.png){kind=icon}
返回
Rangeland species potential mapping using machine learning algorithms
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ecoleng.2023.106900.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Documenting habitats of rangeland plant species is required to properly manage rangelands and to understand ecosystem processes. A reliable rangeland species potential map can help managers and policy makers design a sustainable grazing system on rangelands. The aim of this study is to map the plant species in the Qurveh City rangelands, Kurdistan Province, Iran, using state-of-the-art machine learning algorithms, including Support Vector Machine (SVM), Artificial Neural Network (ANN), Naive Bayes (NB), Bayes Net (BN) and Classification and Regression Tree (CART). A total of 185 rangeland species were used in the study, together with 20 conditioning factors, to build and validate models. The One-R feature section technique and multicollinearity test were used, respectively, to determine the most important factors and correlations between them. Model validation was performed using sensitivity, specificity, accuracy, F1-measure, Matthews correlation coefficient (MCC), Kappa, root mean square error (RMSE), and area under the receiver operating characteristic curve (AUC). Results showed that topographic wetness index (TWI), slope angle, elevation, soil phosphorus and soil potassium were the five most important factors to increase the rangeland plants habitat suitability. The Naive Bayes algorithm (AUC = 0.782) had the highest performance and prediction accuracy and best consistency across the species in the investigated rangeland, followed by the SVM (AUC = 0.763), ANN (AUC = 0.762), CART (AUC = 0.627), and BN (AUC = 0.617) models.
Keyword:
Rangeland management
Plant habitat suitability
Artificial intelligence
Machine learning
期刊
IF:
4.1
论文数:
7.0K
被引数:
2.2W
机构
引用论文
Controlled Decoration of Divalent Nickel onto CdS/CdSe Core/Shell Quantum Dots to Boost Visible‐Light‐Induced Hydrogen Generation in Water.png)
ChemPlusChem
ChemPlusChemIF0
PREAI
分享收藏
Comparing different multiple flow algorithms to calculate RUSLE factors of slope length (L) and slope steepness (S) in SwitzerlandGEOMORPHOLOGY
OAAI
GEOMORPHOLOGYIF3.3
OAAI分享收藏Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree浅层滑坡灾害的空间预测模型: 支持向量机，人工神经网络，核逻辑回归和逻辑模型树的功效比较评估LANDSLIDES
LANDSLIDESIF7
PREAI
分享收藏
查看更多内容