Bat algorithm optimised extreme learning machine (Bat‐ELM): A novel approach for daily river water temperature modelling
Here, the capability of the Bat algorithm optimised extreme learning machines ELM (Bat‐ELM) is demonstrated for river water temperature (Tw) modelling in the Orda River, Poland. Results using the multilayer perceptron neural network (MLPNN), the classification and regression Tree (CART) and the mult...
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Published in | The Geographical journal Vol. 189; no. 1; pp. 78 - 89 |
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Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
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London
Blackwell Publishing Ltd
01.03.2023
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Abstract | Here, the capability of the Bat algorithm optimised extreme learning machines ELM (Bat‐ELM) is demonstrated for river water temperature (Tw) modelling in the Orda River, Poland. Results using the multilayer perceptron neural network (MLPNN), the classification and regression Tree (CART) and the multiple linear regression (MLR) models were presented for comparison. The models were developed according to two scenarios: (1) using air temperature (Ta) as input for predicting Tw, and (2) using Ta and the periodicity (i.e., day, month and year number). River Tw calibration and validation results derived from air temperature and the periodicity show its potential application. The Bat‐ELM accurately predicts the Tw and surpassed all other models with coefficient of correlation (R) values ranging within the limits of 0.973 to 0.981, and the Nash‐Sutcliffe efficiency (NSE) values will fall within the interval of 0.947 to 0.963. Findings from this research also highlight the robustness of the Bat‐ELM using the periodicity by enhancing its ability to estimate river Tw.
Short
New machine learning for better prediction of water temperature using Bat‐ELM model. |
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AbstractList | Here, the capability of the Bat algorithm optimised extreme learning machines ELM (Bat‐ELM) is demonstrated for river water temperature (Tw) modelling in the Orda River, Poland. Results using the multilayer perceptron neural network (MLPNN), the classification and regression Tree (CART) and the multiple linear regression (MLR) models were presented for comparison. The models were developed according to two scenarios: (1) using air temperature (Ta) as input for predicting Tw, and (2) using Ta and the periodicity (i.e., day, month and year number). River Tw calibration and validation results derived from air temperature and the periodicity show its potential application. The Bat‐ELM accurately predicts the Tw and surpassed all other models with coefficient of correlation (R) values ranging within the limits of 0.973 to 0.981, and the Nash‐Sutcliffe efficiency (NSE) values will fall within the interval of 0.947 to 0.963. Findings from this research also highlight the robustness of the Bat‐ELM using the periodicity by enhancing its ability to estimate river Tw.
Short
New machine learning for better prediction of water temperature using Bat‐ELM model. Abstract Here, the capability of the Bat algorithm optimised extreme learning machines ELM (Bat‐ELM) is demonstrated for river water temperature ( T w ) modelling in the Orda River, Poland. Results using the multilayer perceptron neural network (MLPNN), the classification and regression Tree (CART) and the multiple linear regression (MLR) models were presented for comparison. The models were developed according to two scenarios: (1) using air temperature ( T a ) as input for predicting T w , and (2) using T a and the periodicity (i.e., day, month and year number). River T w calibration and validation results derived from air temperature and the periodicity show its potential application. The Bat‐ELM accurately predicts the T w and surpassed all other models with coefficient of correlation ( R ) values ranging within the limits of 0.973 to 0.981, and the Nash‐Sutcliffe efficiency (NSE) values will fall within the interval of 0.947 to 0.963. Findings from this research also highlight the robustness of the Bat‐ELM using the periodicity by enhancing its ability to estimate river T w . Here, the capability of the Bat algorithm optimised extreme learning machines ELM (Bat‐ELM) is demonstrated for river water temperature (Tw) modelling in the Orda River, Poland. Results using the multilayer perceptron neural network (MLPNN), the classification and regression Tree (CART) and the multiple linear regression (MLR) models were presented for comparison. The models were developed according to two scenarios: (1) using air temperature (Ta) as input for predicting Tw, and (2) using Ta and the periodicity (i.e., day, month and year number). River Tw calibration and validation results derived from air temperature and the periodicity show its potential application. The Bat‐ELM accurately predicts the Tw and surpassed all other models with coefficient of correlation (R) values ranging within the limits of 0.973 to 0.981, and the Nash‐Sutcliffe efficiency (NSE) values will fall within the interval of 0.947 to 0.963. Findings from this research also highlight the robustness of the Bat‐ELM using the periodicity by enhancing its ability to estimate river Tw. |
Author | Heddam, Salim Zounemat‐Kermani, Mohammad Malik, Anurag Tikhamarine, Yazid Elbeltagi, Ahmed Kim, Sungwon Ptak, Mariusz Danandeh Mehr, Ali |
Author_xml | – sequence: 1 givenname: Salim orcidid: 0000-0002-8055-8463 surname: Heddam fullname: Heddam, Salim email: heddamsalim@yahoo.fr organization: Laboratory of Research in Biodiversity Interaction Ecosystem and Biotechnology – sequence: 2 givenname: Sungwon orcidid: 0000-0002-9371-8884 surname: Kim fullname: Kim, Sungwon organization: Dongyang University – sequence: 3 givenname: Ali orcidid: 0000-0003-2769-106X surname: Danandeh Mehr fullname: Danandeh Mehr, Ali organization: Antalya Bilim University – sequence: 4 givenname: Mohammad orcidid: 0000-0002-1421-8671 surname: Zounemat‐Kermani fullname: Zounemat‐Kermani, Mohammad organization: Shahid Bahonar University of Kerman – sequence: 5 givenname: Mariusz orcidid: 0000-0003-1225-1686 surname: Ptak fullname: Ptak, Mariusz organization: Adam Mickiewicz University – sequence: 6 givenname: Ahmed orcidid: 0000-0002-5506-9502 surname: Elbeltagi fullname: Elbeltagi, Ahmed organization: Mansoura University – sequence: 7 givenname: Anurag orcidid: 0000-0002-0298-5777 surname: Malik fullname: Malik, Anurag organization: Regional Research Station – sequence: 8 givenname: Yazid orcidid: 0000-0001-6656-5360 surname: Tikhamarine fullname: Tikhamarine, Yazid organization: University of Tamanrasset |
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Snippet | Here, the capability of the Bat algorithm optimised extreme learning machines ELM (Bat‐ELM) is demonstrated for river water temperature (Tw) modelling in the... Abstract Here, the capability of the Bat algorithm optimised extreme learning machines ELM (Bat‐ELM) is demonstrated for river water temperature ( T w )... |
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SubjectTerms | Air temperature Algorithms Artificial neural networks Bat‐ELM Calibration CART Learning Machine learning MLPNN Modelling Multilayer perceptrons Neural networks Periodicity Regression analysis Rivers Robustness Water Water temperature |
Title | Bat algorithm optimised extreme learning machine (Bat‐ELM): A novel approach for daily river water temperature modelling |
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