Rainfall is increasing in frequency and intensity due to climate change. Hydrological models exist that can report bottlenecks in urban infrastructures. However, these require accurate rainfall estimations with high temporal and spatial resolution. The fulfillment of these requirements is challenged due to high costs. This can be solved with cost-effective acoustic rainfall sensors, that can be densely distributed over a city. A (machine learning) prediction model is then needed to infer rainfall from acoustic data. It is infeasible to create or calibrate a seperate model to the specific characteristics of every acoustic sensor. This research focuses on domain adaptation techniques that transfer a model designed for one sensor, called the source, to a characteristically different one, called the target. A two-stage regressor model is devised that estimates rainfall intensity from acoustic data. In a supervised learning setting, this gave an average $R^2$ score of 0.67. Transfer Component Analysis (TCA) has been used as a semi-supervised domain adaptation technique, where no annotations from the target sensor are needed. This resulted in an average $R^2$ of 0.59. Finally, a supervised domain adaptation technique, where some annotations are used, is proposed using TCA and a mean matching method. Two flavours are researched. In the batched approach a small selection of target annotations are gathered, where after the rainfall is predicted with a delay. The online approach predicts in real-time, but adjusts the prediction model when a new target annotation is available. These resulted in an average $R^2$ of 0.52 and 0.32, respectively.