Survey of different data-intelligent modeling strategies for forecasting air temperature using geographic information as model predictors

Sanikhani, Hadi and Deo, Ravinesh C. ORCID: https://orcid.org/0000-0002-2290-6749 and Samui, Pijush and Kisi, Ozgur and Mert, Chian and Mirabbasi, Rasoul and Gavili, Siavash and Yaseen, Zaher Mundher (2018) Survey of different data-intelligent modeling strategies for forecasting air temperature using geographic information as model predictors. Computers and Electronics in Agriculture, 152. pp. 242-260. ISSN 0168-1699


Abstract

Air temperature modelling is a paramount task for practical applications such as agricultural production, designing 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, U95 and t-statistic at 95% confidence interval with conventional metrics, bias error, R2, 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 U95 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 temperature forecasting.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to Published version, in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Current - Institute for Life Sciences and the Environment (1 Aug 2018 -)
Faculty/School / Institute/Centre: Current - Institute for Life Sciences and the Environment (1 Aug 2018 -)
Date Deposited: 22 Jul 2018 23:49
Last Modified: 13 Jun 2019 05:41
Uncontrolled Keywords: air temperature model; geographic information; energy modelling; data-intelligent models
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080110 Simulation and Modelling
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970105 Expanding Knowledge in the Environmental Sciences
Identification Number or DOI: 10.1016/j.compag.2018.07.008
URI: http://eprints.usq.edu.au/id/eprint/34595

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