This study developed a novel physics-informed deep transfer learning (PDTL) approach, which integrates the physical mechanisms from an analytical model using a transfer learning technique. Results indicate that the DTL model maintains satisfactory performance even in heterogeneous conditions, with uncertainties in observations and sparse training data compared to the deep neural network (DNN) model.