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In this webinar, Stone Environmental Modeler Dr. Jens Kiesel introduces the Random Forest machine learning algorithm for hydrological predictions. He briefly shares machine learning techniques and discuss the pros and cons of the Random Forest algorithm. The core focus will be on input data processing, applying the algorithm, assessing performance, and identifying the importance of the predictor variables targeted to streamflow simulations. Jens shares insights into lessons learned and pitfalls in using Random Forest and provides a simple code example to get you started implementing your own Random Forest machine learning model.