In geophysics, crowdsourcing is an emerging non-traditional environmental monitoring approach that encourages contributions of data from individual citizens. Because of their reliance on undertrained citizens and imprecise low-cost sensors, crowdsourced data applications suffer from different types of noises that can deteriorate the overall monitoring accuracy. In this study, we propose a machine learning approach for automatic Crowdsourced data Quality Control (CSQC) by detecting and removing noisy data points in spatially and temporally discrete crowdsourced observations. We design a set of features from the original and interpolated rainfall data, and apply them to train and test the CSQC models based on both supervised and non-supervised machine learning algorithms. Performances of the CSQC models under various scenarios assuming no further retraining are also tested (hereafter referred to as transferability). The results based on synthetic but realistic data show that the CSQC model can significantly reduce the overall rainfall estimation error. Under the stationary assumption, CSQC models based on both supervised and unsupervised algorithms can have decent performances in noisy data identification and overall rainfall estimation error reduction; however, if the model is transferred to other cities with different rainfall structure or noise composition (without retraining), the supervised Multi-Layer Perceptrons (MLPs) turns out to be the best performing one.