Recognition and Ranking of Factors Influencing Flood Occurrence in Baghlan Markazi District Using AHP and SAW Methods

Saleh Mohammad Salehy; Mohammad Bashir Aimaq; Masoud Haqbin

doi:10.62810/jnsr.v3i2.226

Authors

Saleh Mohammad Salehy Baghlan University, Department of Mining Engineering, Faculty of Engineering, Afghanistan
Mohammad Bashir Aimaq Baghlan University, Department of Mining Engineering, Faculty of Engineering, Afghanistan
Masoud Haqbin Jawzjan University, Department of Petroleum Engineering, Faculty of Geology and Mining, Afghanistan

DOI:

https://doi.org/10.62810/jnsr.v3i2.226

Keywords:

Afghanistan, AHP Method, Baghlan, Floods, Hazards, Segregation

Abstract

Floods are one of the major natural hazards globally, leading to substantial economic damages and loss of human lives annually as a result of various contributing factors. Among natural disasters, floods contribute to approximately 20% of global disaster-related mortality and 33% of total economic losses. Afghanistan is one of the developing countries that, due to its broader challenges, has not yet implemented a comprehensive strategy for flood management. Baghlan Province, as one of Afghanistan's key industrial and agricultural regions, is highly vulnerable to flooding due to its topographical features. Central Baghlan District experienced significant human and financial losses as a result of floods in the spring of 1403 AH. This paper first conducts a detailed analysis of the factors influencing flood occurrence in Sheikh Jalal, Darwaza Kan, Laqiha, and Shahrak Mohajerin areas of Central Baghlan District. Subsequently, four key factors and twenty specific criteria were identified as significantly affecting flood occurrence in the region. After conducting field observations and site studies, a questionnaire was developed in accordance with the identified effective factors and criteria, structured within the framework of the Analytical Hierarchy Process (AHP). Interviews were then carried out with local residents, domain experts, and government officials to collect relevant data. After obtaining the weights of the criteria, the second questionnaire was designed using the Simple Additive Weighting (SAW) method, and interviews were conducted with local residents, experts, and relevant officials. The influencing factors were ranked based on their normalized weights, where the environmental factor ranked first with a weight of 0.261, while the economic factor ranked last with a weight of 0.224.

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