Finding Coastal Vulnerability Maximization Sites using MCDM-Guided NIPT (Nature Inspired Predictive Technique)
Authors : RATNADEEP MODAK*; Sujit Kumar Pal**; TILOTTAMA CHAKRABORTY***; SATYABRATA SAHA****; MRINMOY MAJUMDER*****
*Teaching Assistant, Civil Engg Department.NIT Agartala
**Professor,Civil Engg Department.NIT Agartala
***Assistant Professor,Civil Engg Department.NIT Agartala
****Assistant Professor,Civil Engg Department.NIT Agartala
*****Associate Professor,Civil Engg Department.NIT Agartala
Abstract
The coastal regions of many oceans on many continents are becoming more vulnerable due to the effects of climate change and extensive urbanization. A coastal zone can become vulnerable due to a number of issues, such as encroachment, uncontrolled tourism, coastal erosion, deforestation of mangroves, etc. But not every spot along a coast is at risk in the same way. Certain people are more susceptible than others. In order to create an indication that reflects a location's vulnerability, each input parameter's function in the vulnerability calculation must be clearly stated. The goal of the current study is to identify the factor that most significantly contributes to vulnerability. However, as of right now, there is no method for identifying the primary factor that caused coastal vulnerability. The current goal was to carry out a strategy to determine which metric is most important. It will be challenging to year mark the site as a result, necessitating quick mitigating actions. This work employs a two-phase methodology to address this problem. First, using the MAUT, it will assess the likely causes of the coastal damage parameters. Then, depending on the results of these comparisons, it will compare many sites. Second, it will use GMDH and MAUT to create a decision-making application for identifying coastal vulnerability areas. This will allow for the implementation of mitigating measures where necessary, assuring the best use of money allotted for the adoption of compensatory measures.
Keywords
MCDM,GMDH,MAUT