  Data acquisition and recording in the form of databases are routine
operations. The process of collecting data, however, may experience
irregularities, resulting in databases with missing data. Missing entries might
alter analysis efficiency and, consequently, the associated decision-making
process. This paper focuses on databases collecting information related to the
natural environment. Given the broad spectrum of recorded activities, these
databases typically are of mixed nature. It is therefore relevant to evaluate
the performance of missing data processing methods considering this
characteristic. In this paper we investigate the performances of several
missing data imputation methods and their application to the problem of missing
data in environment. A computational study was performed to compare the method
missForest (MF) with two other imputation methods, namely Multivariate
Imputation by Chained Equations (MICE) and K-Nearest Neighbors (KNN). Tests
were made on 10 pretreated datasets of various types. Results revealed that MF
generally outperformed MICE and KNN in terms of imputation errors, with a more
pronounced performance gap for mixed typed databases where MF reduced the
imputation error up to 150%, when compared to the other methods. KNN was
usually the fastest method. MF was then successfully applied to a case study on
Quebec wastewater treatment plants performance monitoring. We believe that the
present study demonstrates the pertinence of using MF as imputation method when
dealing with missing environmental data.
