Forest Management Type Identification Based on Stacking Ensemble Learning

被引:0
作者
Liu, Jiang [1 ]
Chen, Jingmin [2 ]
Chen, Shaozhi [3 ]
Wu, Keyi [1 ]
机构
[1] Chinese Acad Forestry, Res Inst Forestry Policy & Informat, Beijing 100091, Peoples R China
[2] Liaoning Zhanggutai Natl Nat Reserve Management Ct, Fuxin 123100, Peoples R China
[3] Chinese Acad Forestry, Beijing 100091, Peoples R China
来源
FORESTS | 2024年 / 15卷 / 05期
关键词
sustainable forest management; forestry informatization and digitization; Yichun Forestry Group; key decision-making indicator; stacking; feature selection; identification; TREE MANAGEMENT; VISUALIZATION; ALGORITHMS; DIVERSITY; GROWTH;
D O I
10.3390/f15050887
中图分类号
S7 [林业];
学科分类号
0829 ; 0907 ;
摘要
Forest management is the fundamental approach to continuously improve forest quality and achieve the quadruple functions of forests. The identification of forest management types is the basis of forest management and a key technical link in the formulation of forest management plans. However, due to insufficient application of forestry informatization and digitization, there are problems in the organization and application of management types, such as inaccurate identification, diversified standards, long organizational cycles, and low decision-making efficiency. Typical technical models are difficult to widely promote and apply. To address these challenges, this study proposes the Stacking Ensemble Forest Management Type Identification (SEFMTI) method based on Stacking ensemble learning. Initially, four typical forest management types from the sustainable forest management pilot of the Yichun Forestry Group were selected as research subjects, and 19 stand parameters were chosen to form the research data, training various recognition models. Subsequently, the Least Absolute Shrinkage and Selection Operator (LASSO) regression and random forest (RF) methods were used to analyze key decision-making indicators for forest management type recognition and compare the performance of different models. The results show that (1) the SEFMTI model achieved an accuracy rate of 97.14%, effectively improving the accuracy of forest management type recognition while ensuring stability; (2) average age (AG), age group (AGG), crown density (CD), and stand origin (SO) are key decision-making indicators for recognizing forest management types; and (3) after feature selection, the SEFMTI model significantly enhanced the efficiency of model training while maintaining a high accuracy rate. The results validate the feasibility of the SEFMTI identification method, providing a basis for the gradual implementation of sustainable forest management pilots and aiding in the precise improvement of forest quality.
引用
收藏
页数:21
相关论文
共 50 条
  • [21] StackTTCA: a stacking ensemble learning-based framework for accurate and high-throughput identification of tumor T cell antigens
    Charoenkwan, Phasit
    Schaduangrat, Nalini
    Shoombuatong, Watshara
    [J]. BMC BIOINFORMATICS, 2023, 24 (01)
  • [22] An ensemble optimizer with a stacking ensemble surrogate model for identification of groundwater contamination source
    Zhu, Liuzhi
    Lu, Wenxi
    Luo, Chengming
    Xu, Yaning
    Wang, Zibo
    [J]. JOURNAL OF CONTAMINANT HYDROLOGY, 2024, 267
  • [23] Heterogeneous ensemble learning for enhanced crash forecasts-A frequentist and machine learning based stacking framework
    Ahmad, Numan
    Wali, Behram
    Khattak, Asad J.
    [J]. JOURNAL OF SAFETY RESEARCH, 2023, 84 : 418 - 434
  • [24] Cost-sensitive stacking ensemble learning for company financial distress prediction
    Wang, Shanshan
    Chi, Guotai
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2024, 255
  • [25] Toward an Accurate Liver Disease Prediction Based on Two-Level Ensemble Stacking Model
    Hassan Mohamed, Marghany
    Hussein Ali, Botheina
    Ibrahim Taloba, Ahmed
    Aseeri, Ahmad O.
    Abd Elaziz, Mohamed
    El-Sappagah, Shaker
    Mahmoud El-Rashidy, Nora
    [J]. IEEE ACCESS, 2024, 12 : 180210 - 180237
  • [26] Stacking-based ensemble learning of decision trees for interpretable prostate cancer detection
    Wang, Yuyan
    Wang, Dujuan
    Geng, Na
    Wang, Yanzhang
    Yin, Yunqiang
    Jin, Yaochu
    [J]. APPLIED SOFT COMPUTING, 2019, 77 : 188 - 204
  • [27] Artificial intelligence for load forecasting: A stacking learning approach based on ensemble diversity regularization
    Shi, Jiaqi
    Li, Chenxi
    Yan, Xiaohe
    [J]. ENERGY, 2023, 262
  • [28] SAPPHIRE: A stacking-based ensemble learning framework for accurate prediction of thermophilic proteins
    Charoenkwan, Phasit
    Schaduangrat, Nalini
    Moni, Mohammad Ali
    Lio, Pietro
    Manavalan, Balachandran
    Shoombuatong, Watshara
    [J]. COMPUTERS IN BIOLOGY AND MEDICINE, 2022, 146
  • [29] An optimized multi-classifiers ensemble learning for identification of ginsengs based on electronic nose
    Sun, Xiyang
    Liu, Linfeng
    Wang, Zhan
    Miao, Jiacheng
    Wang, You
    Luo, Zhiyuan
    Li, Guang
    [J]. SENSORS AND ACTUATORS A-PHYSICAL, 2017, 266 : 135 - 144
  • [30] A stacking ensemble approach for identification of informative tweets on twitter data
    Dasari S.K.
    Gorla S.
    Prasad Reddy P.V.G.D.
    [J]. International Journal of Information Technology, 2023, 15 (5) : 2651 - 2662