.png){kind=icon}
Return
Machine learning for ecological analysis
.png){kind=icon}
PRE
AI
DOI：10.1016/j.cej.2025.160780.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Machine Learning
Landscape Ecological Analysis
Ecological Process Modeling and Prediction
Ecosystem Management
Journal
IF:
13.2
Papers:
7.5W
Citations:
48.5W
Organization
Cited Papers
ShareSaveShareSaveShareSaveShareSaveLarge language models for structured reporting in radiology: performance of GPT-4, ChatGPT-3.5, Perplexity and Bing.png)
RADIOLOGIA MEDICA
RADIOLOGIA MEDICAIF4.8
PREAI
ShareSaveShareSavePredicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran
OAAI
OAAIShareSave
View more