Simulation of evapotranspiration of marsh meadow in Qinghai Lake Basin based on machine learning model

Xuan Chen Liu; Li Ming Gao; Le Le Zhang; Ke Long Chen

doi:10.1117/12.3010219

15 November 2023 Simulation of evapotranspiration of marsh meadow in Qinghai Lake Basin based on machine learning model

Xuan Chen Liu, Li Ming Gao, Le Le Zhang, Ke Long Chen

Proceedings Volume 12815, International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023); 128151Y (2023) https://doi.org/10.1117/12.3010219
Event: International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023), 2023, Kaifeng, China

Abstract

Accurate evapotranspiration (ET) simulation is of great significance for the study of surface hydrological cycle in alpine swamp meadow under the background of climate change. In this paper, the ET estimation model was constructed based on the meteorological and EDV data recorded at the Wayanshan Wetland observation Station in Qinghai Lake Basin, based on random forest (RF), extreme gradient lifting (XGB), support vector machine (SVR) and artificial neural network (ANN) learning algorithms, and the simulation accuracy of different machine learning models was evaluated. Cross-validation results show that the coefficient of determination of RF, XGB, SVR and ANN models is 0.97, 0.91, 0.85 and 0.75, and the root mean square error is 0.07, 0.04, 0.38 and 1.12, respectively. Among all models, RF has the best simulation effect, while XGB and SVR have better simulation effect. The ANN model has the worst simulation effect. According to the seasonal distribution trend of simulated ET and observed ET, all models can simulate the seasonal cycle of evapotranspiration well, and the average RMSE is 0.12-0.38 mm/d. The interpretability analysis of evapotranspiration and meteorological factors shows that air temperature, net radiation and soil water content are the key factors that dominate the evapotranspiration change of alpine marsh meadow in Qinghai Lake Basin.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xuan Chen Liu, Li Ming Gao, Le Le Zhang, and Ke Long Chen "Simulation of evapotranspiration of marsh meadow in Qinghai Lake Basin based on machine learning model", Proc. SPIE 12815, International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023), 128151Y (15 November 2023); https://doi.org/10.1117/12.3010219

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KEYWORDS

Machine learning

Atmospheric modeling

Data modeling

Artificial neural networks

Environmental sensing

Meteorology

Water content

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