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Pregled bibliografske jedinice broj: 966266

Modeling daily water temperature for rivers: comparison between adaptive neuro-fuzzy inference systems and artificial neural networks models


(State Key Laboratory of Hydrology-Water resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nnajing, China ; Faculty of Science, Agronomy Department, Hydraulics Division, Algeria ; Institute for Marine and Atmospheric Research, Department of Physics, Utrecht University, The Netherlands ; Service for Torrent Control, Autonomous Province of Trento, Italy) Zhu, Senlin; Heddam, Salim; Nyarko, Emmanuel Karlo; Hadzima-Nyarko, Marijana; Piccolroaz, Sebastiano; Wu, Shiqiang
Modeling daily water temperature for rivers: comparison between adaptive neuro-fuzzy inference systems and artificial neural networks models // Environmental science and pollution research, 26 (2018), 1; 402-420 doi:10.1007/s11356-018-3650-2 (međunarodna recenzija, članak, znanstveni)


Naslov
Modeling daily water temperature for rivers: comparison between adaptive neuro-fuzzy inference systems and artificial neural networks models

Autori
Zhu, Senlin ; Heddam, Salim ; Nyarko, Emmanuel Karlo ; Hadzima-Nyarko, Marijana ; Piccolroaz, Sebastiano ; Wu, Shiqiang

Kolaboracija
State Key Laboratory of Hydrology-Water resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nnajing, China ; Faculty of Science, Agronomy Department, Hydraulics Division, Algeria ; Institute for Marine and Atmospheric Research, Department of Physics, Utrecht University, The Netherlands ; Service for Torrent Control, Autonomous Province of Trento, Italy

Izvornik
Environmental science and pollution research (0944-1344) 26 (2018), 1; 402-420

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
River water temperature ; Air temperature ; River flow discharge ; Gregorian calendar ; MLPNN ; ANFIS ; Hydrological regime

Sažetak
River water temperature is a key control of many physical and bio-chemical processes in river systems, which theoretically depends on multiple factors. Here, four different machine learning models, including multilayer perceptron neural network models (MLPNN), adaptive neuro-fuzzy inference systems (ANFIS) with fuzzy c-mean clustering algorithm (ANFIS_FC), ANFIS with grid partition method (ANFIS_GP), and ANFIS with subtractive clustering method (ANFIS_SC), were implemented to simulate daily river water temperature, using air temperature (Ta), river flow discharge (Q), and the components of the Gregorian calendar (CGC) as predictors. The proposed models were tested in various river systems characterized by different hydrological conditions. Results showed that including the three inputs as predictors (Ta, Q, and the CGC) yielded the best accuracy among all the developed models. In particular, model performance improved considerably compared to the case where only Ta is used as predictor, which is the typical approach of most of previous machine learning applications. Additionally, it was found that Q played a relevant role mainly in snow-fed and regulated rivers with higher-altitude hydropower reservoirs, while it improved to a lower extent model performance in lowland rivers. In the validation phase, the MLPNN model was generally the one providing the highest performances, although in some river stations ANFIS_FC and ANFIS_GP were slightly more accurate. Overall, the results indicated that the machine learning models developed in this study can be effectively used for river water temperature simulation.

Izvorni jezik
Engleski

Znanstvena područja
Građevinarstvo, Računarstvo



POVEZANOST RADA


Ustanove
Građevinski i arhitektonski fakultet Osijek,
Fakultet elektrotehnike, računarstva i informacijskih tehnologija Osijek

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus
  • MEDLINE


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