EVALUATION AND COMPARISON OF GRNN, MLP AND RBF NEURAL NETWORKS FOR ESTIMATING CUCUMBER, TOMATO AND REFERENCE CROPS’ EVAPOTRANSPIRATION IN GREENHOUSE CONDITION

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

REZAVERDINEJAD V.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Water and Soil Science Year:2015 | Volume:25 | Issue:4/2 Page(s): 123-136

Download Full-Text

Persian Verion

View:

911

Download:

Cites:

Information Journal Paper

Title

EVALUATION AND COMPARISON OF GRNN, MLP AND RBF NEURAL NETWORKS FOR ESTIMATING CUCUMBER, TOMATO AND REFERENCE CROPS’ EVAPOTRANSPIRATION IN GREENHOUSE CONDITION

Author(s)

REZAVERDINEJAD V. | Issue Writer Certificate

Keywords

AIR VAPOR PRESSUREQ2

ARTIFICIAL NEURAL NETWORKQ3

CUCUMBERQ3

EVAPOTRANSPIRATIONQ2

INCOMING RADIATIONQ3

TOMATOQ3

Abstract

In this study, the performance of multi-layer perceptron (MLP), radial basis function (RBF) and generalize regression neural networks (GRNN), were evaluated to estimate CUCUMBER, TOMATO and reference crops’ EVAPOTRANSPIRATION (ET) in greenhouse environment. For this purpose, the lysimetric ET values along with the effective meteorological factors on EVAPOTRANSPIRATION process, including air temperature, humidity, AIR VAPOR PRESSURE, and INCOMING RADIATION were measured. The results indicated that introducing the all meteorological factors as ARTIFICIAL NEURAL NETWORK models’ inputs increased the models accuracy. Based on the results, the GRNN model estimated the reference EVAPOTRANSPIRATION with the highest accuracy in comparison to the RBF and MLP models. The average estimation errors of MLP, RBF and GRNN networks for the test phase were 9.4, 13.3 and 9 percent corresponding to the values of 0.24, 0.27 and 0.20 mmd-1, respectively. Performance of GRNN model in estimating CUCUMBER and TOMATO EVAPOTRANSPIRATION values also was appropriate. The average values of the errors for the estimated amounts of the CUCUMBER and TOMATO EVAPOTRANSPIRATION by the GRNN model, were 11.0 (0.21 mmd-1) and 10.1 (0.22 mmd-1) percent, while they were obtained by MLP and RBF models 11.4 (0.22 mmd-1) and 10.9 (0.26 mmd-1); and also 12.3 (0.23 mm d-1) and 13.8 (0.28 mm d-1) percent for MLP and RBF models, respectively. Based on ideal point error index, the GRNN model showed accurate performance in estimating crop EVAPOTRANSPIRATION in greenhouse.

Cites

No record.

References

No record.

Cite

APA: Copy

REZAVERDINEJAD, V.. (2015). EVALUATION AND COMPARISON OF GRNN, MLP AND RBF NEURAL NETWORKS FOR ESTIMATING CUCUMBER, TOMATO AND REFERENCE CROPS’ EVAPOTRANSPIRATION IN GREENHOUSE CONDITION. WATER AND SOIL SCIENCE (AGRICULTURAL SCIENCE), 25(4/2), 123-136. SID. https://sid.ir/paper/147858/en

Vancouver: Copy

REZAVERDINEJAD V.. EVALUATION AND COMPARISON OF GRNN, MLP AND RBF NEURAL NETWORKS FOR ESTIMATING CUCUMBER, TOMATO AND REFERENCE CROPS’ EVAPOTRANSPIRATION IN GREENHOUSE CONDITION. WATER AND SOIL SCIENCE (AGRICULTURAL SCIENCE)[Internet]. 2015;25(4/2):123-136. Available from: https://sid.ir/paper/147858/en

IEEE: Copy

V. REZAVERDINEJAD, “EVALUATION AND COMPARISON OF GRNN, MLP AND RBF NEURAL NETWORKS FOR ESTIMATING CUCUMBER, TOMATO AND REFERENCE CROPS’ EVAPOTRANSPIRATION IN GREENHOUSE CONDITION,” WATER AND SOIL SCIENCE (AGRICULTURAL SCIENCE), vol. 25, no. 4/2, pp. 123–136, 2015, [Online]. Available: https://sid.ir/paper/147858/en

Related Journal Papers