Evaluation and Prediction of Agricultural Water Use Efficiency in the Jianghan Plain Based on the Tent-SSA-BPNN Model
The Jianghan Plain (JHP) is a key agricultural area in China where efficient agricultural water use (AWUE) is vital for sustainable water management, food security, environmental sustainability, and economic growth. This study introduces a novel AWUE prediction model for the JHP, combining a BP neur...
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MDPI AG
2025-01-01
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| author | Tianshu Shao Xiangdong Xu Yuelong Su |
| author_facet | Tianshu Shao Xiangdong Xu Yuelong Su |
| author_sort | Tianshu Shao |
| collection | DOAJ |
| description | The Jianghan Plain (JHP) is a key agricultural area in China where efficient agricultural water use (AWUE) is vital for sustainable water management, food security, environmental sustainability, and economic growth. This study introduces a novel AWUE prediction model for the JHP, combining a BP neural network with the Sparrow Search Algorithm (SSA) and an improved Tent Mixing Algorithm (Tent-SSA-BPNN). This hybrid model addresses the limitations of traditional methods by enhancing AWUE forecast accuracy and stability. By integrating historical AWUE data and environmental factors, the model provides a detailed understanding of AWUE’s spatial and temporal variations. Compared to traditional BP neural networks and other methods, the Tent-SSA-BPNN model significantly improves prediction accuracy and stability, achieving an accuracy (ACC) of 96.218%, a root mean square error (RMSE) of 0.952, and a coefficient of determination (R<sup>2</sup>) of 0.9939, surpassing previous models. The results show that (1) from 2010 to 2022, the average AWUE in the JHP fluctuated within a specific range, exhibiting a decrease of 0.69%, with significant differences in the spatial and temporal distributions across various cities; (2) the accuracy (ACC) of the Tent-SSA-BPNN prediction model was 96.218%, the root mean square error (RMSE) was 0.952, and the coefficient of determination (R²) value was 0.9939. (3) Compared with those of the preoptimization model, the ACC, RMSE, and R² values of the Tent-SSA-BPNN model significantly improved in terms of accuracy and stability, clearly indicating the efficacy of the optimization. (4) The prediction results reveal that the proportion of agricultural water consumption has a significant impact on AWUE. These results provide actionable insights for optimizing water resource allocation, particularly in water-scarce regions, and guide policymakers in enhancing agricultural water management strategies, supporting sustainable agricultural development. |
| format | Article |
| id | doaj-art-09e356cd727f4f97bfdf0fc7165312da |
| institution | Kabale University |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Agriculture |
| spelling | doaj-art-09e356cd727f4f97bfdf0fc7165312da2025-01-24T13:15:52ZengMDPI AGAgriculture2077-04722025-01-0115214010.3390/agriculture15020140Evaluation and Prediction of Agricultural Water Use Efficiency in the Jianghan Plain Based on the Tent-SSA-BPNN ModelTianshu Shao0Xiangdong Xu1Yuelong Su2School of Innovation and Entrepreneurship, Zhejiang University of Finance and Economics Dongfang College, Haining 314408, ChinaSchool of Foreign Languages, Zhejiang University of Finance and Economics Dongfang College, Haining 314408, ChinaThe College of Urban & Environmental Sciences, Central China Normal University, Wuhan 430079, ChinaThe Jianghan Plain (JHP) is a key agricultural area in China where efficient agricultural water use (AWUE) is vital for sustainable water management, food security, environmental sustainability, and economic growth. This study introduces a novel AWUE prediction model for the JHP, combining a BP neural network with the Sparrow Search Algorithm (SSA) and an improved Tent Mixing Algorithm (Tent-SSA-BPNN). This hybrid model addresses the limitations of traditional methods by enhancing AWUE forecast accuracy and stability. By integrating historical AWUE data and environmental factors, the model provides a detailed understanding of AWUE’s spatial and temporal variations. Compared to traditional BP neural networks and other methods, the Tent-SSA-BPNN model significantly improves prediction accuracy and stability, achieving an accuracy (ACC) of 96.218%, a root mean square error (RMSE) of 0.952, and a coefficient of determination (R<sup>2</sup>) of 0.9939, surpassing previous models. The results show that (1) from 2010 to 2022, the average AWUE in the JHP fluctuated within a specific range, exhibiting a decrease of 0.69%, with significant differences in the spatial and temporal distributions across various cities; (2) the accuracy (ACC) of the Tent-SSA-BPNN prediction model was 96.218%, the root mean square error (RMSE) was 0.952, and the coefficient of determination (R²) value was 0.9939. (3) Compared with those of the preoptimization model, the ACC, RMSE, and R² values of the Tent-SSA-BPNN model significantly improved in terms of accuracy and stability, clearly indicating the efficacy of the optimization. (4) The prediction results reveal that the proportion of agricultural water consumption has a significant impact on AWUE. These results provide actionable insights for optimizing water resource allocation, particularly in water-scarce regions, and guide policymakers in enhancing agricultural water management strategies, supporting sustainable agricultural development.https://www.mdpi.com/2077-0472/15/2/140Jianghan Plainagricultural water use efficiencysuper-efficient SBM equationtent chaos mappingBP neural network predictionSparrow Search Algorithm |
| spellingShingle | Tianshu Shao Xiangdong Xu Yuelong Su Evaluation and Prediction of Agricultural Water Use Efficiency in the Jianghan Plain Based on the Tent-SSA-BPNN Model Agriculture Jianghan Plain agricultural water use efficiency super-efficient SBM equation tent chaos mapping BP neural network prediction Sparrow Search Algorithm |
| title | Evaluation and Prediction of Agricultural Water Use Efficiency in the Jianghan Plain Based on the Tent-SSA-BPNN Model |
| title_full | Evaluation and Prediction of Agricultural Water Use Efficiency in the Jianghan Plain Based on the Tent-SSA-BPNN Model |
| title_fullStr | Evaluation and Prediction of Agricultural Water Use Efficiency in the Jianghan Plain Based on the Tent-SSA-BPNN Model |
| title_full_unstemmed | Evaluation and Prediction of Agricultural Water Use Efficiency in the Jianghan Plain Based on the Tent-SSA-BPNN Model |
| title_short | Evaluation and Prediction of Agricultural Water Use Efficiency in the Jianghan Plain Based on the Tent-SSA-BPNN Model |
| title_sort | evaluation and prediction of agricultural water use efficiency in the jianghan plain based on the tent ssa bpnn model |
| topic | Jianghan Plain agricultural water use efficiency super-efficient SBM equation tent chaos mapping BP neural network prediction Sparrow Search Algorithm |
| url | https://www.mdpi.com/2077-0472/15/2/140 |
| work_keys_str_mv | AT tianshushao evaluationandpredictionofagriculturalwateruseefficiencyinthejianghanplainbasedonthetentssabpnnmodel AT xiangdongxu evaluationandpredictionofagriculturalwateruseefficiencyinthejianghanplainbasedonthetentssabpnnmodel AT yuelongsu evaluationandpredictionofagriculturalwateruseefficiencyinthejianghanplainbasedonthetentssabpnnmodel |