使用原始植被数据的可解释的人工智能方法将时空分类为从未成熟的水域到大堡礁的流量
Cherie M O'Sullivan1, Ravinesh C Deo2,3, Afshin Ghahramani4,5
1University of Southern Queensland, Toowoomba, QLD, 4350, Australia. Cherie.O'Sullivan@unisq.edu.au.
Scientific reports
|October 24, 2023
概括
对于溶解无机 (DIN) 建模来说,识别类似的水域是一个挑战. 这项研究使用空间数据和人工智能来匹配未成熟的水域,改善大堡礁的水质预测.
科学领域:
- 环境科学 环境科学
- 水质建模水质建模
- 人工智能的人工智能
背景情况:
- 在水质建模中,将数据传输到未经老化的水域是常见的.
- 溶解无机 (DIN) 采集区的相似性定义不佳,影响了模型的准确性.
- 空间数据代理提供了一个通过DIN响应对采集区进行分类的解决方案.
研究的目的:
- 探索空间数据代理的适用性,以匹配基于DIN制度的未加的流域与测量流域.
- 提高DIN的水质模型在流入大堡礁的未开采的流域中的预测能力.
- 确定未经测量的流域的优先监测区域,这些流域与未经测量的对应区域缺乏相似性.
主要方法:
- 利用神经网络模式识别模型 (ANN-PR) 和 SHAP-XAI 来使用代理空间数据匹配集群.
- 采用神经网络水质 (ANN-WQ) 模拟器,在测量数据上进行训练,以验证流域匹配的适用性.
- 在无监督学习场景中通过模拟对匹配的采集区的DIN来测试模型性能.
主要成果:
- 根据DIN制度对训练数据进行区分,在无监督场景中显著改善了ANN-WQ模拟性能 (p < 0.05).
- 代理空间数据在对具有类似 DIN 制度的水域进行分类方面被证明是有效的.
- 识别出缺乏与测量区域相似的水域,作为优先监测区域.
结论:
- 空间数据代理是分类类似溶解无机 (DIN) 制度的水域的一个有价值的工具.
- 开发的人工智能驱动的方法增强了未成熟的水域的水质建模.
- 针对不同采集区的有针对性的监测对于在大堡礁地区全面采集DIN数据至关重要.
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