在孟加拉国使用机器学习算法识别影响天气参数和季节性干旱预测
Md Abdullah Al Mamun1, Mou Rani Sarker2, Md Abdur Rouf Sarkar3,4
1Agricultural Statistics Division, Bangladesh Rice Research Institute, Gazipur, 1701, Bangladesh.
Scientific reports
|January 4, 2024
概括
孟加拉国的干旱强度有所下降,但其复发率有所增加,影响了农作物生产. 机器学习确定了影响干旱模式的关键气候因素,以便更好地预测和减缓战略.
科学领域:
- 环境科学 环境科学
- 气候学 气候学 气候学
- 农业科学 农业科学
背景情况:
- 干旱对环境构成重大风险,特别是在孟加拉国等依赖农业的国家,威胁到粮食安全和生计.
- 孟加拉国对环境危害的脆弱性被干旱条件所加剧,影响了其庞大的人口.
- 了解干旱动态对于制定有效的适应和减缓战略至关重要.
研究的目的:
- 确定影响孟加拉国干旱的气候属性的相对重要性.
- 为了估计干旱的季节性强度和频率在农业气候地区.
- 分析短暂的干旱趋势及其对作物生产和生计的影响.
主要方法:
- 利用四十年 (1981-2020年) 的天气数据进行分析.
- 采用了复杂的机器学习 (ML) 方法,包括Ranger,BagEarth,支持矢量机和随机森林 (RF),以使用标准化降水蒸发转化指数 (SPEI) 来分类农业气候区域.
- 应用RF和Boruta算法来确定水平衡,降水和温度对干旱发生和强度的影响.
主要成果:
- 确定了水平衡,降水和温度作为干旱强度和发生的关键驱动因素.
- 时空分析显示,随着时间的推移,干旱强度下降,但返回时间增加.
- 观察到干旱强度的空间转移,从孟加拉国北部到中部和南部,对农业产生负面影响.
结论:
- 干旱预测和缓解需要了解气候因素的相互作用及其时空变化.
- 有效的干旱管理需要政策制定者,研究人员,社区和当地参与者之间的合作,以制定强有力的适应战略.
- 加强对天气状况的监测对于孟加拉国严格干旱管理至关重要.
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