Characterization of Meteorological Drought Using Monte Carlo Feature Selection and Steady-State Probabilities. (31st May 2022)

• Record Type: Journal Article
• Authors: Niaz, Rizwan; Tanveer, Fahad; Almazah, Mohammed M. A.; Hussain, Ijaz; Alkhatib, Soliman; Al-Razami, A.Y.
• Other Names: Afan Haitham Abdulmohsin Academic Editor.
• Is Part Of: Complexity. Volume 2022(2022)
• Publication Date: 2022-05-31
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2022/1172805
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : Drought is a creeping phenomenon that slowly holds an area over time and can be continued for many years. The impacts of drought occurrences can affect communities and environments worldwide in several ways. Thus, assessment and monitoring of drought occurrences in a region are crucial for reducing its vulnerability to the negative impacts of drought. Therefore, comprehensive drought assessment techniques and methods are required to develop adaptive strategies that a region can undertake to reduce its vulnerability to drought substantially. For this purpose, this study proposes a new method known as a regional comprehensive assessment of meteorological drought (RCAMD). The Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and Standardized Precipitation and Temperature Index (SPTI) are jointly used for the development of the RCAMD. Further, the RCAMD employs Monte Carlo feature selection (MCFS) and steady-state probabilities (SSPs) to comprehensively collect information from various stations and drought indices. Moreover, the RCAMD is validated on the six selected stations in the northern areas of Pakistan. The outcomes associated with the RCAMD provide a comprehensive regional assessment of meteorological drought and become the initial source for bringing more considerations to drought monitoring and early warning systems.