干旱区生态环境知识资源中心(Arid): Comparison of Machine Learning Models to Predict Lake Area in an Arid Area

Arid

DOI	10.3390/rs15174153
	Comparison of Machine Learning Models to Predict Lake Area in an Arid Area
	Wang, Di; Huo, Zailin; Miao, Ping; Tian, Xiaoqiang
通讯作者	Huo, ZL
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2023
卷号	15 期号:17
英文摘要	Machine learning (ML)-based models are popular for complex physical system simulation and prediction. Lake is the important indicator in arid and semi-arid areas, and to achieve the proper management of the water resources in a lake basin, it is crucial to estimate and predict the lake dynamics, based on hydro-meteorological variations and anthropogenic disturbances. This task is particularly challenging in arid and semi-arid regions, where water scarcity poses a significant threat to human life. In this study, a typical arid area of China was selected as the study area, and the performances of eight widely used ML models (i.e., Bayesian Ridge (BR), K-Nearest Neighbor (KNN), Gradient Boosting Decision Tree (GBDT), Extra Trees (ET), Random Forest (RF), Adaptive Boosting (AB), Bootstrap aggregating (Bagging), eXtreme Gradient Boosting (XGB)) were evaluated in predicting lake area. Monthly lake area was determined by meteorological (precipitation, air temperature, Standardised Precipitation Evapotranspiration Index (SPEI)) and anthropogenic factors (ETc, NDVI, LUCC). Lake area determined by Landsat satellite image classification for 2000-2020 was analysed side-by-side with the Standardised Precipitation Evapotranspiration Index (SPEI) on 9 and 12-month time scales. With the evaluation of six input variables and eight ML algorithms, it was found that the RF models performed best when using the SPEI-9 index, with R2 = 0.88, RMSE = 1.37, LCCC = 0.95, and PRD = 1331.4 for the test samples. Furthermore, the performance of the ML model constructed with the 9-month time scale SPEI (SPEI-9) as an input variable (MLSPEI-9) depended on seasonal variations, with the average relative errors of up to 0.62 in spring and a minimum of 0.12 in summer. Overall, this study provides valuable insights into the effectiveness of different ML models for predicting lake area by demonstrating that the right inputs can lead to a remarkable increase in performance of up to 13.89%. These findings have important implications for future research on lake area prediction in arid zones and demonstrate the power of ML models in advancing scientific understanding of complex natural systems.
英文关键词	lake area machine learning (ML) SPEI remote sensing Google Earth Engine (GEE)
类型	Article
语种	英语
开放获取类型	Green Published, gold
收录类别	SCI-E
WOS记录号	WOS:001061263600001
WOS关键词	ARTIFICIAL-INTELLIGENCE ; SATELLITE IMAGES ; WATER ; TEMPERATURE ; ALGORITHMS ; DROUGHT ; CLIMATE ; VOLUME
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/398314
推荐引用方式 GB/T 7714	Wang, Di,Huo, Zailin,Miao, Ping,et al. Comparison of Machine Learning Models to Predict Lake Area in an Arid Area[J],2023,15(17).
APA	Wang, Di,Huo, Zailin,Miao, Ping,&Tian, Xiaoqiang.(2023).Comparison of Machine Learning Models to Predict Lake Area in an Arid Area.REMOTE SENSING,15(17).
MLA	Wang, Di,et al."Comparison of Machine Learning Models to Predict Lake Area in an Arid Area".REMOTE SENSING 15.17(2023).