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

Abstract

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.

Characterization of Meteorological Drought Using Monte Carlo Feature Selection and Steady‐State Probabilities

“…e suitable stations are chosen based on relative importance (RI) values. e mathematical detail about the RI is given in [85]. For the current analysis, the Astore Station with RI value of 0.1385 is higher than other selected stations for SPI.…”

“…Niaz et al [85] used MCFS for selecting informative stations for their analysis. ey applied MCFS in the Punjab region of Pakistan and selected important stations based on SPTI.…”

“…e application of SSP is provided in several publications [76,97]. Niaz et al [85] used SSP as a weighting scheme from the long-run time-series data for different drought classes in the northern region of Pakistan. e proposed weighting scheme was used to accumulate information from the selected homogeneous stations.…”

“…Further, the Standardized Precipitation Evapotranspiration Index (SPEI) is also a well-known index proposed by [50] that triggers the effect of temperature variability on drought estimation. Numerous analyses have employed SPEI for the drought evaluation [77][78][79][80][81][82][83], and Standardized Precipitation and Temperature Index (SPTI) by [84] is also considered in multiple studies for the assessment of meteorological drought [76,[84][85][86].…”

A New Comprehensive Framework for Identifying and Monitoring the Interseasonal Characteristics of Meteorological Drought

“…Several researchers and climatologists worldwide have used drought indices to investigate meteorological drought and inform the drought community to develop drought policies and conduct risk management [26,[30][31][32]. Drought indices are particularly useful tools and play an important role in monitoring droughts and providing a comprehensive overview of drought conditions [33][34][35][36][37][38][39]. Te Standardized Precipitation Index (SPI) has prevailed to assess meteorological drought due to simplicity in calculation and ability to assess drought for diferent time scales [24,30,[40][41][42][43][44][45][46].…”