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We present deep learning models for cross polar cap potential (CPCP) by applying multilayer perceptron (MLP) and long short-term memory (LSTM) networks to estimate CPCP based on Super Dual Auroral Radar Network (SuperDARN) measurements. Three statistical parameters are proposed, which are root-mean-square error (RMSE), mean absolute error and linear correlation coefficient (LC), to validate and test the models by measuring their performance on an independent data set that was withheld from thedoi:10.1029/2020ea001219 fatcat:dvb7npvw3bctffafr44txxhcey