Modeling monthly pan evaporation process over the Indian central Himalayas

application of multiple learning artificial intelligence model

Document identifier: oai:DiVA.org:ltu-77534
Access full text here:10.1080/19942060.2020.1715845
Keyword: Engineering and Technology, Civil Engineering, Geotechnical Engineering, Teknik och teknologier, Samhällsbyggnadsteknik, Geoteknik, Pan evaporation, Multiple model strategy, Gamma test, Indian central Himalayas, Meteorological variables, Soil Mechanics
Publication year: 2020
Relevant Sustainable Development Goals (SDGs):
SDG 9 Industry, innovation and infrastructureSDG 13 Climate actionSDG 6 Clean water and sanitation
The SDG label(s) above have been assigned by OSDG.ai

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.

Authors

Anurag Malik

Department of Soil and Water Conservation Engineering, College of Technology, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India
Other publications >>

Anil Kumar

Department of Soil and Water Conservation Engineering, College of Technology, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India
Other publications >>

Sungwon Kim

Department of Railroad Construction and Safety Engineering, Dongyang University, Yeongju, Republic of Korea
Other publications >>

Mahsa H. Kashani

Department of Water Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran
Other publications >>

Vahid Karim

Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran
Other publications >>

Ahmad Sharafati

Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
Other publications >>

Mohammad Ali Ghorban

Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran
Other publications >>

Nadhir Al-Ansari

Luleå tekniska universitet; Geoteknologi
Other publications >>

Sinan Q. Salih

Institute of Research and Development, Duy Tan University, Da Nang, Vietnam
Other publications >>

Zaher Mundher Yaseen

Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam
Other publications >>

Kwok-Wing Chau

Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hong Kong, People’s Republic of China
Other publications >>

Documents attached


Click on thumbnail to read

Record metadata

Click to view metadata