Context Aware Evapotranspiration (ETs) for Saline Soils Reclamation
Accurate Evapotranspiration for saline soils (ETs) is important as well as challenging for the reclamation of saline soils through an effective leaching process. Evapotranspiration (ET) by FAO-56 Penman-Monteith standard method is complex, especially for saline soils. Moreover, existing studies focu...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9887960/ |
| _version_ | 1811250330854227968 |
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| author | Arfat Ahmad Khan Muhammad Asif Nauman Rab Nawaz Bashir Rashid Jahangir Roobaea Alroobaea Ahmed Binmahfoudh Majed Alsafyani Chitapong Wechtaisong |
| author_facet | Arfat Ahmad Khan Muhammad Asif Nauman Rab Nawaz Bashir Rashid Jahangir Roobaea Alroobaea Ahmed Binmahfoudh Majed Alsafyani Chitapong Wechtaisong |
| author_sort | Arfat Ahmad Khan |
| collection | DOAJ |
| description | Accurate Evapotranspiration for saline soils (ETs) is important as well as challenging for the reclamation of saline soils through an effective leaching process. Evapotranspiration (ET) by FAO-56 Penman-Monteith standard method is complex, especially for saline soils. Moreover, existing studies focus on the use of the Internet of Things (IoT) and machine learning-enabled smart and precision irrigation water recommendation systems along with the ET estimation by limited parameters. The ETs for saline soils are also equally important for the reclamation of saline soils, which is ignored by the existing literature. The study proposed IoT and machine leaching-based architecture of context-aware monthly ETs estimations for saline soil reclamation with the effective leaching process. The IoT-enabled crop field contexts in terms of crop field temperature, soil salinity, and irrigation water salinity are used as input features to the Long Short-Term Memory (LSTM) and ensembled LSTM models for monthly ETs predictions. The performance of the proposed solution is observed in terms of the accuracy of the machine learning models along with the comparison against the FAO-56 PM-based standard method. The implementation of the proposed solution reveals that the ensembled LSTM-based approach for ETs is more accurate as compared to the LSTM model with accuracies of 92 and 90% for the training and validation datasets, respectively. The predictions made by the ensembled LSTM are more in line with the FAO-56 PM-based method with a Pearson correlation of 0.916 as compared to LSTM models. The implementation of the proposed solution in real-time environments reveals that the proposed solution is more effective in reducing the soil salinity as compared to the traditional method. |
| first_indexed | 2024-04-12T16:02:06Z |
| format | Article |
| id | doaj.art-0007462a44e7439082cfd69074de7e22 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T16:02:06Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-0007462a44e7439082cfd69074de7e222022-12-22T03:26:10ZengIEEEIEEE Access2169-35362022-01-011011005011006310.1109/ACCESS.2022.32060099887960Context Aware Evapotranspiration (ETs) for Saline Soils ReclamationArfat Ahmad Khan0Muhammad Asif Nauman1Rab Nawaz Bashir2https://orcid.org/0000-0001-7409-1775Rashid Jahangir3Roobaea Alroobaea4https://orcid.org/0000-0001-8199-5852Ahmed Binmahfoudh5Majed Alsafyani6Chitapong Wechtaisong7https://orcid.org/0000-0002-4143-2227College of Computing, Khon Kaen University, Khon Kaen, ThailandDepartment of Computer Science, University of Engineering and Technology Lahore, Lahore, PakistanDepartment of Computer Science, COMSATS University Islamabad, Vehari Campus, Vehari, PakistanDepartment of Computer Science, COMSATS University Islamabad, Vehari Campus, Vehari, PakistanDepartment of Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi ArabiaDepartment of Computer Engineering, College of Computers and Information Technology, Taif University, Taif, Saudi ArabiaDepartment of Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi ArabiaSchool of Telecommunication Engineering, Suranaree University of Technology, Nakhon Ratchasima, ThailandAccurate Evapotranspiration for saline soils (ETs) is important as well as challenging for the reclamation of saline soils through an effective leaching process. Evapotranspiration (ET) by FAO-56 Penman-Monteith standard method is complex, especially for saline soils. Moreover, existing studies focus on the use of the Internet of Things (IoT) and machine learning-enabled smart and precision irrigation water recommendation systems along with the ET estimation by limited parameters. The ETs for saline soils are also equally important for the reclamation of saline soils, which is ignored by the existing literature. The study proposed IoT and machine leaching-based architecture of context-aware monthly ETs estimations for saline soil reclamation with the effective leaching process. The IoT-enabled crop field contexts in terms of crop field temperature, soil salinity, and irrigation water salinity are used as input features to the Long Short-Term Memory (LSTM) and ensembled LSTM models for monthly ETs predictions. The performance of the proposed solution is observed in terms of the accuracy of the machine learning models along with the comparison against the FAO-56 PM-based standard method. The implementation of the proposed solution reveals that the ensembled LSTM-based approach for ETs is more accurate as compared to the LSTM model with accuracies of 92 and 90% for the training and validation datasets, respectively. The predictions made by the ensembled LSTM are more in line with the FAO-56 PM-based method with a Pearson correlation of 0.916 as compared to LSTM models. The implementation of the proposed solution in real-time environments reveals that the proposed solution is more effective in reducing the soil salinity as compared to the traditional method.https://ieeexplore.ieee.org/document/9887960/Evapotranspiration (ET)evapotranspiration for saline soils (ETs)saline soillong short-term memory model (LSTM)ensembled LSTMFAO-56 Penman-Monteith |
| spellingShingle | Arfat Ahmad Khan Muhammad Asif Nauman Rab Nawaz Bashir Rashid Jahangir Roobaea Alroobaea Ahmed Binmahfoudh Majed Alsafyani Chitapong Wechtaisong Context Aware Evapotranspiration (ETs) for Saline Soils Reclamation IEEE Access Evapotranspiration (ET) evapotranspiration for saline soils (ETs) saline soil long short-term memory model (LSTM) ensembled LSTM FAO-56 Penman-Monteith |
| title | Context Aware Evapotranspiration (ETs) for Saline Soils Reclamation |
| title_full | Context Aware Evapotranspiration (ETs) for Saline Soils Reclamation |
| title_fullStr | Context Aware Evapotranspiration (ETs) for Saline Soils Reclamation |
| title_full_unstemmed | Context Aware Evapotranspiration (ETs) for Saline Soils Reclamation |
| title_short | Context Aware Evapotranspiration (ETs) for Saline Soils Reclamation |
| title_sort | context aware evapotranspiration ets for saline soils reclamation |
| topic | Evapotranspiration (ET) evapotranspiration for saline soils (ETs) saline soil long short-term memory model (LSTM) ensembled LSTM FAO-56 Penman-Monteith |
| url | https://ieeexplore.ieee.org/document/9887960/ |
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