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Imputation of Time Series Data via Kohonen Self Organizing Maps in the Presence of Missing Data

depends largely on the quality of hydrological data provided. This study is associated with the development of rainfall data infilling model. The model was developed and validated based on the past fifty years of observed hydrological data in Malaysia. It utilizes the basic principles of Artificial Neural Network (ANN), combined with Nearest Neighbour Imputation (NNeigh) technique. The result of this study shows that the proposed methods are sufficiently robust to cope with vagaries due to sample size and extreme data insufficiency even at 80% missing data level. Further, this study concludes that ANN is superior in patching missing data, specifically rainfall data or any other data with similar characteristics. The fact that the proposed model is able to produce pattern of imputed data which closely resembles that of the observed values where peaks of the observed hydrographs are mostly attainable proves that the proposed model is able to capture daily maximum rainfall series. Annual daily maximum rainfall series is essential in the planning and design of large scale and high risk water resources projects. The ability to capture the maximum rainfall series indicates that the proposed model possesses a remarkably outstanding performance. Supported with robust Artificial Intelligence evidences, this study managed to secure the overall size of data, showing great promise for preparing a clean and complete data set for public domain and has tremendous potential for commercialization.