The document outlines a project on vulnerability analysis of wetlands under changing climate scenarios using water cycle algorithms and poly-neural networks. It details a methodology for assessing vulnerability by categorizing factors into reliability and risk enhancement groups, applying analytical hierarchy process to weigh their importance. The ultimate goal is to maximize a vulnerability assessment function (VAF) using optimization techniques to identify the most optimal solution for predicting wetland vulnerabilities.

1. Vulnerability Analysis of
Wetlands under Changed
Climate Scenarios with the help
Water Cycle and Poly-Neural
Networks
Dr. Mrinmoy Majumder
HydroGeek@Substack
VeryShortTimeCourse@Baipatra,w
s
Achieve this project objective as
your internship at
EnergyinStyle.website
Part of BOT Group available at
https://innovates.gumroad.com/l/opt
imization_techniques

2. Prerequisite
Water Cycle Algorithms
https://youtu.be/6HdyvSkS
P8c
Polyneural Network
https://youtu.be/S-
la6JXwamY

3. Vulnerability Assessment Function for Wetlands
• Select the factors from literature and stakeholders survey to identify the most
significant factors.
• Separate these into two groups :
i)One group is for Reliability Enhancing Factors(R)
ii)Another group is for Risk Enhancing Factors(r)
Find the weightage of importance of each factors of each group with respect the
impact of climate change on them. Use the Analytical Hierarchy Process Multi
Criteria Decision Making method to determine the weightage. Here Climate
Variables like Rainfall and Evapotranspiration can be selected as the criteria and all
the factors as the alternative. Determine the weightage one group at a time.
Now place the sum of the value of the Reliability Enhancement Factors, (each
multiplied with their weightage of importance) in Numerator and the sum of the
value of Risk Enhancing Factor (each multiplied with their weightage of
importance) in the Denominator.

4. Let
Vulnerability Assessment
Function be VAF
• ⅈ𝑓

5. Application of Optimization Techniques to
Maximize VAF
• Next step is to maximize VAF by considering the VAF as the
objective function
• The value of the factors(both Reliability and Risk) as design
variables
• Using Water Cycle Algorithm and Polynomial Neural Network as
optimization technique.
• Here if the value of R and r is normalized then 0 and 1 can be
considered as the lower and upper constraint of the variables.
• Now maximize VAF once by Water Cycle and another time by
Polynomial Neural Networks. Note the maximum VAF as optimized
by Water Cycle and Polynomial Neural Network. Select the higher
VAF as the most optimal solution and note down the value of the
design variables as the optimal ratio.
• This same VAF can be utilized to predict vulnerability of wetlands