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Mathematical modeling of dam-breach flow can provide a better understanding of dam failure events, which in turn helps people to reduce potential losses. In the present study, the smooth particle hydrodynamics (SPH) modeling approach was employed to simulate the three-dimensional (3D) partial-breach dam-break flow using two different viscosity models: the artificial viscosity and sub-particle-scale models. The validated and best-performing SPH model was further employed to conduct numerical experiments for various scenarios, which generated a comprehensive dataset. The current work also presents a novel time-series field-reconstruction deep learning (DL) approach: Time Series Convolutional Neural Input Network (TSCNIN) for modeling the transient process of partial-breach dam-break flow and for providing the complete flow field. This approach was constructed based on the long short-term memory and convolutional neural network algorithms with additional input layers. A DL-based model was trained and validated using the numerical data, and tested using two additional unseen scenarios. The results demonstrated that the DL-based model can accurately and efficiently predict the transient water inundation process, and model the influence of dam-break gaps. This study provided a new avenue of simulating partial-breach dam-break flow using the time-series DL approaches and demonstrated the capability of the TSCNIN algorithm in reconstructing the complete fields of transient variables. [ABSTRACT FROM AUTHOR]

Accurate estimation of potential evapotranspiration (ET 0) is important for the sound design of irrigation schedules, management of water resources, assessment of hydrological drought, and research on atmospheric variations. The present study proposed a novel deep learning (DL) approach for daily ET 0 estimations with limited daily climate data: HS- LSTM. This approach was constructed based on a classic ET 0 model and a long short-term memory neural network (LSTM). Specifically, the Hargreaves-Samani (HS) model was employed as the classic model, and the predictors were restricted to the daily maximum and minimum air temperature data. Ground truth data for ET 0 were employed to train, validate, and test the models. Traditional machine learning (ML) algorithms comprising adaptive neuro-fuzzy inference system (ANFIS), genetic programming (GP), multi-gene genetic programming (MGGP), and one-dimensional CNN (1D-CNN), as well as the HS-ML models (HS-ANFIS, HS-GP, HS-MGGP, HS-1D-CNN), were also established and assessed for daily ET 0 estimations. Compared to the other tested approaches, the errors of the HS-LSTM technique significantly decreased, demonstrating that the novel HS-LSTM approach significantly outperformed the other techniques beyond the study area (in Songliao Basin, Northeast China, which is a semi-humid zone with temperate continental climate). The developed models can then be used to estimate future ET 0 with only air temperature forecasts, which can be readily obtained from public weather forecasts. The present study provides a new and promising strategy that can provide more accurate estimations of daily ET 0 with limited meteorological data, along with significant implications for enhancing atmospheric research. • We propose a novel HS-LSTM approach for improving daily ET 0 estimation. • The approach was applied in Northeast China with a temperate continental climate. • The proposed approach performed better than traditional machine learning approaches. • The developed models can accurately estimate daily ET 0 using only air temperature data. [ABSTRACT FROM AUTHOR]

• A novel 2D-CNN approach for predicting river depth fields was proposed. • The 2D-CNN performed much better than classical machine learning algorithms. • The correlations between 2D-CNN based water depths and ground truths exceeded 0.99. Rapid prediction of spatially distributed hydrological variables, such as water depths in rivers, is an important but challenging task. This study proposes a novel matrix-based deep learning approach for predicting spatial distribution of water depths in rivers. The proposed approach was constructed based on a two-dimensional (2D) convolutional neural network (CNN) with a new architecture that was specifically designed for providing spatial distribution maps. A numerical dataset was established based on a field cruise and two-dimensional hydraulic modeling for different scenarios, and numerical experiments were designed to predict spatial distribution of water depths for different scenarios using the adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM), genetic programming (GP), multi-gene genetic programming (MGGP), one-dimensional CNN (1D-CNN), and the proposed CNN algorithms. The results showed that the proposed CNN approach captured both the large-scale and small-scale spatial patterns remarkably well, and it outperformed the other approaches. This study shows that the 2D CNN algorithm is better than the classical machine learning (ML) algorithms for inundation modeling. The proposed approach is thus a promising tool for providing rapid predictions of spatial distribution of water depths in river systems and can potentially be leveraged to predict other spatially distributed hydrological variables. [ABSTRACT FROM AUTHOR]