Establishing Coupled Models for Estimating Daily Dew Point Temperature Using Nature-Inspired Optimization Algorithms
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Hydrology |
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| Online Access: | https://www.mdpi.com/2306-5338/9/1/9 |
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| author | Saeid Mehdizadeh Babak Mohammadi Farshad Ahmadi |
| author_facet | Saeid Mehdizadeh Babak Mohammadi Farshad Ahmadi |
| author_sort | Saeid Mehdizadeh |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T01:22:20Z |
| format | Article |
| id | doaj.art-ce17b5fe7c61463b87a1e88621ce908d |
| institution | Directory Open Access Journal |
| issn | 2306-5338 |
| language | English |
| last_indexed | 2024-03-10T01:22:20Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Hydrology |
| spelling | doaj.art-ce17b5fe7c61463b87a1e88621ce908d2023-11-23T13:58:27ZengMDPI AGHydrology2306-53382022-01-0191910.3390/hydrology9010009Establishing Coupled Models for Estimating Daily Dew Point Temperature Using Nature-Inspired Optimization AlgorithmsSaeid Mehdizadeh0Babak Mohammadi1Farshad Ahmadi2Water Engineering Department, Urmia University, Urmia 5756151818, IranDepartment of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 12, SE-223 62 Lund, SwedenDepartment of Hydrology & Water Resources Engineering, Shahid Chamran University of Ahvaz, Ahvaz 6135783151, IranPotential 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.https://www.mdpi.com/2306-5338/9/1/9artificial intelligencebee colony optimizationdew point temperaturedragonfly algorithmhydrological modelingsoft computing |
| spellingShingle | Saeid Mehdizadeh Babak Mohammadi Farshad Ahmadi Establishing Coupled Models for Estimating Daily Dew Point Temperature Using Nature-Inspired Optimization Algorithms Hydrology artificial intelligence bee colony optimization dew point temperature dragonfly algorithm hydrological modeling soft computing |
| title | Establishing Coupled Models for Estimating Daily Dew Point Temperature Using Nature-Inspired Optimization Algorithms |
| title_full | Establishing Coupled Models for Estimating Daily Dew Point Temperature Using Nature-Inspired Optimization Algorithms |
| title_fullStr | Establishing Coupled Models for Estimating Daily Dew Point Temperature Using Nature-Inspired Optimization Algorithms |
| title_full_unstemmed | Establishing Coupled Models for Estimating Daily Dew Point Temperature Using Nature-Inspired Optimization Algorithms |
| title_short | Establishing Coupled Models for Estimating Daily Dew Point Temperature Using Nature-Inspired Optimization Algorithms |
| title_sort | establishing coupled models for estimating daily dew point temperature using nature inspired optimization algorithms |
| topic | artificial intelligence bee colony optimization dew point temperature dragonfly algorithm hydrological modeling soft computing |
| url | https://www.mdpi.com/2306-5338/9/1/9 |
| work_keys_str_mv | AT saeidmehdizadeh establishingcoupledmodelsforestimatingdailydewpointtemperatureusingnatureinspiredoptimizationalgorithms AT babakmohammadi establishingcoupledmodelsforestimatingdailydewpointtemperatureusingnatureinspiredoptimizationalgorithms AT farshadahmadi establishingcoupledmodelsforestimatingdailydewpointtemperatureusingnatureinspiredoptimizationalgorithms |