modeling of dam reservoir volume using adaptive neuro fuzzy method.

modeling of dam reservoir volume using adaptive neuro fuzzy method.

;M. DEMIRCI;F. UNES;Z. KAYA;B. TASAR;H. VARCİN
progress in nuclear energy 2018 Vol. 2018 pp. 145-152
140
demirci2018aerulmodeling

Abstract

Dam reservoir capacity estimation are important for dam structures, operation, design and safety assessments. Predictions of reservoir volumes must be considered as one of the main part of water resources management. As it is known in water resources management, reservoir capacity has direct effects on choosing irrigation systems, energy production, water supply systems etc. in a study region. In this study, the reservoir capacity of the Stony Brook dam in the USA state of Massachusetts, was tried to be estimated. Data set is taken by U.S. Geological Survey Institute (USGS) website. Reservoir capacity was estimated by Adaptive Neuro Fuzzy (NF) and Multilinear Linear Regression Analysis (MLR). NF model results was compared with MLR results. For the comparison, Mean Square Error (MSE), Mean Absolute Error (MAE) and correlation coefficient statistics were used.

Citation

ID: 161580
Ref Key: demirci2018aerulmodeling
Use this key to autocite in SciMatic or Thesis Manager

References

Blockchain Verification

Account:
NFT Contract Address:
0x95644003c57E6F55A65596E3D9Eac6813e3566dA
Article ID:
161580
Unique Identifier:
10.24193/AWC2018_18
Network:
Scimatic Chain (ID: 481)
Loading...
Blockchain Readiness Checklist
Authors
Abstract
Journal Name
Year
Title
5/5
Creates 1,000,000 NFT tokens for this article
Token Features:
  • ERC-1155 Standard NFT
  • 1 Million Supply per Article
  • Transferable via MetaMask
  • Permanent Blockchain Record
Blockchain QR Code
Scan with Saymatik Web3.0 Wallet

Saymatik Web3.0 Wallet