Rukmangadh Myana

Jimeng Shi, Zeda Yin, Rukmangadh Myana et al.

Simulating and predicting the water level/stage in river systems is essential for flood warnings, hydraulic operations, and flood mitigations. Physics-based detailed hydrological and hydraulic computational tools, such as HEC-RAS, MIKE, and SWMM, can be used to simulate a complete watershed and compute the water stage at any point in the river system. However, these physics-based models are computationally intensive, especially for large watersheds and for longer simulations, since they use detailed grid representations of terrain elevation maps of the entire watershed and solve complex partial differential equations (PDEs) for each grid cell. To overcome this problem, we train several deep learning (DL) models for use as surrogate models to rapidly predict the water stage. A portion of the Miami River in South Florida was chosen as a case study for this paper. Extensive experiments show that the performance of various DL models (MLP, RNN, CNN, LSTM, and RCNN) is significantly better than that of the physics-based model, HEC-RAS, even during extreme precipitation conditions (i.e., tropical storms), and with speedups exceeding 500x. To predict the water stages more accurately, our DL models use both measured variables of the river system from the recent past and covariates for which predictions are typically available for the near future.

Jimeng Shi, Rukmangadh Myana, Vitalii Stebliankin et al.

Accurate time series forecasting is a fundamental challenge in data science. It is often affected by external covariates such as weather or human intervention, which in many applications, may be predicted with reasonable accuracy. We refer to them as predicted future covariates. However, existing methods that attempt to predict time series in an iterative manner with autoregressive models end up with exponential error accumulations. Other strategies hat consider the past and future in the encoder and decoder respectively limit themselves by dealing with the historical and future data separately. To address these limitations, a novel feature representation strategy -- shifting -- is proposed to fuse the past data and future covariates such that their interactions can be considered. To extract complex dynamics in time series, we develop a parallel deep learning framework composed of RNN and CNN, both of which are used hierarchically. We also utilize the skip connection technique to improve the model's performance. Extensive experiments on three datasets reveal the effectiveness of our method. Finally, we demonstrate the model interpretability using the Grad-CAM algorithm.