Simulating and predicting soil water dynamics using three models for the Taihu Lake region of China
Drought stress under a changing climate can significantly affect agricultural production. Simulation of soil water dynamics in field conditions becomes necessary to understand changes of soil water conditions to develop irrigation guidelines. In this study, three models including Auto-Regressive Int...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IWA Publishing
2022-04-01
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| Series: | Water Supply |
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| Online Access: | http://ws.iwaponline.com/content/22/4/4030 |
| _version_ | 1828748802932080640 |
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| author | Can Chen Qing Lv Qian Tang |
| author_facet | Can Chen Qing Lv Qian Tang |
| author_sort | Can Chen |
| collection | DOAJ |
| description | Drought stress under a changing climate can significantly affect agricultural production. Simulation of soil water dynamics in field conditions becomes necessary to understand changes of soil water conditions to develop irrigation guidelines. In this study, three models including Auto-Regressive Integrated Moving Average (ARIMA), Back-Propagation Artificial Neural Network (BP-ANN), and Least Squares Support Vector Machine (LS-SVM) were used to simulate the soil water content in the 0–14 cm and 14–33 cm soil layers across the Taihu Lake region of China. Rainfall, evaporation, temperature, humidity and wind speed that affect soil water content changes were considered in the BP-ANN and LS-SVM, but not in ARIMA. The results showed that the variability of soil water content in the 0–14 cm soil layer was greater than that in 14–33 cm. Correlation coefficients (r) of soil water content between simulations and observations were highest (0.9827) using LS-SVM in the 14–33 cm soil layer, while they were the lowest (0.7019) using ARIMA in the 0–14 cm soil layer; but no significant difference in r values was observed between the two soil layers with the BP-ANN model. Compared with the other two models, the LS-SVM model seems to be more accurate for forecasting the dynamics of soil moisture. The results suggested that agro-climatic data can be used to predict the severity of soil drought stress and provide guidance for irrigation to increase crop production in the Taihu Lake region of China. HIGHLIGHTS
To understand the dynamics of soil water in the Taihu Lake region of China.;
The simulation accuracy of LS-SVM was the highest.;
To predict the trend of soil water content in the study area.; |
| first_indexed | 2024-04-14T06:59:16Z |
| format | Article |
| id | doaj.art-935e0991dfaa4a4b92b5afca67f5b68b |
| institution | Directory Open Access Journal |
| issn | 1606-9749 1607-0798 |
| language | English |
| last_indexed | 2024-04-14T06:59:16Z |
| publishDate | 2022-04-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Water Supply |
| spelling | doaj.art-935e0991dfaa4a4b92b5afca67f5b68b2022-12-22T02:06:48ZengIWA PublishingWater Supply1606-97491607-07982022-04-012244030404210.2166/ws.2022.032032Simulating and predicting soil water dynamics using three models for the Taihu Lake region of ChinaCan Chen0Qing Lv1Qian Tang2 College of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China College of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China College of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China Drought stress under a changing climate can significantly affect agricultural production. Simulation of soil water dynamics in field conditions becomes necessary to understand changes of soil water conditions to develop irrigation guidelines. In this study, three models including Auto-Regressive Integrated Moving Average (ARIMA), Back-Propagation Artificial Neural Network (BP-ANN), and Least Squares Support Vector Machine (LS-SVM) were used to simulate the soil water content in the 0–14 cm and 14–33 cm soil layers across the Taihu Lake region of China. Rainfall, evaporation, temperature, humidity and wind speed that affect soil water content changes were considered in the BP-ANN and LS-SVM, but not in ARIMA. The results showed that the variability of soil water content in the 0–14 cm soil layer was greater than that in 14–33 cm. Correlation coefficients (r) of soil water content between simulations and observations were highest (0.9827) using LS-SVM in the 14–33 cm soil layer, while they were the lowest (0.7019) using ARIMA in the 0–14 cm soil layer; but no significant difference in r values was observed between the two soil layers with the BP-ANN model. Compared with the other two models, the LS-SVM model seems to be more accurate for forecasting the dynamics of soil moisture. The results suggested that agro-climatic data can be used to predict the severity of soil drought stress and provide guidance for irrigation to increase crop production in the Taihu Lake region of China. HIGHLIGHTS To understand the dynamics of soil water in the Taihu Lake region of China.; The simulation accuracy of LS-SVM was the highest.; To predict the trend of soil water content in the study area.;http://ws.iwaponline.com/content/22/4/4030arima modelbp-ann modells-svm modelsimulating and predictingsoil water dynamicstaihu lake region |
| spellingShingle | Can Chen Qing Lv Qian Tang Simulating and predicting soil water dynamics using three models for the Taihu Lake region of China Water Supply arima model bp-ann model ls-svm model simulating and predicting soil water dynamics taihu lake region |
| title | Simulating and predicting soil water dynamics using three models for the Taihu Lake region of China |
| title_full | Simulating and predicting soil water dynamics using three models for the Taihu Lake region of China |
| title_fullStr | Simulating and predicting soil water dynamics using three models for the Taihu Lake region of China |
| title_full_unstemmed | Simulating and predicting soil water dynamics using three models for the Taihu Lake region of China |
| title_short | Simulating and predicting soil water dynamics using three models for the Taihu Lake region of China |
| title_sort | simulating and predicting soil water dynamics using three models for the taihu lake region of china |
| topic | arima model bp-ann model ls-svm model simulating and predicting soil water dynamics taihu lake region |
| url | http://ws.iwaponline.com/content/22/4/4030 |
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