A Novel Hybrid Spatiotemporal Missing Value Imputation Approach for Rainfall Data: An Application to the Ratnapura Area, Sri Lanka

Abstract

Meteorological time series, such as rainfall data, show spatiotemporal characteristics and are often faced with the problem of containing missing values. Discarding missing values or modeling data with missing values causes negative impacts on the accuracy of the final predictions. Hence, accurately estimating missing values by considering the spatiotemporal variations in data has become a crucial step in eco-hydrological modeling. The multi-layer perceptron (MLP) is a promising tool for modeling temporal variation, while spatial kriging (SK) is a promising tool for capturing spatial variations. Therefore, in this study, we propose a novel hybrid approach combining the multi-layer perceptron method and spatial kriging to impute missing values in rainfall data. The proposed approach was tested using spatiotemporal data collected from a set of nearby rainfall gauging stations in the Ratnapura area, Sri Lanka. Missing values are present in collected rainfall data consecutively for a considerably longer period. This pattern has scattered among stations discontinuously over five years. The proposed hybrid model captures the temporal variability and spatial variability of the rainfall data through MLP and SK, respectively. It integrates predictions obtained through both MLP and SK with a novel optimal weight allocation method. The performance of the model was compared with individual approaches, MLP, SK, and spatiotemporal kriging. The results indicate that the novel hybrid approach outperforms spatiotemporal kriging and the other two pure approaches.