  In surveys, the interest lies in estimating finite population parameters such
as population totals and means. In most surveys, some auxiliary information is
available at the estimation stage. This information may be incorporated in the
estimation procedures to increase their precision. In this article, we use
random forests to estimate the functional relationship between the survey
variable and the auxiliary variables. In recent years, random forests have
become attractive as National Statistical Offices have now access to a variety
of data sources, potentially exhibiting a large number of observations on a
large number of variables. We establish the theoretical properties of
model-assisted procedures based on random forests and derive corresponding
variance estimators. A model-calibration procedure for handling multiple survey
variables is also discussed. The results of a simulation study suggest that the
proposed point and estimation procedures perform well in term of bias,
efficiency, and coverage of normal-based confidence intervals, in a wide
variety of settings. Finally, we apply the proposed methods using data on radio
audiences collected by M\'ediam\'etrie, a French audience company.
