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使用巨型植物预测淡水生物质量:实证建模方法的比较

Daniel Gebler¹, Pedro Segurado², Maria Teresa Ferreira²

¹Department of Ecology and Environmental Protection, Poznan University of Life Sciences, Wojska Polskiego 28, 60-637, Poznan, Poland. daniel.gebler@up.poznan.pl.

Environmental science and pollution research international

|November 20, 2024

概括

像人工神经网络 (ANN) 和增强回归树 (BRT) 这样的预测模型可以有效地评估南欧河流的生态质量. 这些模型将巨体指数与环境压力因素联系起来,有助于河流管理.

科学领域:

环境科学环境科学
生态生态学生态生态学
计算生物学计算生物学

背景情况:

南欧河流的生物评估具有挑战性,因为参考数据稀缺,植物群落受到影响的复杂压力相互作用.
预测建模通过整合各种环境压力和识别影响水生生态系统的关键因素,提供了一个解决方案.

研究的目的:

开发和比较预测模型,以评估地中海河流的生态质量,使用巨型植物数据.
调查宏生物指数和环境变量之间的关系,确定河流健康的关键驱动因素.

主要方法:

收集了葡萄牙292个地中海河流和溪流位置的数据集,包括巨型植物和环境数据.
预测模型的比较:多重线性回归 (MLR),增强回归树 (BRT) 和人工神经网络 (ANN).
对环境变量进行了两种巨生物指数 (河岸植被指数 - RVI,河流巨生物指数 - IBMR) 的研究.

主要成果:

河流宏生物指数 (IBMR) 的模型表明,与河岸植被指数 (RVI) 相比,生态联系更好.
对于IBMR,人工神经网络 (ANN) 的表现优于增强回归树 (BRT),显示出高预测准确度 (r=0.877,RMSE=10.0).
影响IBMR预测的关键变量包括经度,地质,水文,气候,水体大小和土地使用.

结论:

增强回归树 (BRT) 和人工神经网络 (ANN) 是河流生态质量评估的可行工具.
这些模型支持对河流环境管理的知情决策,特别是在数据有限的地区.
河流的巨生物指数 (IBMR) 显示,与河岸植被指数 (RVI) 相比,与环境压力因素的生态联系更强烈.

关键词:

人工神经网络的人工神经网络生物适应性生物适应性增强的回归树可以提高回归树.线性回归是一种线性回归.巨生物是巨生物的一种.评估河流的评估

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