文献类型：期刊文献

英文题名：Machine learning for ecological analysis

作者：Yu, Zhengyang[1] Bu, Chunfeng[1] Li, Yanjie[2]

第一作者：Yu, Zhengyang

通信作者：Yu, ZY[1];Li, YJ[2]

机构：[1]Henan Zhongping Jiaoke Res & Design Inst Co Ltd, Pingdingshan 467000, Henan Province, Peoples R China;[2]Chinese Acad Forestry, Res Inst Subtrop Forestry, 73,Daqiao Rd, Hangzhou 311400, Zhejiang Provin, Peoples R China

年份：2025

卷号：507

外文期刊名：CHEMICAL ENGINEERING JOURNAL

收录：;EI(收录号：20250817910906);Scopus(收录号：2-s2.0-85217946509);WOS:【SCI-EXPANDED(收录号:WOS:001434280500001)】；

基金：This research was supported by Fundamental Research Funds of CAF, No. CAFYBB2022QA001 and the Science and Technology innovation 2030-Agricultural biological breeding major project (2023ZD040580105) .

语种：英文

外文关键词：Machine Learning; Landscape Ecological Analysis; Ecological Process Modeling and Prediction; Ecosystem Management

摘要：This study systematically reviews the application of machine learning (ML) in ecological analysis, with a focus on its use in modeling and predicting ecological processes. By analyzing relevant literature, we identify key trends and challenges, particularly in the use of Random Forest (RF), Support Vector Machines (SVMs), and Deep Learning (DL) algorithms. Our findings highlight RF as the most widely adopted algorithm for ecological classification tasks, while SVMs and DL techniques are particularly effective for handling complex, multimodal datasets and modeling non-linear ecological processes. The study demonstrates how these ML methods are advancing ecological research by improving predictions of environmental change, enhancing ecosystem management, and enabling more accurate simulations of ecological dynamics. We also discuss the integration of ML into ecological science, underscoring its potential to overcome traditional research limitations and open new avenues for understanding complex ecological patterns. This research contributes to the growing body of work on ML applications in ecology, offering insights into both the current state and future directions of the field.