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Babak Mohammadi

Doktorand

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Establishing Coupled Models for Estimating Daily Dew Point Temperature Using Nature-Inspired Optimization Algorithms

Författare

  • Saeid Mehdizadeh
  • Babak Mohammadi
  • Farshad Ahmadi

Summary, in English

Potential of a classic adaptive neuro-fuzzy inference system (ANFIS) was evaluated in the current study for estimating the daily dew point temperature (Tdew). The study area consists of two stations located in Iran, namely the Rasht and Urmia. The daily Tdew time series of the studied stations were modeled through the other effective variables comprising minimum air temperature (Tmin), extraterrestrial radiation (Ra), vapor pressure deficit (VPD), sunshine duration (n), and relative humidity (RH). The correlation coefficients between the input and output parameters were utilized to determine the most effective inputs. Furthermore, novel hybrid models were proposed in this study in order to increase the estimation accuracy of Tdew. For this purpose, two optimization algorithms named bee colony optimization (BCO) and dragonfly algorithm (DFA) were coupled on the classic ANFIS. It was concluded that the hybrid models (i.e., ANFIS-BCO and ANFIS-DFA) demonstrated better performances compared to the classic ANFIS. The full-input pattern of the coupled models, specifically the ANFIS-DFA, was found to present the most accurate results for both the selected stations. Therefore, the developed hybrid models can be proposed as alternatives to the classic ANFIS to accurately estimate the daily Tdew.

Avdelning/ar

  • Institutionen för naturgeografi och ekosystemvetenskap

Publiceringsår

2022-01

Språk

Engelska

Publikation/Tidskrift/Serie

Hydrology

Volym

9

Issue

1

Dokumenttyp

Artikel i tidskrift

Förlag

MDPI AG

Ämne

  • Physical Geography
  • Meteorology and Atmospheric Sciences

Nyckelord

  • Artificial intelligence
  • Hydrological modeling
  • Dew point temperature
  • Soft computing
  • Water Resources Management
  • machine learning

Status

Published

ISBN/ISSN/Övrigt

  • ISSN: 2306-5338