Abstract: Dynamic generation of climate projections is provided. The method comprises receiving past climate data of a first spatial resolution. The past climate data of the first spatial resolution is converted to past climate data of a second spatial resolution. A machine learning model is trained with a deep learning algorithm with a training set of the data to generate a trained model object that maps a relationship between the past climate data of the first spatial resolution and the first climate data of the second spatial resolution. The trained model object is validated with a validation set of the data. The trained model object is applied to climate projections of the second spatial resolution to generate climate projections of the first spatial resolution.

Abstract: Various systems, mediums, and methods to process requests involve retrieving a first pattern detection model that is configured to detect a plurality of first patterns based on features of the first patterns. Processing the requests also includes retrieving a plurality of second patterns and selecting a plurality of second features for detecting the plurality of second patterns. Processing the requests further includes generating a second pattern detection model by incorporating one or more of the second feature into the first pattern detection model to create an interim pattern detection model and to train the interim model by second patterns. Then a request having a request pattern is received and based on the second detection model a pattern detection score is generated for the received request. The request is processed based on the detection score. In some embodiments, the user activity patterns are associated with the requests.