Estimation of Nutrient Concentrations in Runoff from Beef Cattle Feedlot using Adaptive Neuro-Fuzzy Inference Systems

Title:

Estimation of Nutrient Concentrations in Runoff from Beef Cattle Feedlot using Adaptive Neuro-Fuzzy Inference Systems

Creator:

Simsek, H., Cemek, B., Odabas, M. S., and Rahman, S.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:a06a9af5-aa1a-496d-ba8b-f7ba03cebee8
uuid:a06a9af5-aa1a-496d-ba8b-f7ba03cebee8
doi:10.14311/NNW.2015.25.025

Subject:

nutrient concentration, cattle feedlot, grid partition based fuzzy inference system (ANFIS-GP), and subtractive clustering based fuzzy inference system (ANFIS-SC)

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

Nutrient concentrations in runoff from beef cattle feedlots were estimated using two different adaptive network-based fuzzy inference systems (ANFIS), which were: (1) grid partition (ANFIS-GP) and (2) subtractive clustering based fuzzy inference system (ANFIS-SC). The input parameters were pH and electrical conductivity (EC); and the output parameters were total Kjeldahl nitrogen (TKN), ammonium-N (NH4-N), orthophosphate (ortho-P), and potassium (K). Models performances were evaluated based on root mean square error, mean absolute error, mean bias error, and determination coeficient statistics. For the same dataset, the ANFIS model outputs were also compared with a previously published nutrient concentration predictability model for runoff using artificial neural network (ANN) outputs. Results showed that both ANFIS-GP and ANFIS-SC models successfully predicted the runoff nutrient concentration. The comparison results revealed that the ANFIS-GP model performed slightly better than ANFIS-SC model in estimating TKN, NH4-N, ortho-P, and K. When compared with the ANN model for the same dataset, ANFIS outperformed ANN in nutrient concentration prediction in runoff.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2015 Volume:25 | Number:5

Harvested from:

CDK

Metadata only:

false