research-article

Assessment of Machine Learning Methods for Modeling Alpine Grassland Biomass in Southern Qinghai Province, China

Authors:

Tiangang LiangAuthors Info & Claims

CSAE '19: Proceedings of the 3rd International Conference on Computer Science and Application Engineering

Article No.: 77, Pages 1 - 5

https://doi.org/10.1145/3331453.3361679

Published: 22 October 2019 Publication History

Abstract

Accurate and effective modeling of grassland aboveground biomass (AGB) is an important basic task in monitoring and management of grassland and livestock interaction in pastoral areas. In this study, three machine learning methods, multi-layer perceptron network (MLP), support vector regression, and random forest regression (RF) are assessed for modeling the grassland AGB in the southern region, Qinghai Province, China. The results show that: 1) among the three methods, the MLP model performs the worst, with R2 and RMSE of 0.38 and 768.74 kg DW/ha, respectively for the test data, and of 0.34 and 745.06 kg DW/ha for the training dataset; 2) the RF model performs the best, with R2 and RMSE of 0.76 and 473.20 kg DW/ha, respectively for the test data, and of 0.95 and 208.88 kg DW/ha for the training dataset. This performance is similar to the best back propagation ANN model previously reported (0.66 and 556.57 kg DW/ha for test data, 0.85 and 355.04 kg DW/ha for training data, and 0.68 and 537.09 kg DW/ha for validation). The RF model is easy to apply in practice and is a robust tool for modeling grassland biomass based on DEM and remote sensing data.

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Cited By

Zhi QHu XWang PLi MDing YWu YPeng TLi WGuan XShi XLi J(2024)Estimation, Spatiotemporal Dynamics, and Driving Factors of Grassland Biomass Carbon Storage Based on Machine Learning Methods: A Case Study of the Hulunbuir GrasslandRemote Sensing10.3390/rs1619370916:19(3709)Online publication date: 5-Oct-2024
https://doi.org/10.3390/rs16193709
Li MWang JLi KOchir ATogtokh CXu C(2023)Spatial-Temporal Pattern Analysis of Grassland Yield in Mongolian Plateau Based on Artificial Neural NetworkRemote Sensing10.3390/rs1516396815:16(3968)Online publication date: 10-Aug-2023
https://doi.org/10.3390/rs15163968

Index Terms

Assessment of Machine Learning Methods for Modeling Alpine Grassland Biomass in Southern Qinghai Province, China
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Classification and regression trees

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CSAE '19: Proceedings of the 3rd International Conference on Computer Science and Application Engineering

October 2019

942 pages

ISBN:9781450362948

DOI:10.1145/3331453

Conference Chair:
Ali Emrouznejad,
Program Chair:
Zeshui Xu

Copyright © 2019 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 October 2019

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CSAE 2019

CSAE 2019: The 3rd International Conference on Computer Science and Application Engineering

October 22 - 24, 2019

Sanya, China

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Cited By

Zhi QHu XWang PLi MDing YWu YPeng TLi WGuan XShi XLi J(2024)Estimation, Spatiotemporal Dynamics, and Driving Factors of Grassland Biomass Carbon Storage Based on Machine Learning Methods: A Case Study of the Hulunbuir GrasslandRemote Sensing10.3390/rs1619370916:19(3709)Online publication date: 5-Oct-2024
https://doi.org/10.3390/rs16193709
Li MWang JLi KOchir ATogtokh CXu C(2023)Spatial-Temporal Pattern Analysis of Grassland Yield in Mongolian Plateau Based on Artificial Neural NetworkRemote Sensing10.3390/rs1516396815:16(3968)Online publication date: 10-Aug-2023
https://doi.org/10.3390/rs15163968

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