Semantic Scholar Open Access 2020 17 sitasi

Soil water content estimation using ground penetrating radar data via group intelligence optimization algorithms: An application in the Northern Shaanxi Coal Mining Area

Fan Cui Jianyu Ni Yunfei Du Yuxuan Zhao Ying-Ging Zhou

Abstrak

The determination of quantitative relationship between soil dielectric constant and water content is an important basis for measuring soil water content based on ground penetrating radar (GPR) technology. The calculation of soil volumetric water content using GPR technology is usually based on the classic Topp formula. However, there are large errors between measured values and calculated values when using the formula, and it cannot be flexibly applied to different media. To solve these problems, first, a combination of GPR and shallow drilling is used to calibrate the wave velocity to obtain an accurate dielectric constant. Then, combined with experimental moisture content, the intelligent group algorithm is applied to accurately build mathematical models of the relative dielectric constant and volumetric water content, and the Topp formula is revised for sand and clay media. Compared with the classic Topp formula, the average error rate of sand is decreased by nearly 15.8%, the average error rate of clay is decreased by 31.75%. The calculation accuracy of the formula has been greatly improved. It proves that the revised model is accurate, and at the same time, it proves the rationality of the method of using GPR wave velocity calibration method to accurately calculate the volumetric water content.

Topik & Kata Kunci

Penulis (5)

F

Fan Cui

J

Jianyu Ni

Y

Yunfei Du

Y

Yuxuan Zhao

Y

Ying-Ging Zhou

Format Sitasi

Cui, F., Ni, J., Du, Y., Zhao, Y., Zhou, Y. (2020). Soil water content estimation using ground penetrating radar data via group intelligence optimization algorithms: An application in the Northern Shaanxi Coal Mining Area. https://doi.org/10.1177/0144598720973369

Akses Cepat

Lihat di Sumber doi.org/10.1177/0144598720973369
Informasi Jurnal
Tahun Terbit
2020
Bahasa
en
Total Sitasi
17×
Sumber Database
Semantic Scholar
DOI
10.1177/0144598720973369
Akses
Open Access ✓