Optimization of Data-Driven Soil Temperature Forecast—The First Model in Bangladesh
Soil temperature patterns are of great importance for any agro-based economy like Bangladesh since they significantly affect biological, chemical, and physical processes that take place in the soil. Unfortunately, there have been no forecast studies on soil temperature in Bangladesh until now. In th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/23/12616 |
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| author | Lipon Chandra Das Zhihua Zhang M. James C. Crabbe |
| author_facet | Lipon Chandra Das Zhihua Zhang M. James C. Crabbe |
| author_sort | Lipon Chandra Das |
| collection | DOAJ |
| description | Soil temperature patterns are of great importance for any agro-based economy like Bangladesh since they significantly affect biological, chemical, and physical processes that take place in the soil. Unfortunately, there have been no forecast studies on soil temperature in Bangladesh until now. In this article, we used five tree-based models (decision tree, random forest, gradient boosting tree, a hybrid of decision tree and gradient boosting tree, and a hybrid of random forest and gradient boosting tree) to mine strong links among different meteorological factors and soil temperature at different time window sizes. We found that a hybrid of random forest and gradient boosting tree with all the meteorological factors and a five-day time window is optimal for forecasting soil temperature at depths of 10 cm and 30 cm for all lead times (one, three, or five days), whereas the random forest with the same input scenario and time window is optimal for forecasting soil temperature at a depth of 50 cm for long lead times (five days). Since our study includes the first soil temperature forecast model in Bangladesh, it provides valuable insights for agricultural soil management, fertilizer application, and water resource optimization in Bangladesh, as well as in other South Asian countries that share the same climate patterns as Bangladesh. |
| first_indexed | 2024-03-09T01:55:47Z |
| format | Article |
| id | doaj.art-7754aa9a50d847d19e04f13290259e38 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T01:55:47Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-7754aa9a50d847d19e04f13290259e382023-12-08T15:11:10ZengMDPI AGApplied Sciences2076-34172023-11-0113231261610.3390/app132312616Optimization of Data-Driven Soil Temperature Forecast—The First Model in BangladeshLipon Chandra Das0Zhihua Zhang1M. James C. Crabbe2School of Mathematics, Shandong University, Jinan 250100, ChinaSchool of Mathematics, Shandong University, Jinan 250100, ChinaWolfson College, Oxford University, Oxford OX1 1NQ, UKSoil temperature patterns are of great importance for any agro-based economy like Bangladesh since they significantly affect biological, chemical, and physical processes that take place in the soil. Unfortunately, there have been no forecast studies on soil temperature in Bangladesh until now. In this article, we used five tree-based models (decision tree, random forest, gradient boosting tree, a hybrid of decision tree and gradient boosting tree, and a hybrid of random forest and gradient boosting tree) to mine strong links among different meteorological factors and soil temperature at different time window sizes. We found that a hybrid of random forest and gradient boosting tree with all the meteorological factors and a five-day time window is optimal for forecasting soil temperature at depths of 10 cm and 30 cm for all lead times (one, three, or five days), whereas the random forest with the same input scenario and time window is optimal for forecasting soil temperature at a depth of 50 cm for long lead times (five days). Since our study includes the first soil temperature forecast model in Bangladesh, it provides valuable insights for agricultural soil management, fertilizer application, and water resource optimization in Bangladesh, as well as in other South Asian countries that share the same climate patterns as Bangladesh.https://www.mdpi.com/2076-3417/13/23/12616soil temperature forecasthybrid modelsoptimizationBangladesh |
| spellingShingle | Lipon Chandra Das Zhihua Zhang M. James C. Crabbe Optimization of Data-Driven Soil Temperature Forecast—The First Model in Bangladesh Applied Sciences soil temperature forecast hybrid models optimization Bangladesh |
| title | Optimization of Data-Driven Soil Temperature Forecast—The First Model in Bangladesh |
| title_full | Optimization of Data-Driven Soil Temperature Forecast—The First Model in Bangladesh |
| title_fullStr | Optimization of Data-Driven Soil Temperature Forecast—The First Model in Bangladesh |
| title_full_unstemmed | Optimization of Data-Driven Soil Temperature Forecast—The First Model in Bangladesh |
| title_short | Optimization of Data-Driven Soil Temperature Forecast—The First Model in Bangladesh |
| title_sort | optimization of data driven soil temperature forecast the first model in bangladesh |
| topic | soil temperature forecast hybrid models optimization Bangladesh |
| url | https://www.mdpi.com/2076-3417/13/23/12616 |
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