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Estimating daily pan evaporation using adaptive neural-based fuzzy inference system

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dc.creator Terzi, Oezlem
dc.creator KESKİN, Mustafa Erol
dc.creator Taylan, Dilek
dc.date 2009-08-31T21:00:00Z
dc.date.accessioned 2020-10-06T11:26:22Z
dc.date.available 2020-10-06T11:26:22Z
dc.identifier dfe1b13a-7f5c-4341-a9c6-0ba18ccd4668
dc.identifier 10.1007/s00704-008-0092-7
dc.identifier https://avesis.sdu.edu.tr/publication/details/dfe1b13a-7f5c-4341-a9c6-0ba18ccd4668/oai
dc.identifier.uri http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/74155
dc.description Estimation of evaporation is important for water planning, management, and hydrological practices. There are many available methods to estimate evaporation from a water surface, comprising both direct and indirect methods. All the evaporation models are based on crisp conceptions with no uncertainty element coupled into the model structure although in daily evaporation variations there are uncontrollable effects to a certain extent. The probabilistic, statistical, and stochastic approaches require large amounts of data for the modeling purposes and therefore are not practical in local evaporation studies. It is therefore necessary to adopt a better approach for evaporation modeling, which is the fuzzy sets and adaptive neural-based fuzzy inference system (ANFIS) as used in this paper. ANFIS and fuzzy sets have been evaluated for its applicability to estimate evaporation from meteorological data which is including air and water temperatures, solar radiation, and air pressure obtained from Automated GroWheather meteorological station located near Lake Egirdir and daily pan evaporation values measured by XVIII. District Directorate of State Hydraulic Works. Results of ANFIS and fuzzy logic approaches were analyzed and compared with measured daily pan evaporation values. ANFIS approach could be employed more successfully in modeling the evaporation process than fuzzy sets.
dc.language eng
dc.rights info:eu-repo/semantics/closedAccess
dc.title Estimating daily pan evaporation using adaptive neural-based fuzzy inference system
dc.type info:eu-repo/semantics/article


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