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Soil Moisture Prediction in Peri-urban Beijing, China: Gene Expression Programming Algorithm

Hongfei Niu1,2, Fanyu Meng3, Huanfang Yue3, Lihong Yang4, Jing Dong2,5, Xin Zhang2,5,*
1 Liaoning Vocational College of Ecological Engineering, Shenyang, 110122, China
2 National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
3 Beijing Agricultural Technology Extension Station, Beijing, 100029, China
4 Yunnan Agricultural Vocational and Technical College, Kunming, 650031, China
5 Key Laboratory for Quality Testing of Hardware and Software Products on Agricultural Information, Ministry of Agriculture, Beijing, 100097, China
* Corresponding Author: Xin Zhang. Email:

Intelligent Automation & Soft Computing 2021, 28(1), 93-106. https://doi.org/10.32604/iasc.2021.010131

Received 30 August 2020; Accepted 02 October 2020; Issue published 17 March 2021

Abstract

Soil moisture is an important indicator for agricultural planting and agricultural water management. People have been trying to guide crop cultivation, formulate irrigation systems, and develop intelligent agriculture by knowing exactly what the soil moisture is in real time. This paper considers the impact of meteorological parameters on soil-moisture change and proposes a soil-moisture prediction method based on the Gene Expression Programming (GEP) algorithm. The prediction model is tested on datasets from Shunyi, Yanqing and Daxing agricultural farms, Beijing. The results show that the GEP model can predict soil moisture with a maximum correlation coefficient of 0.98, and the root-mean-square errors in three different farms were below 2.32.

Keywords

GEP; prediction; soil moisture

Cite This Article

H. Niu, F. Meng, H. Yue, L. Yang, J. Dong et al., "Soil moisture prediction in peri-urban beijing, china: gene expression programming algorithm," Intelligent Automation & Soft Computing, vol. 28, no.1, pp. 93–106, 2021.

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This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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