Survey of different data-intelligent modeling strategies for forecasting air temperature using geographic information as model predictors
Article
Article Title | Survey of different data-intelligent modeling strategies for forecasting air temperature using geographic information as model predictors |
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ERA Journal ID | 41630 |
Article Category | Article |
Authors | Sanikhani, Hadi (Author), Deo, Ravinesh C. (Author), Samui, Pijush (Author), Kisi, Ozgur (Author), Mert, Chian (Author), Mirabbasi, Rasoul (Author), Gavili, Siavash (Author) and Yaseen, Zaher Mundher (Author) |
Journal Title | Computers and Electronics in Agriculture |
Journal Citation | 152, pp. 242-260 |
Number of Pages | 19 |
Year | 2018 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISSN | 0168-1699 |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.compag.2018.07.008 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0168169917315090 |
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. |
Keywords | air temperature model; geographic information; energy modelling; data-intelligent models |
ANZSRC Field of Research 2020 | 460207. Modelling and simulation |
469999. Other information and computing sciences not elsewhere classified | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | University of Kurdistan, Iran |
School of Agricultural, Computational and Environmental Sciences | |
Ton Duc Thang University, Vietnam | |
Ilia State University, United States | |
International Black Sea University, Georgia | |
Shahrekord University, Iran | |
University of Tehran, Iran | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q4wxv/survey-of-different-data-intelligent-modeling-strategies-for-forecasting-air-temperature-using-geographic-information-as-model-predictors
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