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This article presents a machine learning-based approach for the prediction of 7-day maximum and 1-day minimum air temperatures in India. In this approach, a Gradient-boosting model is developed using latitude, longitude and altitude as model features. Raw data for this study consisted of hourly air temperature data collected over 30 years across 20,168 grid points within India. As part of the predictive methodology, cluster analysis was performed initially, which helped in obtaining homogeneous regions. Three different approaches empirical clustering, statewise clustering and k-mean clustering, were used. The model features pertaining to individual clusters were used with a gradient-boosting approach. Statistical analysis was conducted to check the accuracy of model predictions based on different clustering techniques. In all cases, predictions made at the global level resulted in poor predictions. In most cases, the results obtained with k-mean clustering showed that increasing the number of clusters improved the predictive accuracy. Furthermore, predictive accuracy with statewise or k-mean clustering was dependent on several features involved. The proposed predictive models have a very simple structure that requires the least input (i.e. geographical indicators). Hence the same can be used for faster and accurate computation of 7-day maximum and 1-day minimum air temperature. [ABSTRACT FROM AUTHOR]

As per the performance grading scheme, the selection of asphalt binder for a particular location requires information on seven-day maximum and one-day minimum pavement temperatures. Pavement surface temperatures are usually related to the surrounding air temperature. This study presents a methodology for developing air temperature predictive models using high resolution long-term weather data of India. Gene expression programming (GEP), an evolutionary computing algorithm, was used to evaluate the expressions governing the air temperature as a function of latitude, longitude, elevation, relative humidity, wind speed, solar radiation, and rainfall intensity. A new methodology to evaluate the optimum tree depth for achieving reasonably high accuracy but at reasonably smaller tree depth was also proposed. Statistical analysis involving comparing the goodness of fit and distribution of the prediction error was conducted to understand the prediction capability of the proposed models. The statistical analysis proved the reasonably high predictive power of the gene expressions corresponding to the optimum tree depth. The proposed seven-day maximum and one-day minimum air temperature predictive models have a very simple structure that can be used by field engineers for hand calculation with little effort. [ABSTRACT FROM AUTHOR]