ANN-based PCA to predict evapotranspiration: a case study in India
The Penman–Monteith evapotranspiration (ET) model has superior predictive ability to other methods, but it is challenging to apply in several Indian stations, owing to the need for a large number of climatic variables. The study investigated an artificial neural network (ANN) model for calculating E...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IWA Publishing
2023-07-01
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| Series: | Aqua |
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| Online Access: | http://aqua.iwaponline.com/content/72/7/1145 |
| _version_ | 1797745712496115712 |
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| author | Marykutty Abraham Sankaralingam Mohan |
| author_facet | Marykutty Abraham Sankaralingam Mohan |
| author_sort | Marykutty Abraham |
| collection | DOAJ |
| description | The Penman–Monteith evapotranspiration (ET) model has superior predictive ability to other methods, but it is challenging to apply in several Indian stations, owing to the need for a large number of climatic variables. The study investigated an artificial neural network (ANN) model for calculating ET for various agro-climatic regions of India. Sensitivity analysis showed that the overall average changes in ET0 values for 25% change in the climatic variables were 18, 16, 14, 7, 5, and 4%, respectively, for Tmax, RHmean, Rn, wind speed, Tmin, and sunshine hours. The dominant climatic variables were identified from the principal component analysis (PCA) and ET0 was computed using an ANN with dominant climatic variables. The ANN architecture with backpropagation technique had one hidden layer and neurons ranging from 10 to 30 for all climatic variables and from 5 to 10 for PCA variables. The new ET models were statistically compared with Penman–Monteith ET estimate, and found reliable. PCA variables guaranteed an estimate of ET0 accounting for 98% of the variability. The average values of coefficient of determination, standard error of estimate, and percentage efficiency were observed as 0.96, 0.24, and 94%, respectively.
HIGHLIGHTS
The Penman–Monteith ET model is the standard but data-intensive, so its applicability is limited.;
The crucial climatic variables influencing ET are identified for various agro-climatic regions using principal component analysis and sensitivity analysis.;
New ET models are developed and compared with the standard Penman–Monteith ET estimate.;
Less data-intensive ANN models are proven to be acceptable in estimating ET0.; |
| first_indexed | 2024-03-12T15:28:04Z |
| format | Article |
| id | doaj.art-e6315ea8ea94411f8a117d2dfe197118 |
| institution | Directory Open Access Journal |
| issn | 2709-8028 2709-8036 |
| language | English |
| last_indexed | 2024-03-12T15:28:04Z |
| publishDate | 2023-07-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Aqua |
| spelling | doaj.art-e6315ea8ea94411f8a117d2dfe1971182023-08-10T12:56:51ZengIWA PublishingAqua2709-80282709-80362023-07-017271145116310.2166/aqua.2023.201201ANN-based PCA to predict evapotranspiration: a case study in IndiaMarykutty Abraham0Sankaralingam Mohan1 Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu 600119, India Department of Civil Engineering, IIT Madras, Chennai, Tamil Nadu 600036, India The Penman–Monteith evapotranspiration (ET) model has superior predictive ability to other methods, but it is challenging to apply in several Indian stations, owing to the need for a large number of climatic variables. The study investigated an artificial neural network (ANN) model for calculating ET for various agro-climatic regions of India. Sensitivity analysis showed that the overall average changes in ET0 values for 25% change in the climatic variables were 18, 16, 14, 7, 5, and 4%, respectively, for Tmax, RHmean, Rn, wind speed, Tmin, and sunshine hours. The dominant climatic variables were identified from the principal component analysis (PCA) and ET0 was computed using an ANN with dominant climatic variables. The ANN architecture with backpropagation technique had one hidden layer and neurons ranging from 10 to 30 for all climatic variables and from 5 to 10 for PCA variables. The new ET models were statistically compared with Penman–Monteith ET estimate, and found reliable. PCA variables guaranteed an estimate of ET0 accounting for 98% of the variability. The average values of coefficient of determination, standard error of estimate, and percentage efficiency were observed as 0.96, 0.24, and 94%, respectively. HIGHLIGHTS The Penman–Monteith ET model is the standard but data-intensive, so its applicability is limited.; The crucial climatic variables influencing ET are identified for various agro-climatic regions using principal component analysis and sensitivity analysis.; New ET models are developed and compared with the standard Penman–Monteith ET estimate.; Less data-intensive ANN models are proven to be acceptable in estimating ET0.;http://aqua.iwaponline.com/content/72/7/1145agro-climatic regionsann modelsevapotranspirationpenman–monteith modelprincipal component analysis |
| spellingShingle | Marykutty Abraham Sankaralingam Mohan ANN-based PCA to predict evapotranspiration: a case study in India Aqua agro-climatic regions ann models evapotranspiration penman–monteith model principal component analysis |
| title | ANN-based PCA to predict evapotranspiration: a case study in India |
| title_full | ANN-based PCA to predict evapotranspiration: a case study in India |
| title_fullStr | ANN-based PCA to predict evapotranspiration: a case study in India |
| title_full_unstemmed | ANN-based PCA to predict evapotranspiration: a case study in India |
| title_short | ANN-based PCA to predict evapotranspiration: a case study in India |
| title_sort | ann based pca to predict evapotranspiration a case study in india |
| topic | agro-climatic regions ann models evapotranspiration penman–monteith model principal component analysis |
| url | http://aqua.iwaponline.com/content/72/7/1145 |
| work_keys_str_mv | AT marykuttyabraham annbasedpcatopredictevapotranspirationacasestudyinindia AT sankaralingammohan annbasedpcatopredictevapotranspirationacasestudyinindia |