Evaluation and Prediction of Agricultural Water Use Efficiency in the Jianghan Plain Based on the Tent-SSA-BPNN Model DOI Creative Commons

Tianshu Shao,

XU Xiang-dong, Yuelong Su

et al.

Agriculture, Journal Year: 2025, Volume and Issue: 15(2), P. 140 - 140

Published: Jan. 9, 2025

The Jianghan Plain (JHP) is a key agricultural area in China where efficient water use (AWUE) vital for sustainable management, food security, environmental sustainability, and economic growth. This study introduces novel AWUE prediction model the JHP, combining BP neural network with Sparrow Search Algorithm (SSA) an improved Tent Mixing (Tent-SSA-BPNN). hybrid addresses limitations of traditional methods by enhancing forecast accuracy stability. By integrating historical data factors, provides detailed understanding AWUE’s spatial temporal variations. Compared to networks other methods, Tent-SSA-BPNN significantly improves stability, achieving (ACC) 96.218%, root mean square error (RMSE) 0.952, coefficient determination (R2) 0.9939, surpassing previous models. results show that (1) from 2010 2022, average JHP fluctuated within specific range, exhibiting decrease 0.69%, significant differences distributions across various cities; (2) was (R²) value 0.9939. (3) those preoptimization model, ACC, RMSE, R² values terms clearly indicating efficacy optimization. (4) reveal proportion consumption has impact on AWUE. These provide actionable insights optimizing resource allocation, particularly water-scarce regions, guide policymakers management strategies, supporting development.

Language: Английский

Evaluation and Prediction of Agricultural Water Use Efficiency in the Jianghan Plain Based on the Tent-SSA-BPNN Model DOI Creative Commons

Tianshu Shao,

XU Xiang-dong, Yuelong Su

et al.

Agriculture, Journal Year: 2025, Volume and Issue: 15(2), P. 140 - 140

Published: Jan. 9, 2025

The Jianghan Plain (JHP) is a key agricultural area in China where efficient water use (AWUE) vital for sustainable management, food security, environmental sustainability, and economic growth. This study introduces novel AWUE prediction model the JHP, combining BP neural network with Sparrow Search Algorithm (SSA) an improved Tent Mixing (Tent-SSA-BPNN). hybrid addresses limitations of traditional methods by enhancing forecast accuracy stability. By integrating historical data factors, provides detailed understanding AWUE’s spatial temporal variations. Compared to networks other methods, Tent-SSA-BPNN significantly improves stability, achieving (ACC) 96.218%, root mean square error (RMSE) 0.952, coefficient determination (R2) 0.9939, surpassing previous models. results show that (1) from 2010 2022, average JHP fluctuated within specific range, exhibiting decrease 0.69%, significant differences distributions across various cities; (2) was (R²) value 0.9939. (3) those preoptimization model, ACC, RMSE, R² values terms clearly indicating efficacy optimization. (4) reveal proportion consumption has impact on AWUE. These provide actionable insights optimizing resource allocation, particularly water-scarce regions, guide policymakers management strategies, supporting development.

Language: Английский

Citations

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