机器学习模型对预测孟加拉国阿曼大米产量的比较研究
Taufiqul Islam1, Tanmoy Mazumder1, Md Nishad Shahriair Roni2
1Department of Urban and Regional Planning, Khulna University of Engineering & Technology (KUET), Khulna, 9203, Bangladesh.
Heliyon
|December 18, 2024
概括
随机森林准确地预测了阿曼大米的产量,尽管气候变化. 这种机器学习模型为孟加拉国未来的作物生产和粮食安全挑战提供了洞察力.
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:
- 阿曼大米是孟加拉国的重要主食作物,非常容易受到季风季节气候变化的影响.
- 从1970年到2022年的历史数据显示,对大米生产的气候影响很大.
研究的目的:
- 确定最有效的机器学习模型来预测阿曼大米产量.
- 分析气候条件变化对大米生产的影响.
主要方法:
- 利用52年的历史数据 (1970-2022年) 对阿曼大米产量和气候变量进行分析.
- 预处理的数据包括异常值的校正,归算和每月气候数据的聚合.
- 使用并评估使用RMSE,R2和MAE指标的随机森林,神经网络,决策树,线性回归和梯度增强模型.
主要成果:
- 随机森林在气候变化下预测大米产量方面表现出最高的准确性和稳定性.
- 渐变增强也表现良好,而线性回归捕捉了线性趋势,但错过了非线性模式.
- 神经网络和决策树在建模复杂的气候产出关系时的准确性较低.
结论:
- 随机森林预测,到2030年和2050年,阿曼大米产量将增加,尽管预计气温上升和降雨量略有增加.
- 气温上升对大米生产造成重大压力,影响粮食安全.
- 适应性农业战略和政策对于减轻气候变化对孟加拉国大米生产的影响至关重要.
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