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Modeling monthly pan evaporation process over the Indian central Himalayas: application of multiple learning artificial intelligence model
Department of Soil and Water Conservation Engineering, College of Technology, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India.
Department of Soil and Water Conservation Engineering, College of Technology, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India.
Department of Railroad Construction and Safety Engineering, Dongyang University, Yeongju, Republic of Korea.
Department of Water Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran.
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2020 (English)In: Engineering Applications of Computational Fluid Mechanics, ISSN 1994-2060, E-ISSN 1997-003X, Vol. 14, no 1, p. 323-338Article in journal (Refereed) Published
Abstract [en]

The potential of several predictive models including multiple model-artificial neural network (MM-ANN), multivariate adaptive regression spline (MARS), support vector machine (SVM), multi-gene genetic programming (MGGP), and ‘M5Tree’ were assessed to simulate the pan evaporation in monthly scale (EPm) at two stations (e.g. Ranichauri and Pantnagar) in India. Monthly climatological information were used for simulating the pan evaporation. The utmost effective input-variables for the MM-ANN, MGGP, MARS, SVM, and M5Tree were determined using the Gamma test (GT). The predictive models were compared to each other using several statistical criteria (e.g. mean absolute percentage error (MAPE), Willmott's Index of agreement (WI), root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), and Legate and McCabe’s Index (LM)) and visual inspection. The results showed that the MM-ANN-1 and MGGP-1 models (NSE, WI, LM, RMSE, MAPE are 0.954, 0.988, 0.801, 0.536 mm/month, 9.988% at Pantnagar station, and 0.911, 0.975, 0.724, and 0.364 mm/month, 12.297% at Ranichauri station, respectively) with input variables equal to six were more successful than the other techniques during testing period to simulate the monthly pan evaporation at both Ranichauri and Pantnagar stations. Thus, the results of proposed MM-ANN-1 and MGGP-1 models will help to the local stakeholders in terms of water resources management.

Place, publisher, year, edition, pages
UK: Taylor & Francis Group, 2020. Vol. 14, no 1, p. 323-338
Keywords [en]
Pan evaporation, multiple model strategy, gamma test, Indian central Himalayas, meteorological variables
National Category
Geotechnical Engineering
Research subject
Soil Mechanics
Identifiers
URN: urn:nbn:se:ltu:diva-77534DOI: 10.1080/19942060.2020.1715845OAI: oai:DiVA.org:ltu-77534DiVA, id: diva2:1388888
Note

Validerad;2020;Nivå 2;2020-02-03 (johcin)

Available from: 2020-01-28 Created: 2020-01-28 Last updated: 2020-02-03Bibliographically approved

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Al-Ansari, Nadhir

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