Multi-criteria decision approach to identify flood vulnerability zones using geospatial technology in the Kemp-Welch Catchment, Central Province, Papua New Guinea

Autor: Sailesh Samanta, Hua Morea
Rok vydání: 2020
Předmět:
Zdroj: Applied Geomatics. 12:427-440
ISSN: 1866-928X
1866-9298
Popis: Flood is a natural part of the hydrologic cycle, a natural phenomenon known for its catastrophic impacts on the environment, livelihoods, and properties, both economically and socially. It is the leading natural disaster in the world today affecting so many people, especially in the Asia region. Papua New Guinea has an abundance of rich resources and still possesses most of its natural geographic habitats and environments, but is also familiar with natural disasters like floods, earthquakes, landslides, volcanic eruptions, and droughts. Floods bring about tremendous destructions to anything that lies in its path, but it restores the health of the waterways/channels and redistributes the fertile sediments onto the floodplain. This research paper is focused on flood risk analysis using GIS and remote sensing, multi-criteria decision approach (MCDA), analytical hierarchy process (AHP), and the weighted linear combination (WLC). GIS-based spatial analysis techniques are useful for flood risk and hazard mapping with remote sensing technologies which provides an alternative to the conventional/traditional survey techniques. GIS coupled with remote sensing provides a basic framework/platform that helps in all stages of disaster assessment and management from preparedness, to response and recovery. Multi-criteria decision analysis (MCDA) is a collection of techniques that aid decision-makers in properly structuring multi-faceted decisions and evaluating the alternatives. AHP is a tool under MCDA that is used for dealing with complex decision-making and helps decision-makers set priorities and draw better decisions. Altogether, GIS-based MCDA-AHP became an efficient technique in flood risk mapping where multiple flood influential factors/criteria are incorporated into the GIS analysis process to producing better flood risk maps. In the present study, nine independent variables, namely elevation, slope, soil texture, soil drainage, landform, rainfall, distance from the main river, land use/land cover, and surface runoff, are used for flood vulnerability analysis. The resulted output demonstrated a span of value ranging from 1.13 (least vulnerable) to 4.15 (most vulnerable). The final map with 5 distinct classes is developed based on the natural junk classification method. The result indicated that about 4.57% of land area as “very high” and 12.49% as “high” flood vulnerable class and a total of 6700 people are living in those vulnerable zones. Past flood events are compared with the flood vulnerable database to validate the modeled output in the present study. This type of study will be very useful to the local government for future planning and decision on flood mitigation plans.
Databáze: OpenAIRE
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