Can ensemble machine learning be used to predict the groundwater level dynamics of farmland under future climate: a 10-year study on Huaibei Plain
Abstract Accurate and simple prediction of farmland groundwater level (GWL) is an important aspect of agricultural water management. A farmland GWL prediction model, GWPRE, was developed that integrates four machine learning (ML) models (support vector machine regression, random forest, multiple perceptions, and the stacking ensemble model) with weather forecasts. Based on the GWL and meteorological data of five monitoring wells (N1, N2, N3, N4, and N5) in Huaibei plain from 2010 to 2020, the feasibility of predicting GWL by meteorological factors and ML algorithm was tested. In addition, the stacking ensemble model and future meteorological data after Bayesian model averaging were introduced for the first time to predict GWL under future climate conditions. The results showed that GWL showed an increasing trend in the past decade, but it will decrease in the future. The performance of the stacking ensemble model was better than that of any single ML model, with RMSE reduced by 4.26 ~ 96.97% and the running time reduced by 49.25 ~ 99.40%. GWL was most sensitive to rainfall, and the sensitivity index ranged from 0.2547 to 0.4039. The fluctuation range of GWL of N1, N2, and N3 was 1.5 ~ 2.5 m in the next decade. Due to the possible high rainfall, the GWL decreased in 2024 under RCP 2.6 and 2026 under RCP 8.5. It is worth noting that although the stacking ensemble model can improve the accuracy, it is not always the best among ML models in terms of portability. Nevertheless, the stacking ensemble model was recommended for GWL prediction under climate change..
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Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:29 |
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| Enthalten in: |
Environmental science and pollution research - 29(2022), 29 vom: 08. Feb., Seite 44653-44667 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Jiang, Zewei [VerfasserIn] |
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| Links: |
Volltext [lizenzpflichtig] |
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| Themen: |
Climate change |
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| Anmerkungen: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
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| doi: |
10.1007/s11356-022-18809-8 |
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| 520 | |a Abstract Accurate and simple prediction of farmland groundwater level (GWL) is an important aspect of agricultural water management. A farmland GWL prediction model, GWPRE, was developed that integrates four machine learning (ML) models (support vector machine regression, random forest, multiple perceptions, and the stacking ensemble model) with weather forecasts. Based on the GWL and meteorological data of five monitoring wells (N1, N2, N3, N4, and N5) in Huaibei plain from 2010 to 2020, the feasibility of predicting GWL by meteorological factors and ML algorithm was tested. In addition, the stacking ensemble model and future meteorological data after Bayesian model averaging were introduced for the first time to predict GWL under future climate conditions. The results showed that GWL showed an increasing trend in the past decade, but it will decrease in the future. The performance of the stacking ensemble model was better than that of any single ML model, with RMSE reduced by 4.26 ~ 96.97% and the running time reduced by 49.25 ~ 99.40%. GWL was most sensitive to rainfall, and the sensitivity index ranged from 0.2547 to 0.4039. The fluctuation range of GWL of N1, N2, and N3 was 1.5 ~ 2.5 m in the next decade. Due to the possible high rainfall, the GWL decreased in 2024 under RCP 2.6 and 2026 under RCP 8.5. It is worth noting that although the stacking ensemble model can improve the accuracy, it is not always the best among ML models in terms of portability. Nevertheless, the stacking ensemble model was recommended for GWL prediction under climate change. | ||
| 650 | 4 | |a Groundwater level | |
| 650 | 4 | |a Ensemble machine learning | |
| 650 | 4 | |a Climate change | |
| 650 | 4 | |a Farmland | |
| 650 | 4 | |a Water management | |
| 700 | 1 | |a Yang, Shihong |4 aut | |
| 700 | 1 | |a Liu, Zhenyang |4 aut | |
| 700 | 1 | |a Xu, Yi |4 aut | |
| 700 | 1 | |a Shen, Tao |4 aut | |
| 700 | 1 | |a Qi, Suting |4 aut | |
| 700 | 1 | |a Pang, Qingqing |4 aut | |
| 700 | 1 | |a Xu, Junzeng |4 aut | |
| 700 | 1 | |a Liu, Fangping |4 aut | |
| 700 | 1 | |a Xu, Tao |4 aut | |
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