Enhancing the applicability of Kohonen Self-Organizing Map (KSOM) estimator for gap-filling in hydrometeorological timeseries data

The Kohonen Self-Organizing Map (KSOM) estimator is prescribed as a useful tool for infilling the missing data in hydrometeorology. However, in this study, when the performance of the KSOM estimator is tested for gap-filling in the streamflow, rainfall, evapotranspiration (ET), and temperature timeseries data, collected from 30 gauging stations in India under missing data situations, it is felt that the KSOM modeling performance could be further improved. Consequently, this study tries to answer the research questions as to whether the length of record of the historical data and its variability has any effect on the performance of the KSOM? Whether inclusion of temporal distribution of timeseries data and the nature of outliers in the KSOM framework enhances its performance further? Subsequently, it is established that the KSOM framework should include the coefficient of variation of the datasets for determination of the number of map units, without considering it as a single value function of the sample data size. This could help to upscale and generalize the applicability of KSOM for varied hydrometeorological data types.