Infilling of missing data in groundwater pollution prediction models using statistical methods

Missing data is ubiquitous in hydrology. This phenomenon poses difficulty in the development of data-driven models. Events of missing data in groundwater pollution monitoring networks may occur due to failure of recording devices, malfunctioning of sensors, etc. Handling such missing data implies filling the missing portions of the data structure. Though several studies are available for dealing with missing data in the field of hydrology, literature dealing with such scenarios in groundwater pollution prediction is scarce. This paper assesses four imputation techniques - viz. linear, cubic spline, piece-wise cubic Hermite and modified Akima with cubic Hermite interpolation methods - for developing groundwater pollution prediction models using artificial neural network (ANN). The study employs the development of cascade-forward back-propagation ANN models using missing data ranging from 5% to 75% and evaluating their performance. Results show that imputation techniques can be effective in such circumstances.