Skip to main content

How to apply optimization techniques to water resource problems?

"Maximizing or minimizing some function comparative to some set, often demonstrating a range of choices available in a certain situation. The function allows evaluation of the different choices for determining which might be “best.”....Definition of optimization.

In optimization, we try to maximize/minimize/achieve a target value by changing some variables of an objective equation.

Optimization is used to solve problems in a multidisciplinary field including water resources. The problems like solving multi-reservoir optimization, water allocation problems, economic optimization of water-based systems, performance optimization of water treatment plants, etc. can be easily solved by the application of various classical and new optimization techniques.

If you search on the internet, especially through Sciencedirect or Springer Portals you will find multiple papers on the Optimization Technique and its application to water resource problems

For example, the following are some of the contemporary works in this field: 

Saray, Marzieh Hasanzadeh, et al. "Optimization of Water-Energy-Food Nexus considering CO2 emissions from cropland: A case study in northwest Iran." Applied Energy 307 (2022): 118236.

Ward, Frank A., et al. "Economic optimization to guide climate water stress adaptation." Journal of Environmental Management 301 (2022): 113884.

Alam, Gulzar, et al. "Applications of artificial intelligence in water treatment for optimization and automation of adsorption processes: Recent advances and prospects." Chemical Engineering Journal 427 (2022): 130011.

To  understand what, when, and how you can apply optimization techniques, one of my following tutorials on the fundamentals of optimization techniques can be useful:





@Mrinmoy's Page
@Merchandise or @bShop
Add to Listy/Recommend@Baipatra

Popular posts from this blog

Free Ecourse on MCDM Techniques

1) ELECTRE : ELIMINATION AND CHOICE TRANSLATING REALITY 2)FMAE : FAILURE MODE EFFECTS ANALYSIS 3)MAUT : MULTI ATTRIBUTE UTILITY THEORY 4&5)PROMETHEE : PREFERENCE RANKING ORGANIZATION METHOD FOR ENRICHMENT EVALUATION (One and Two) 6)RA : RELIABILITY ANALYSIS 7)WSM : WEIGHTED SUM METHOD 8)WPM : WEIGHTED PRODUCT METHOD 9)DELPHI Method All these are free video tutorials. For case studies and project ideas please upgrade to Paid Member. Click here  to procure in INR: Other than INR :  Click here You may also like : HydroGeek: The newsletter for researchers of water resources https://hydrogeek.substack.com/ Baipatra VSC: Enroll for online courses for Free http://baipatra.ws Energy in Style: Participate in Online Internships for Free http://energyinstyle.website Innovate S: Online Shop for Water Researchers https://baipatra.stores.instamojo.com/ Call for Paper: International Journal of HydroClimatic Engineering http://energyinstyle.website/journals/ Hydro Geek Newsletter Edition...

What will the future of AI and ML modeling of water resource development look like?

AI & ML modeling application is now widespread in water resource development studies. But due to the uncertainty in water parameters, much more innovation is required for their practical applications In recent years like all the other fields of studies, the application of Artificial Intelligence and Machine Learning (AI&ML) on water resource development projects has increased manifold. For example : Sukanya, S., and Sabu Joseph. " Climate change impacts on water resources: An overview ." Visualization Techniques for Climate Change with Machine Learning and Artificial Intelligence (2023): 55-76. Kommadi, Bhagvan. " AI and ML Applications: 5G and 6G ." (2023). Joseph, Kiran, Ashok K. Sharma, Rudi van Staden, P. L. P. Wasantha, Jason Cotton, and Sharna Small. " Application of Software and Hardware-Based Technologies in Leaks and Burst Detection in Water Pipe Networks: A Literature Review ."  Water  15, no. 11 (2023): 2046. Yurtsever, Mustafa, and E. M...

Master TOPSIS to operationalise multi-criteria trade‑offs using distance‑to‑ideal reasoning and transparent rankings.

1.TOPSIS is best described as A single-objective optimization method for continuous design variables A compensatory MCDM method for ranking finite alternatives across multiple criteria A simulation technique for stochastic hydrologic models A clustering algorithm for grouping similar alternatives 2.In TOPSIS, the Positive Ideal Solution (PIS) represents The alternative with the smallest Euclidean norm in the decision space A hypothetical alternative with the best performance on every criterion The real alternative that appears first in the decision matrix The average of all alternatives over all criteria 3.The Negative Ideal Solution (NIS) in TOPSIS is The worst-performing actual alternative in the dataset A hypothetical alternative with the worst value of each criterion The alternative with the largest index in the matrix The alternative that violates the most constraints 4.For benefit-type criteria (to be maximized), the PIS component is taken as The minimum observed val...