人工智能的进步可以模拟厄尔尼诺-南方振荡对作物产量变化的影响
Usa Humphries Wannasingha1, Muhammad Waqas2, Angkool Wangwongchai1
1Department of Mathematics, Faculty of Science, King Mongkut's University of Technology Thonburi (KMUTT), Bangkok,10140, Thailand.
MethodsX
|October 13, 2025
概括
厄尔尼诺 - 南方振荡 (ENSO) 影响热带农业. 厄尔尼诺会导致干旱,拉尼诺会导致洪水,这两种现象都会减少泰国的雨产量,但人工智能对预测有希望.
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:
- 厄尔尼诺-南方振荡 (ENSO) 显著影响热带农业,特别是雨水米生产,易受干旱和洪水等气候极端影响.
- 了解ENSO对作物产量的影响对于脆弱地区的粮食安全至关重要.
- 人工智能 (AI) 提供了预测气候诱导的农业影响的潜力.
研究的目的:
- 系统地审查二十年来对ENSO阶段 (厄尔尼诺和拉尼娜) 的研究及其对谷物作物产量的影响.
- 专注于泰国的雨水米生产的具体影响.
- 评估AI在预测ENSO相关对作物生产力的影响中的作用和有效性.
主要方法:
- 对跨越二十年的研究进行系统的文献综述.
- 对研究厄尔尼诺和拉尼娜对谷物作物产量的影响的分析,重点是泰国雨后大米.
- 评估人工智能模型 (例如,随机森林,LSTM,CNNs) 用于预测ENSO诱导的作物产量变化.
主要成果:
- 厄尔尼诺现象与降雨量减少和干旱压力增加有关,对大米产量产生负面影响.
- 拉尼娜现象与过度降雨和洪水有关,这也对大米生产率产生了不利影响.
- 人工智能模型显示出预测ENSO影响的巨大潜力,尽管可扩展性和本地适应性挑战仍然存在.
结论:
- 由于干旱和洪水极端,ENSO的厄尔尼诺和拉尼娜阶段对泰国的雨生产构成重大风险.
- 人工智能技术,包括RF,LSTM和CNN,在预测这些气候驱动的产量减少方面表现有前途.
- 混合建模,增强数据质量和适应性技术对于开发适应气候变化的农业系统以减轻ENSO影响至关重要.
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