Artificial Neural Networks and Multiple Linear Regression for Filling in Missing Daily Rainfall Data

As demand for more hydrological data has been increasing, there is a need for the development of more accurate and descriptive models. A pending issue regarding the input data of said models is the missing data from observation stations in the field. In this paper, a methodology utilizing ensembles of artificial neural networks is developed with the goal of estimating missing precipitation data in the extended region of Chania, Greece on a daily timestep. In the investigated stations, there have been multiple missing data events, as well as missing data prior to their installation. The methodology presented aims to generate precipitation time series based on observed data from neighboring stations and its results have been compared with a Multiple Linear Regression model as the basis for improvements to standard practice. For each combination of stations missing daily data, an ensemble has been developed. According to the statistical indexes that were calculated, ANN ensembles resulted in increased accuracy compared to the Multiple Linear Regression model. Despite this, the training time of the ensembles was quite long compared to that of the Multiple Linear Regression model, which suggests that increased accuracy comes at the cost of calculation time and processing power. In conclusion, when dealing with missing data in precipitation time series, ANNs yield more accurate results compared to MLR methods but require more time for producing them. The urgency of the required data in essence dictates which method should be used.