collected from stations distributed across the Mat River Basin in northern Albania. The primary objective is to identify the dominant climatic gradients in the basin and to assess spatial and temporal variability in temperature, precipitation, and derived bioclimatic indicators. The results indicate that the first two principal components explain 94.53% of the total variance, reflecting a high degree of structural coherence within the regional climate system.

Keywords: Principal component analysis; meteorological variable; climatic classification; Mati river basin.

https://doi.org/10.58944/mqgg3666