Skip to main content

Reposted : How to predict time dependent hydrological variables with the help of neural network ?




We all know that most of the hydrological variables are time-dependent. It changes with time.

Artificial Neural Network on the other hand has been found to perform best for time-dependent problems.

Whether there is any relationship between time-dependent variables and neural network is unknown but it can be assured that neural networks can be used to develop models for the prediction of hydrological variables with better accuracy compared to other hydrological models.

The post below depicts the methodology for the application of neural networks in the development of hydrological models.

This is reposted from my Water and Energy Nexus Blog :

Thanking you,
Mrinmoy 
My ResearchGate Id : Mrinmoy_Majumder
Author of: Lecture Notes on MCDM
Labels:

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...
Read more

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...
Read more

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...
Read more