LocustLens:利用环境数据融合和机器学习来预测沙漠虫群
Sidra Khan1, Beenish Ayesha Akram1, Amna Zafar2
1Department of Computer Engineering, University of Engineering and Technology Lahore, Lahore, Pakistan.
PeerJ. Computer science
|December 9, 2024
概括
这项研究介绍了LocustLens,一种机器学习模型,利用土壤水分,温度和降水量预测沙漠虫的袭击. 在考虑环境影响的同时,LocustLens实现了98%的准确性,超过了其他模型.
科学领域:
- 农业科学 农业科学
- 环境科学 环境科学
- 计算机科学 计算机科学
背景情况:
- 沙漠虫导致全球严重的粮食短缺和经济损失.
- 由于复杂的环境和地理因素,预测虫袭击具有挑战性.
研究的目的:
- 开发一种机器学习模型,用于预测42个国家的沙漠虫袭击.
- 为了确定包括土壤水分,最高温度和降水在内的关键预测因素.
主要方法:
- 通过将TerraClimate环境数据与粮农组织虫群数据集成,为42个国家开发了全球虫攻击数据库 (GLAD42).
- 使用反向地理编码来提高空间数据的可解释性.
- 利用K-最近邻近算法 (K-NN) 以其准确性和高效的再培训能力.
- 评估了机器学习模型的碳排放量,以实现环保.
主要成果:
- 开发的LocustLens模型实现了98%的预测准确度,超过了基线K-NN (96%),决策树 (97%),支持矢量分类器 (91%) 等.
- 测量了碳排放,LocustLens的碳排放量为4.87公斤,而其他型号的碳排放量从0.02公斤以上.
结论:
- 在预测沙漠虫袭击方面,LocustLens表现出卓越的性能.
- 该模型提供了一个准确的,环保的方法来预测虫袭击,有助于减轻农业和经济损害.
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