Modeling monthly pan evaporation process over the Indian central Himalayas: application of multiple learning artificial intelligence model

Anurag Malik, Anil Kumar, Sungwon Kim, Mahsa H. Kashani, Vahid Karimi, Ahmad Sharafati, Mohammad Ali Ghorbani, Nadhir Al-Ansari, Sinan Q. Salih, Zaher Mundher Yaseen, Kwok Wing Chau

Research output: Journal article publicationJournal articleAcademic researchpeer-review

52 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)323-338
Number of pages16
JournalEngineering Applications of Computational Fluid Mechanics
Issue number1
Publication statusPublished - 1 Jan 2020


  • gamma test
  • Indian central Himalayas
  • meteorological variables
  • multiple model strategy
  • Pan evaporation

ASJC Scopus subject areas

  • Computer Science(all)
  • Modelling and Simulation

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