Contents lists available at ScienceDirect Computers and Electronics in Agriculture journal homepage: www.elsevier.com/locate/compag Original papers Survey of different data-intelligent modeling strategies for forecasting air temperature using geographic information as model predictors Hadi Sanikhani a , Ravinesh C. Deo b , Pijush Samui c,d , Ozgur Kisi e , Cihan Mert f , Rasoul Mirabbasi g , Siavash Gavili h , Zaher Mundher Yaseen i, ⁎ a University of Kurdistan, Faculty of Agriculture, Water Eng. Dept., Sanandaj, Iran b University of Southern Queensland, Institute of Agriculture and Environment, School of Agricultural Computational and Environmental Sciences, QLD 4300, Australia c Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Viet Nam d Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Viet Nam e Ilia State University, Faculty of Natural Sciences and Engineering, Tbilisi, Georgia f International Black Sea University, Faculty of Computer Technologies and Engineering, Tbilisi, Georgia g Shahrekord University, Faculty of Agriculture, Water Eng. Dept., Shahrekord, Iran h University of Tehran, College of Abureyhan, Water Eng. Dept., Tehran, Iran i Sustainable Developments in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Viet Nam ARTICLE INFO Keywords: Air temperature model Geographic information Energy modelling Data-intelligent models ABSTRACT Air temperature modelling is a paramount task for practical applications such as agricultural production, de- signing energy-efficient buildings, harnessing of solar energy, health-risk assessments, and weather prediction. This paper entails the design and application of data-intelligent models for air temperature estimation without climate-based inputs, where only the geographic factors (i.e., latitude, longitude, altitude, & periodicity or the monthly cycle) are used in the model design procedure performed for a large spatial study region of Madhya Pradesh, central India. The evaluated data-intelligent models considered are: generalized regression neural network (GRNN), multivariate adaptive regression splines (MARS), random forest (RF), and extreme learning machines (ELM), where the forecasted results are cross-validated independently at 11 sparsely distributed sites. Observed and forecasted temperature is benchmarked with the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe’s coefficient (E), Legates & McCabe’s Index (LMI), and the spatially-represented temperature maps. In accordance with statistical metrics, the temperature forecasting accuracy of the GRNN model exceeds that of the MARS, RF and ELM models, as did the overall areal- averaged results for all tested sites. In terms of the global performance indicator (GPI; as a universal metric combining the expanded uncertainty, U 95 and t-statistic at 95% confidence interval with conventional metrics, bias error, R 2 , RMSE) providing a complete assessment of the site-averaged results, the GRNN model yielded a GPI = 0.0181 vs. 0.0451, 0.1461 and 0.6736 for the MARS, RF and ELM models, respectively, which concurred with deductions made using U 95 and t-statistic. Spatial maps for the cool winter, hot summer and monsoon seasons also confirmed the preciseness of the GRNN model, as did the 12-monthly average annual maps, and the inter-model evaluation of the most accurate and the least accurate sites using Taylor diagrams comparing the RMSE-centered difference and the correlations with observed data. In accordance with the results, the study ascertains that the GRNN model was a qualified data-intelligent tool for temperature estimation without a need for climate-based inputs, at least in the present investigation, and this model can be explored for its utility in energy management, building and construction, agriculture, heatwave studies, health and other socio-economic areas, particularly in data-sparse regions where only geographic and topographic factors are utilized for tem- perature forecasting. 1. Introduction Air temperature is a meteorological variable that affects hydrologic, atmospheric and energy cycles. Hence, the prediction of temperature is a crucial task for studying the dynamics of inter-connected components of the atmosphere. Because of the low density and non-uniform https://doi.org/10.1016/j.compag.2018.07.008 Received 5 December 2017; Received in revised form 16 June 2018; Accepted 5 July 2018 ⁎ Corresponding author. E-mail address: yaseen@tdt.edu.vn (Z.M. Yaseen). Computers and Electronics in Agriculture 152 (2018) 242–260 0168-1699/ © 2018 Elsevier B.V. All rights reserved. T