FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3169963517> ?p ?o ?g. }

• W3169963517 endingPage "107003" @default.
• W3169963517 startingPage "107003" @default.
• W3169963517 abstract "Reference evapotranspiration (ET 0 ), widely used in efficient and meaningful scheduling of irrigation events, is an essential component of agricultural water management strategy for proper utilization of limited water resources. Accurate and early prediction of ET 0 can provide the basis for designing effective irrigation scheduling and help in resourceful management of water in agriculture. This study aims to evaluate and compare the performances of different hybridized Adaptive Neuro Fuzzy Inference System (ANFIS) models with optimization algorithms for predicting daily ET 0 . The FAO-56 Penman-Monteith method was used to estimate daily ET 0 values using historical weather data obtained from a weather station in Bangladesh. The obtained climatic variables and the estimated ET 0 values form the input-output training patterns for the hybridized ANFIS models. The performances of these hybridized ANFIS models were compared with the classical ANFIS model tuned with combined Gradient Descent method and the Least Squares Estimate (GD-LSE) algorithm. Performance ranking of these ANFIS models was performed using Shannon’s Entropy (SE), Variation Coefficient (VC), and Grey Relational Analysis (GRA) based decision theories supported by eight statistical indices. Results indicate that both SE and VC based decision theories provided the similar ranking though the numeric values of weights differed. On the other hand, GRA provided a slightly different sequence of ranking. Both SE and VC identified Firefly Algorithm-ANFIS (FA-ANFIS) as the best performing model followed by Particle Swarm Optimization-ANFIS. In contrast, FA-ANFIS was found to be the second-best performing model according to the ranking provided by GRA with a negligible difference in weight between FA-ANFIS and the classical ANFIS model (GD-LSE-ANFIS). Therefore, FA-ANFIS can be considered as the best model, which can be utilized to predict daily ET 0 values for areas with similar climatic conditions. The findings of this research is of great importance for the planning of effective irrigation scheduling. • Performance comparison of different optimization algorithm tuned ANFIS models for ET 0 prediction is demonstrated. • Fifteen optimization algorithms are utilized for ANFIS parameter tuning. • Benefit and cost indices are incorporated within the decision theories to compare the performances of ANFIS models. • Shannon’s Entropy, Variation Coefficient, and Grey Relational Analysis are utilized to provide ranking of the ANFIS models." @default.
• W3169963517 created "2021-06-22" @default.
• W3169963517 creator A5055071673 @default.
• W3169963517 creator A5058537160 @default.
• W3169963517 creator A5072960564 @default.
• W3169963517 creator A5078813876 @default.
• W3169963517 creator A5087688538 @default.
• W3169963517 date "2021-09-01" @default.
• W3169963517 modified "2023-10-16" @default.
• W3169963517 title "Optimization algorithms as training approaches for prediction of reference evapotranspiration using adaptive neuro fuzzy inference system" @default.
• W3169963517 cites W1484413473 @default.
• W3169963517 cites W1595159159 @default.
• W3169963517 cites W1923378012 @default.
• W3169963517 cites W1972641521 @default.
• W3169963517 cites W1973344262 @default.
• W3169963517 cites W1974182808 @default.
• W3169963517 cites W1975009952 @default.
• W3169963517 cites W1982358155 @default.
• W3169963517 cites W1993885071 @default.
• W3169963517 cites W1995450389 @default.
• W3169963517 cites W1995875735 @default.
• W3169963517 cites W2003243598 @default.
• W3169963517 cites W2005806759 @default.
• W3169963517 cites W2013355544 @default.
• W3169963517 cites W2013377700 @default.
• W3169963517 cites W2019207321 @default.
• W3169963517 cites W2020093619 @default.
• W3169963517 cites W2020999234 @default.
• W3169963517 cites W2021717613 @default.
• W3169963517 cites W2024060531 @default.
• W3169963517 cites W2024069451 @default.
• W3169963517 cites W2025148441 @default.
• W3169963517 cites W2030015277 @default.
• W3169963517 cites W2030605379 @default.
• W3169963517 cites W2033011996 @default.
• W3169963517 cites W2054014270 @default.
• W3169963517 cites W2056760934 @default.
• W3169963517 cites W2068028584 @default.
• W3169963517 cites W2070427882 @default.
• W3169963517 cites W2073587500 @default.
• W3169963517 cites W2074082212 @default.
• W3169963517 cites W2079325629 @default.
• W3169963517 cites W2089342818 @default.
• W3169963517 cites W2100718938 @default.
• W3169963517 cites W2107941094 @default.
• W3169963517 cites W2118051273 @default.
• W3169963517 cites W2126105956 @default.
• W3169963517 cites W2129694088 @default.
• W3169963517 cites W2138537392 @default.
• W3169963517 cites W2144758276 @default.
• W3169963517 cites W2146047772 @default.
• W3169963517 cites W2152195021 @default.
• W3169963517 cites W2154061444 @default.
• W3169963517 cites W2156194072 @default.
• W3169963517 cites W2159265133 @default.
• W3169963517 cites W2167493478 @default.
• W3169963517 cites W2168081761 @default.
• W3169963517 cites W2177283882 @default.
• W3169963517 cites W2247666031 @default.
• W3169963517 cites W2266077308 @default.
• W3169963517 cites W2482393783 @default.
• W3169963517 cites W2555645875 @default.
• W3169963517 cites W2565672080 @default.
• W3169963517 cites W2603417106 @default.
• W3169963517 cites W2749106749 @default.
• W3169963517 cites W2787907398 @default.
• W3169963517 cites W2808724894 @default.
• W3169963517 cites W2811389973 @default.
• W3169963517 cites W2883787252 @default.
• W3169963517 cites W2886977329 @default.
• W3169963517 cites W2890463426 @default.
• W3169963517 cites W2908453979 @default.
• W3169963517 cites W2914010233 @default.
• W3169963517 cites W2920819147 @default.
• W3169963517 cites W2921467030 @default.
• W3169963517 cites W2922363882 @default.
• W3169963517 cites W2923494546 @default.
• W3169963517 cites W2944755434 @default.
• W3169963517 cites W2950434885 @default.
• W3169963517 cites W2957731227 @default.
• W3169963517 cites W2963918665 @default.
• W3169963517 cites W2991192488 @default.
• W3169963517 cites W3003515180 @default.
• W3169963517 cites W3011744717 @default.
• W3169963517 cites W3036869571 @default.
• W3169963517 cites W3037961787 @default.
• W3169963517 cites W3042434740 @default.
• W3169963517 cites W3043476082 @default.
• W3169963517 cites W3046973866 @default.
• W3169963517 cites W3081335492 @default.
• W3169963517 cites W3083174013 @default.
• W3169963517 cites W3085019631 @default.
• W3169963517 cites W3096916113 @default.
• W3169963517 cites W3098647604 @default.
• W3169963517 cites W3100539479 @default.
• W3169963517 doi "https://doi.org/10.1016/j.agwat.2021.107003" @default.
• W3169963517 hasPublicationYear "2021" @default.
• W3169963517 type Work @default.

• next