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

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@Article{Malik:2020:EACFM,

• author = "Anurag Malik and Anil Kumar and Sungwon Kim and Mahsa H. Kashani and Vahid Karimi and Ahmad Sharafati and Mohammad Ali Ghorbani and Nadhir Al-Ansari and Sinan Q. Salih and Zaher Mundher Yaseen and Kwok-Wing Chau",
• title = "Modeling monthly pan evaporation process over the Indian central Himalayas: application of multiple learning artificial intelligence model",
• journal = "Engineering Applications of Computational Fluid Mechanics",
• year = "2020",
• volume = "14",
• number = "1",
• pages = "323--338",
• keywords = "genetic algorithms, genetic programming, water evaporation, multiple model strategy, gamma test, Asia, Indian central Himalayas, meteorological variables, geotechnical engineering, geoteknik",
• publisher = "Taylor \& Francis",
• ISSN = "1994-2060",
• URL = "http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-77534",
• DOI = "doi:10.1080/19942060.2020.1715845",
• 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), Willmotts Index of agreement (WI), root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), and Legate and McCabe 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 percent at Pantnagar station, and 0.911, 0.975, 0.724, and 0.364 mm/month, 12.297percent 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.",
• bibsource = "OAI-PMH server at www.diva-portal.org",
• identifier = "doi:10.1080/19942060.2020.1715845; Scopus 2-s2.0-85079246577",
• language = "eng",
• oai = "oai:DiVA.org:ltu-77534",
• rights = "info:eu-repo/semantics/openAccess",

}

Genetic Programming entries for Anurag Malik Anil Kumar Sungwon Kim Mahsa Hasanpour Kashani Vahid Karimi Ahmad Sharafati Mohammad Ali Ghorbani Nadhir Al-Ansari Sinan Q Salih Zaher Mundher Yaseen Kwok-Wing Chau

Citations