机器学习技术在生态学中的应用进展

Application of machine learning technology in ecology

随着生态学研究的深入,生态学逐渐进入大数据时代。作为人工智能的核心技术之一,机器学习能够高效处理生态大数据,得到广泛应用。本文对近年来国内外的相关研究和应用进行系统的总结和分析,从水文、土壤、气象与气候、植被等多个研究要素进行应用综述及举例分析,涉及水文循环、碳循环、气象预测、气候变化、物种分布、健康评估、景观生态、资源管理等多个研究领域。最后,基于对机器学习技术在生态学研究中存在的问题进行分析,并对未来的趋势进行了展望。总体而言,随机森林与神经网络由于其自身特性,是生态学研究中较为常用的机器学习方法。而集成多种机器学习算法,或集成机器学习与传统统计方法、生态学模型等是未来基于机器学习的生态学研究最佳方案。

With the gradual deepening of ecological research,ecology has entered the era of big data.As one of the core technologies of artificial intelligence,machine learning has been widely used to efficiently process ecological big data.We systematically summarized and analyzed the relevant research and the application of machine learning in recent years.The applications of machine learning in hydrology,soil,meteorology and climate,vegetation and other factors were analyzed with examples,which were involved in many research fields,including hydrological cy⁃cle,carbon cycle,meteorological prediction,climate change,species distribution,health assessment,landscape ecology,and resource management.Finally,its future trend was prospected based on the analysis of the problems of machine learning technology in ecological research.In general,random forest and neural network are the most com⁃monly used machine learning methods in ecological research due to their characteristics.Integrating multiple ma⁃chine learning algorithms,or integrating machine learning with traditional statistical methods and ecological models,is the best solution for future machine⁃learning⁃based ecological research.