Vulnerability Analysis of Wetlands under Changed Climate Scenarios with the help Water Cycle and Poly-Neural Networks
Casestudy on Vulnerability Analysis of Wetlands under Changed Climate Scenarios with the help Water Cycle and Poly-Neural Networks. 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. To get the complete instructions enroll for the internshp at energyinstyle.website
Recommended
More Related Content
Similar to Vulnerability Analysis of Wetlands under Changed Climate Scenarios with the help Water Cycle and Poly-Neural Networks
Similar to Vulnerability Analysis of Wetlands under Changed Climate Scenarios with the help Water Cycle and Poly-Neural Networks (20)
More from Mrinmoy Majumder
More from Mrinmoy Majumder (20)
Recently uploaded
Recently uploaded (20)
Vulnerability Analysis of Wetlands under Changed Climate Scenarios with the help Water Cycle and Poly-Neural Networks
- 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