A Novel Framework for Selecting Informative Meteorological Stations Using Monte Carlo Feature Selection (MCFS)‎ Algorithm

Abstract EN

Spatial distribution of meteorological stations has a significant role in hydrological research.

The meteorological data play a significant role in drought monitoring; in this regard, accurate and suitable provision of meteorological stations is becoming crucial to improve and strengthen the skill of drought prediction.

In this perspective, the choice of meteorological stations in a specific region has substantial importance for accurate estimation and continuous monitoring of drought hazards at the regional level.

However, installation and data mining on a large number of meteorological stations require high cost and resources.

Therefore, it is necessary to rank and find dependencies among existing meteorological stations in a particular region for further climatological analysis and reanalysis of databases.

In this paper, the Monte Carlo feature selection and interdependency discovery (MCFS-ID) algorithm-based framework is proposed to identify the important meteorological station in a particular region.

We applied the proposed framework on 12 meteorological stations situated in varying climatological regions of Punjab (Pakistan).

We employed the drought index SPTI on 1-, 3-, 6-, 9-, 12-, 24-, and 48-month time-scale data to find the interdependencies among meteorological stations at various locations.

We found that Sialkot has significance regional importance for studying SPTI-3, SPTI-6, and SPTI-48 indices.

This regional importance is based on scores of relative importance (RI); for example, the RI values for SPTI-3, SPTI-6, and SPTI-48 indices are 0.1570, 0.1080, and 0.0270, respectively.

Furthermore, the Jhelum station has more relative importance (RI = 0.1410 and 0.1030) for SPTI-1 and SPTI-9 indices, while varying concentration behaviour is observed in the remaining time scales.