Multilayer perceptron-based predictive model using wavelet transform for the reconstruction of missing rainfall data

The quality and completeness of rainfall data is a critical aspect in time series analysis and for the prediction of future water-related disasters. An accurate estimation of missing data is essential for better rainfall prediction results. This study suggests a novel approach for estimating missing rainfall data using Multilayer Perceptron (MLP) neural networks based on three configurations that are represented by the monsoon season (MS), non-monsoon season (NMS), and non-seasonal variation. For this purpose, first, the rainfall dataset was transformed by the wavelet transform method and then, a mathematical model was created to analyze and predict the transformed data in Seoul, South Korea. Missing rainfall data in three time periods from Seoul station were reconstructed using the transformed rainfall data of the other five stations (e.g., Guroguchung, Daegokgyo, Songjeongden, Dongmakgoljuchajang, and Wallgaegyo). The results showed that using the Coiflet wavelet transform with MLP model (named Coi_MLP) estimated missing data more accurately, which is obtained from the results of statistical criteria including root mean square error, mean absolute error, and correlation coefficient of 1.18, 0.49, and 0.99 for transformed MS data and 0.76, 0.18, and 0.99 for transformed NMS data, respectively. The Coi_MLP model can effectively perform rainfall data reconstruction and predict missing rainfall data accurately, especially when the length of the statistical period is limited to the MS and NMS with different volumes of rainfall.

Automated summary

This study proposes a novel approach using Multilayer Perceptron (MLP) neural networks to estimate missing rainfall data. The approach considers three configurations representing different seasons and variations. The rainfall dataset was transformed using the wavelet transform method and a mathematical model was created to analyze and predict the transformed data. Missing rainfall data in Seoul station were reconstructed using the transformed data from other stations. Results showed that the Coi_MLP model with Coiflet wavelet transform accurately estimated missing data.