Prediction of Drought Severity Using Model-Based Clustering

Rizwan Niaz, Ijaz Hussain, Xiang Zhang, Zulfiqar Ali, Elsayed Elsherbini Elashkar, Jameel Ahmad Khader, Sadaf Shamshoddin Soudagar and Alaa Mohamd Shoukry

Mathematical Problems in Engineering, 2021, vol. 2021, 1-10

Abstract:

Drought is a common climatic extreme that frequently spreads across large spatial and time scales. It affects living standard of people throughout the globe more than any other climate extreme. Therefore, the present study proposed a new technique, known as model-based clustering of categorical drought states sequences (MBCCDSS), for monthly prediction of drought severity to timely inform decision-makers to anticipate reliable actions and plans to minimize the negative impacts of drought. The potential of the proposed technique is based on the expectation-maximization (EM) algorithm for finite mixtures with first-order Markov model components. Moreover, the proposed approach is validated on six meteorological stations in the northern area of Pakistan. The study outcomes provide the basis to explore and frame more essential assessments to mitigate drought impacts for the selected stations.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2021/9954293.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2021/9954293.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:9954293

DOI: 10.1155/2021/9954293

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().