Prediction of soil moisture in Inner Mongolia’s League based on machine learning

Yu Zhu; Xiangnan Jing; Aoyun Ding

doi:10.1117/12.3012575

21 December 2023 Prediction of soil moisture in Inner Mongolia’s League based on machine learning

Yu Zhu, Xiangnan Jing, Aoyun Ding

Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 1297005 (2023) https://doi.org/10.1117/12.3012575
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China

Abstract

Traditional mathematical statistics and machine learning models have limitations in soil moisture prediction. This study offers an algorithm based on XGBoost under correlation for soil moisture prediction at a depth of 10 cm, which can achieve an accuracy of 99.29 %. It has a greater advantage over several other machine learning models. The model is also optimized by Grid Search and Randomized Search for the model hyperparameter, and its model is proven to have good generalization ability and robustness.

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

Citation Download Citation

Yu Zhu, Xiangnan Jing, and Aoyun Ding "Prediction of soil moisture in Inner Mongolia’s League based on machine learning", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 1297005 (21 December 2023); https://doi.org/10.1117/12.3012575

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