Daily soil temperature prediction using hybrid deep learning and SHAP for sustainable soil management
The thermal conditions within soil layers represent essential information across diverse fields including sustainable agriculture, power generation, biological research, ecological studies, forest management, and thermal energy systems. Therefore, this study assessed prediction capabilities for subt...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Engineering Applications of Computational Fluid Mechanics |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19942060.2025.2541686 |
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| author | Meysam Alizamir Kaywan Othman Ahmed Salim Heddam Sungwon Kim Jeong Eun Lee |
| author_facet | Meysam Alizamir Kaywan Othman Ahmed Salim Heddam Sungwon Kim Jeong Eun Lee |
| author_sort | Meysam Alizamir |
| collection | DOAJ |
| description | The thermal conditions within soil layers represent essential information across diverse fields including sustainable agriculture, power generation, biological research, ecological studies, forest management, and thermal energy systems. Therefore, this study assessed prediction capabilities for subterranean thermal measurements using different advanced deep learning models at six distinct depth points across dual climate monitoring locations, Darbandikhan and Chamchamal within Iraq's Kurdistan territory. Furthermore, various configurations of the input variables were examined across seven distinct observational scenarios to identify the most significant predictive factors. Based on the results of this study, various deep learning models demonstrated optimal performance for soil temperature prediction at different depths across the two stations. At Chamchamal station, the hybrid deep learning model that combines bidirectional gated recurrent unit (BiGRU) and convolutional neural network (CNN), denoted as BiGRU-CNN achieved the best result for the 05 cm depth (RMSE = 1.298°C), while the hybrid model based on gated recurrent unit (GRU) and convolutional neural network (CNN), referred to as GRU–CNN yielded the best performance at 10 cm (RMSE = 1.333°C). BiLSTM-CNN provided the most accurate predictions at 20 cm (RMSE = 1.489°C), and BiGRU-CNN generated superior results at 30 cm (RMSE = 1.267°C). For deeper soil layers, GRU-CNN delivered the most accurate results at 50 cm (RMSE = 1.058°C), and LSTM-CNN performed the best at 100 cm (RMSE = 1.171°C). Additionally, at Darbandikhan station, BiLSTM-CNN generated the most accurate predictions at 50 cm depth (RMSE = 1.506°C). The results indicate that the proposed methodologies offer viable solutions for soil temperature forecasting when utilizing the meteorological input variables. These models demonstrate substantial potential as reliable tools for accurately estimating subsurface thermal conditions based on available parameters. |
| format | Article |
| id | doaj-art-91328f3cd75c45e3b5b8fc1850792b45 |
| institution | Kabale University |
| issn | 1994-2060 1997-003X |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Engineering Applications of Computational Fluid Mechanics |
| spelling | doaj-art-91328f3cd75c45e3b5b8fc1850792b452025-08-20T03:41:53ZengTaylor & Francis GroupEngineering Applications of Computational Fluid Mechanics1994-20601997-003X2025-12-0119110.1080/19942060.2025.2541686Daily soil temperature prediction using hybrid deep learning and SHAP for sustainable soil managementMeysam Alizamir0Kaywan Othman Ahmed1Salim Heddam2Sungwon Kim3Jeong Eun Lee4Institute of Research and Development, Duy Tan University, Da Nang, VietnamCivil Engineering Department, Tishk International University, Sulaimani, IraqFaculty of Science, Agronomy Department, Hydraulics Division, Skikda, AlgeriaDepartment of Railroad Construction and Safety Engineering, Dongyang University, Yeongju, Republic of KoreaDepartment of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology (KICT), Goyang-si, Republic of KoreaThe thermal conditions within soil layers represent essential information across diverse fields including sustainable agriculture, power generation, biological research, ecological studies, forest management, and thermal energy systems. Therefore, this study assessed prediction capabilities for subterranean thermal measurements using different advanced deep learning models at six distinct depth points across dual climate monitoring locations, Darbandikhan and Chamchamal within Iraq's Kurdistan territory. Furthermore, various configurations of the input variables were examined across seven distinct observational scenarios to identify the most significant predictive factors. Based on the results of this study, various deep learning models demonstrated optimal performance for soil temperature prediction at different depths across the two stations. At Chamchamal station, the hybrid deep learning model that combines bidirectional gated recurrent unit (BiGRU) and convolutional neural network (CNN), denoted as BiGRU-CNN achieved the best result for the 05 cm depth (RMSE = 1.298°C), while the hybrid model based on gated recurrent unit (GRU) and convolutional neural network (CNN), referred to as GRU–CNN yielded the best performance at 10 cm (RMSE = 1.333°C). BiLSTM-CNN provided the most accurate predictions at 20 cm (RMSE = 1.489°C), and BiGRU-CNN generated superior results at 30 cm (RMSE = 1.267°C). For deeper soil layers, GRU-CNN delivered the most accurate results at 50 cm (RMSE = 1.058°C), and LSTM-CNN performed the best at 100 cm (RMSE = 1.171°C). Additionally, at Darbandikhan station, BiLSTM-CNN generated the most accurate predictions at 50 cm depth (RMSE = 1.506°C). The results indicate that the proposed methodologies offer viable solutions for soil temperature forecasting when utilizing the meteorological input variables. These models demonstrate substantial potential as reliable tools for accurately estimating subsurface thermal conditions based on available parameters.https://www.tandfonline.com/doi/10.1080/19942060.2025.2541686Soil temperaturedeep learningGRU-CNNLSTM-CNNSHAP |
| spellingShingle | Meysam Alizamir Kaywan Othman Ahmed Salim Heddam Sungwon Kim Jeong Eun Lee Daily soil temperature prediction using hybrid deep learning and SHAP for sustainable soil management Engineering Applications of Computational Fluid Mechanics Soil temperature deep learning GRU-CNN LSTM-CNN SHAP |
| title | Daily soil temperature prediction using hybrid deep learning and SHAP for sustainable soil management |
| title_full | Daily soil temperature prediction using hybrid deep learning and SHAP for sustainable soil management |
| title_fullStr | Daily soil temperature prediction using hybrid deep learning and SHAP for sustainable soil management |
| title_full_unstemmed | Daily soil temperature prediction using hybrid deep learning and SHAP for sustainable soil management |
| title_short | Daily soil temperature prediction using hybrid deep learning and SHAP for sustainable soil management |
| title_sort | daily soil temperature prediction using hybrid deep learning and shap for sustainable soil management |
| topic | Soil temperature deep learning GRU-CNN LSTM-CNN SHAP |
| url | https://www.tandfonline.com/doi/10.1080/19942060.2025.2541686 |
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