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...
| Publicado en: | Geographical Journal Vol. 189; no. 1; pp. 78 - 90 |
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| Autores principales: | , , , , , , , |
| Formato: | Artículo |
| Publicado: |
Wiley-Blackwell
Mar2023
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=161724399&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 161724399 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00167398 GEO jtl: Geographical Journal issn: 00167398 maglogo: Y pubinfo: dt: Mar2023 vid: 189 iid: 1 pid: 480 pub: Wiley-Blackwell artinfo: ui: 161724399 10.1111/geoj.12478 ppf: 78 ppct: 12 formats: fmt: – @attributes: type: T db: hlh ui: 161724399 – @attributes: type: C db: hlh ui: 161724399 – @attributes: type: P db: hlh ui: 161724399 tig: atl: Bat algorithm optimised extreme learning machine (Bat‐ELM): A novel approach for daily river water temperature modelling. aug: au: Heddam, Salim Kim, Sungwon Danandeh Mehr, Ali Zounemat‐Kermani, Mohammad Ptak, Mariusz Elbeltagi, Ahmed Malik, Anurag Tikhamarine, Yazid affil: Faculty of Science, Agronomy Department, Hydraulics Division, Laboratory of Research in Biodiversity Interaction Ecosystem and Biotechnology, Skikda, Algeria Department of Railroad Construction and Safety Engineering, Dongyang University, Yeongju‐si, Korea Department of Civil Engineering, Antalya Bilim University, Antalya, Turkey Department of Water Engineering, Shahid Bahonar University of Kerman, Kerman, Iran Department of Hydrology and Water Management, Adam Mickiewicz University, Poznań, Poland College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura, Egypt Punjab Agricultural University, Regional Research Station, Punjab,, India Departments of Science and Technology, University of Tamanrasset, Tamanrasset, Algeria su: Poland Machine learning Water temperature Regression trees Atmospheric temperature Algorithms sug: subj: Poland Machine learning Water temperature Regression trees Atmospheric temperature Algorithms keyword: Bat‐ELM CART MLPNN modelling periodicity water temperature Bat‐ELM CART MLPNN modelling periodicity water temperature ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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