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|Title:||Modeling monthly pan evaporation process over the Indian central Himalayas : application of multiple learning artificial intelligence model||Authors:||Malik, A
|Issue Date:||2020||Source:||Engineering applications of computational fluid mechanics, 2020, v. 14, no. 1, p. 323-338||Abstract:||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.||Keywords:||Pan evaporation
Multiple model strategy
Indian central himalayas
|Publisher:||Taylor & Francis||Journal:||Engineering applications of computational fluid mechanics||ISSN:||1994-2060||EISSN:||1997-003X||DOI:||10.1080/19942060.2020.1715845||Rights:||© 2020 The Author(s).
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use,distribution, and reproduction in any medium, provided the original work is properly cited.
The following publication Anurag Malik, Anil Kumar, Sungwon Kim, Mahsa H. Kashani, VahidKarimi, Ahmad Sharafati, Mohammad Ali Ghorbani, Nadhir Al-Ansari, Sinan Q. Salih, ZaherMundher Yaseen & Kwok-Wing Chau (2020) Modeling monthly pan evaporation processover the Indian central Himalayas: application of multiple learning artificial intelligencemodel, Engineering Applications of Computational Fluid Mechanics, 14:1, 323-338 is available at https://dx.doi.org/10.1080/19942060.2020.1715845
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