Spatial interpolation methods for estimating monthly rainfall distribution in Thailand

Abstract Spatial interpolation methods usually differ in their underlying mathematical concepts, each with inherent advantages and drawbacks depending on the properties of data. This paper, therefore, aims to compare and evaluate the performances of well-established interpolation techniques for estimating monthly rainfall data in Thailand. The selected methods include the inverse distance-based method, multiple linear regression (MLR), artificial neural networks (ANN), and ordinary kriging (OK). The technique of searching nearest stations is additionally imposed for some aforementioned schemes. The k -fold cross-validation method is exploited to assess the efficiency of each method, then the metric scores, RMSE, and MAE are used for comparisons. The results suggest the ANN might be the least favorite as it underperforms in many folds. While the OK method provides the most accurate prediction, the inverse distance weighting (IDW), particularly inverse exponential weighting (IEW), and MLR are considerably comparative. Overall, IEW is plausible for monthly rainfall estimation of Thailand because it is less computationally expensive than the OK and its flexible computation.