Webbläsaren som du använder stöds inte av denna webbplats. Alla versioner av Internet Explorer stöds inte längre, av oss eller Microsoft (läs mer här: * https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Var god och använd en modern webbläsare för att ta del av denna webbplats, som t.ex. nyaste versioner av Edge, Chrome, Firefox eller Safari osv.

Default user image.

Babak Mohammadi

Doktorand

Default user image.

Implementing novel hybrid models to improve indirect measurement of the daily soil temperature : Elman neural network coupled with gravitational search algorithm and ant colony optimization

Författare

  • Saeid Mehdizadeh
  • Babak Mohammadi
  • Quoc Bao Pham
  • Dao Nguyen Khoi
  • Nguyen Thi Thuy Linh

Summary, in English

Soil temperature (ST) as a vital variable of soil plays a key role in agriculture products, surface energy transactions, soil moisture balance, etc. In developing countries like Iran, access to the ST data may be limited. Hence, estimating this parameter by an appropriate alternative approach is of great importance. Two novel hybrid models are developed in this study based on Elman neural network (ENN) coupled with gravitational search algorithm (GSA) and ant colony optimization (ACO) for improving the daily ST estimation at various soil depths (i.e., ENN-GSA and ENN-ACO). In fact, both the optimization algorithms including the GSA and ACO were applied to train the parameters of ENN. To achieve this, the daily data from two stations, namely the Isfahan and Rasht located in Iran were employed during 1998–2017. The classical ENN and hybrid ENN-GSA and ENN-ACO models are developed using the other meteorological parameters under eleven different scenarios. The results illustrated that the proposed hybrid models outperformed the classical ENN for estimating the daily ST of the studied locations at different depths; however, the hybrid ENN-GSA was the best-performing model at the studied stations and whole the soil depths. In addition, all the standalone and hybrid models illustrated the highest accuracy under full-input pattern.

Publiceringsår

2020-12-01

Språk

Engelska

Publikation/Tidskrift/Serie

Measurement: Journal of the International Measurement Confederation

Volym

165

Dokumenttyp

Artikel i tidskrift

Förlag

Elsevier

Ämne

  • Physical Geography

Nyckelord

  • Ant colony optimization
  • Elman neural networks
  • Estimation
  • Gravitational search algorithm
  • Soil temperature

Status

Published

ISBN/ISSN/Övrigt

  • ISSN: 0263-2241