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

•Modern data-intelligence are developed to predict air temperature.•Geographical information is used as input attributes predictors.•Regional case study in India is investigated using 45 stations.•Air temperature is successfully predicted without climatic information.•Over all, the developed data-intelligence models are exhibited robust outcomes. 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 accordan