Jien Zhang

Aishwarya Sarkar, Jien Zhang, Chaoqun Lu et al.

Extracting and meticulously analyzing geo-spatiotemporal features is crucial to recognize intricate underlying causes of natural events, such as floods. Limited evidence about hidden factors leading to climate change makes it challenging to predict regional water discharge accurately. In addition, the explosive growth in complex geo-spatiotemporal environment data that requires repeated learning by the state-of-the-art neural networks for every new region emphasizes the need for new computationally efficient methods, advanced computational resources, and extensive training on a massive amount of available monitored data. We, therefore, propose HydroDeep, an effectively reusable pretrained model to address this problem of transferring knowledge from one region to another by effectively capturing their intrinsic geo-spatiotemporal variance. Further, we present four transfer learning approaches on HydroDeep for spatiotemporal interpretability that improve Nash-Sutcliffe efficiency by 9% to 108% in new regions with a 95% reduction in time.

Aishwarya Sarkar, Jien Zhang, Chaoqun Lu et al.

Due to limited evidence and complex causes of regional climate change, the confidence in predicting fluvial floods remains low. Understanding the fundamental mechanisms intrinsic to geo-spatiotemporal information is crucial to improve the prediction accuracy. This paper demonstrates a hybrid neural network architecture - HydroDeep, that couples a process-based hydro-ecological model with a combination of Deep Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) Network. HydroDeep outperforms the independent CNN's and LSTM's performance by 1.6% and 10.5% respectively in Nash-Sutcliffe efficiency. Also, we show that HydroDeep pre-trained in one region is adept at passing on its knowledge to distant places via unique transfer learning approaches that minimize HydroDeep's training duration for a new region by learning its regional geo-spatiotemporal features in a reduced number of iterations.