Computation of Evapotranspiration with Artificial Intelligence for Precision Water Resource Management
Accurate estimation of reference evapotranspiration (ETo) provides useful information for water resource management and sustainable agriculture. This study estimates ETo with recurrent neural networks (RNNs), namely long short-term memory (LSTM) and bidirectional LSTM. Four representative meteorolog...
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ftdoajarticles:oai:doaj.org/article:417406d8a7c34a549a923b5d5d6e2e3c 2023-05-15T17:37:59+02:00 Computation of Evapotranspiration with Artificial Intelligence for Precision Water Resource Management Hassan Afzaal Aitazaz A. Farooque Farhat Abbas Bishnu Acharya Travis Esau 2020-02-01T00:00:00Z https://doi.org/10.3390/app10051621 https://doaj.org/article/417406d8a7c34a549a923b5d5d6e2e3c EN eng MDPI AG https://www.mdpi.com/2076-3417/10/5/1621 https://doaj.org/toc/2076-3417 2076-3417 doi:10.3390/app10051621 https://doaj.org/article/417406d8a7c34a549a923b5d5d6e2e3c Applied Sciences, Vol 10, Iss 5, p 1621 (2020) recurrent neural networks deep learning irrigation scheduling penman–monteith physical hydrology components water cycle budgeting Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 article 2020 ftdoajarticles https://doi.org/10.3390/app10051621 2022-12-31T03:41:12Z Accurate estimation of reference evapotranspiration (ETo) provides useful information for water resource management and sustainable agriculture. This study estimates ETo with recurrent neural networks (RNNs), namely long short-term memory (LSTM) and bidirectional LSTM. Four representative meteorological sites (North Cape, Summerside, Harrington, and Saint Peters) were selected across Prince Edward Island (PEI), Canada to form a PEI dataset from mean values of the four sites’ climatic variables for capturing climatic variability from all parts of the province. Based on subset regression analysis, the highest contributing climatic variables, namely maximum air temperature and relative humidity, were selected as input variables for RNNs’ training (2011−2015) and testing (2016−2017) runs. The results suggested that the LSTM and bidirectional LSTM are suitable methods to accurately (R 2 > 0.90) estimate ETo for all sites except Harrington. Testing period (2016−2017) root mean square errors were recorded in range of 0.38−0.58 mm/day for all sites. No major differences were observed in accuracy of LSTM and bidirectional LSTM. Another objective of this study was to highlight the potential gap between ET O and rainfall for assessing agriculture sustainability in Prince Edward Island. Analyses of the data highlighted that the cumulative ETo surpassed the cumulative rainfall potentially affecting yield of major crops in the island. Therefore, agriculture sustainability requires viable options such as supplemental irrigation to replenish the crop water requirements as and when needed. Article in Journal/Newspaper North Cape Prince Edward Island Directory of Open Access Journals: DOAJ Articles Canada North Cape ENVELOPE(165.700,165.700,-70.650,-70.650) Applied Sciences 10 5 1621 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
recurrent neural networks deep learning irrigation scheduling penman–monteith physical hydrology components water cycle budgeting Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 |
spellingShingle |
recurrent neural networks deep learning irrigation scheduling penman–monteith physical hydrology components water cycle budgeting Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Hassan Afzaal Aitazaz A. Farooque Farhat Abbas Bishnu Acharya Travis Esau Computation of Evapotranspiration with Artificial Intelligence for Precision Water Resource Management |
topic_facet |
recurrent neural networks deep learning irrigation scheduling penman–monteith physical hydrology components water cycle budgeting Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 |
description |
Accurate estimation of reference evapotranspiration (ETo) provides useful information for water resource management and sustainable agriculture. This study estimates ETo with recurrent neural networks (RNNs), namely long short-term memory (LSTM) and bidirectional LSTM. Four representative meteorological sites (North Cape, Summerside, Harrington, and Saint Peters) were selected across Prince Edward Island (PEI), Canada to form a PEI dataset from mean values of the four sites’ climatic variables for capturing climatic variability from all parts of the province. Based on subset regression analysis, the highest contributing climatic variables, namely maximum air temperature and relative humidity, were selected as input variables for RNNs’ training (2011−2015) and testing (2016−2017) runs. The results suggested that the LSTM and bidirectional LSTM are suitable methods to accurately (R 2 > 0.90) estimate ETo for all sites except Harrington. Testing period (2016−2017) root mean square errors were recorded in range of 0.38−0.58 mm/day for all sites. No major differences were observed in accuracy of LSTM and bidirectional LSTM. Another objective of this study was to highlight the potential gap between ET O and rainfall for assessing agriculture sustainability in Prince Edward Island. Analyses of the data highlighted that the cumulative ETo surpassed the cumulative rainfall potentially affecting yield of major crops in the island. Therefore, agriculture sustainability requires viable options such as supplemental irrigation to replenish the crop water requirements as and when needed. |
format |
Article in Journal/Newspaper |
author |
Hassan Afzaal Aitazaz A. Farooque Farhat Abbas Bishnu Acharya Travis Esau |
author_facet |
Hassan Afzaal Aitazaz A. Farooque Farhat Abbas Bishnu Acharya Travis Esau |
author_sort |
Hassan Afzaal |
title |
Computation of Evapotranspiration with Artificial Intelligence for Precision Water Resource Management |
title_short |
Computation of Evapotranspiration with Artificial Intelligence for Precision Water Resource Management |
title_full |
Computation of Evapotranspiration with Artificial Intelligence for Precision Water Resource Management |
title_fullStr |
Computation of Evapotranspiration with Artificial Intelligence for Precision Water Resource Management |
title_full_unstemmed |
Computation of Evapotranspiration with Artificial Intelligence for Precision Water Resource Management |
title_sort |
computation of evapotranspiration with artificial intelligence for precision water resource management |
publisher |
MDPI AG |
publishDate |
2020 |
url |
https://doi.org/10.3390/app10051621 https://doaj.org/article/417406d8a7c34a549a923b5d5d6e2e3c |
long_lat |
ENVELOPE(165.700,165.700,-70.650,-70.650) |
geographic |
Canada North Cape |
geographic_facet |
Canada North Cape |
genre |
North Cape Prince Edward Island |
genre_facet |
North Cape Prince Edward Island |
op_source |
Applied Sciences, Vol 10, Iss 5, p 1621 (2020) |
op_relation |
https://www.mdpi.com/2076-3417/10/5/1621 https://doaj.org/toc/2076-3417 2076-3417 doi:10.3390/app10051621 https://doaj.org/article/417406d8a7c34a549a923b5d5d6e2e3c |
op_doi |
https://doi.org/10.3390/app10051621 |
container_title |
Applied Sciences |
container_volume |
10 |
container_issue |
5 |
container_start_page |
1621 |
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1766138210442805248 |