Evaluating Performance of Multiple Machine Learning Models for Drought Monitoring: A Case Study of Typical Grassland in Inner Mongolia

Yuchi Wang, Jiahe Cui, Bailing Miao (), Zhiyong Li, Yongli Wang, Chengzhen Jia and Cunzhu Liang
Additional contact information
Yuchi Wang: Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau & Collaborative Innovation Center for Grassland Ecological Security, Ministry of Education of China, School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, China
Jiahe Cui: College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China
Bailing Miao: Inner Mongolia Meteorological Institute, Hohhot 010051, China
Zhiyong Li: Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau & Collaborative Innovation Center for Grassland Ecological Security, Ministry of Education of China, School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, China
Yongli Wang: Inner Mongolia Meteorological Institute, Hohhot 010051, China
Chengzhen Jia: Inner Mongolia Meteorological Institute, Hohhot 010051, China
Cunzhu Liang: Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau & Collaborative Innovation Center for Grassland Ecological Security, Ministry of Education of China, School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, China

Land, 2024, vol. 13, issue 6, 1-20

Abstract: Driven by continuously evolving precipitation shifts and temperature increases, the frequency and intensity of droughts have increased. There is an obvious need to accurately monitor drought. With the popularity of machine learning, many studies have attempted to use machine learning combined with multiple indicators to construct comprehensive drought monitoring models. This study tests four machine learning model frameworks, including random forest (RF), convolutional neural network (CNN), support vector regression (SVR), and BP neural network (BP), which were used to construct four comprehensive drought monitoring models. The accuracy and drought monitoring ability of the four models when simulating a well-documented Inner Mongolian grassland site were compared. The results show that the random forest model is the best among the four models. The R 2 range of the test set is 0.44–0.79, the RMSE range is 0.44–0.72, and the fitting accuracy relationship could be described as RF > CNN > SVR ≈ BP. Correlation analysis between the fitting results of the four models and SPEI found that the correlation coefficient of RF from June to September was higher than that of the other three models, though we noted the correlation coefficient of CNN in May was slightly higher than that of RF (CNN = 0.79; RF = 0.78). Our results demonstrate that comprehensive drought monitoring indices developed from RF models are accurate, have high drought monitoring ability, and can achieve the same monitoring effect as SPEI. This study can provide new technical support for comprehensive regional drought monitoring.

Keywords: environmental remote sensing; drought monitoring; machine learning; model construction (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2073-445X/13/6/754/pdf (application/pdf)
https://www.mdpi.com/2073-445X/13/6/754/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:13:y:2024:i:6:p:754-:d:1403614

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().