动态SG-SKRDX混合框架用于准确的天气预报和卡维里三角洲的作物适用性
K Janani1, R Alageswaran2, Rengarajan Amirtharajan3
1School of Computing, SASTRA Deemed University, Thanjavur, Tamil Nadu, India.
Scientific reports
|December 13, 2025
概括
本研究介绍了泰米尔纳德邦可持续农业的动态SG-SKRDX模型. 它将先进的天气预报与机器学习作物建议相结合,以提高卡维里三角洲地区的气候适应性和经济稳定性.
科学领域:
- 农业科学 农业科学
- 气候科学 气候科学
- 数据科学数据科学数据科学
背景情况:
- 由于气候变化,泰米尔纳德州卡弗里三角洲地区的农业生产率下降.
- 需要将土著技术知识 (ITK) 与适应气候变化的现代技术相结合.
- 动态天气预报和适应性作物推的挑战.
研究的目的:
- 为特定地区的天气预报和作物推开发混合模型.
- 加强卡维里三角洲地区的可持续农业和经济稳定.
- 将先进的机器学习 (ML) 和深度学习 (DL) 与传统实践相结合.
主要方法:
- 开发了动态SG-SKRDX模型,这种混合方法结合了天气预报和作物建议.
- 使用支持矢量回归器 (SVR) 通的反复单位 (GRU) (SG) 模型进行天气预测 (温度,湿度,降水).
- 采用了一组动态的ML模型 (SVM,KNN,RF,DT,XGBoost) 来进行适应性作物建议 (动态SKRDX).
主要成果:
- SG天气模型实现了低误差率 (0.65%MSE,8.07%RMSE,4.69%MAE) 和良好的R平方 (65.49%).
- 动态SKRDX作物推模型表现出高性能,准确率为93.41%,精度为93.72%,回忆率为93.41%,F1-Score为93.33%.
- 该模型成功地预测天气,并推特定区域的作物.
结论:
- 动态SG-SKRDX模型为卡维里三角洲地区的天气预报和作物推提供了卓越的方法.
- 这种技术驱动的解决方案支持可持续农业,并提高经济稳定性.
- 该模型为农业适应不断变化的气候条件提供了一条途径.
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