Integrating coupled simulation of surface water and groundwater with Artificial Intelligence

Surface water and groundwater, integral to the hydrological cycle, engage in complex hydraulic interactions and frequent transformations. Isolating surface water and groundwater systems in individual studies often fails to capture and analyse their interrelationships, limiting the comprehensive understanding of regional water resources. Additionally, conventional physics-based coupled models encounter challenges arising from the complexities and non-linearity of interactions, impeding their accuracy in simulation results. To address this challenge, this thesis proposes a novel framework that integrates artificial intelligence and physics-based coupled models to simulate variations in surface water and groundwater, establishing a foundation for integrated water resource management. Specifically, the study develops a boundary-coupled framework to model interactions between surface water and groundwater. In this framework, a data-driven deep learning model is employed to simulate surface water flow. Additionally, physics-based analytical models are used to describe groundwater movement in riparian zones, while simplifying river behaviour to a Dirichlet boundary condition to assimilate data from the surface water model. Subsequently, the simulated values from analytical solutions serve as the source data, while groundwater observation data is employed as the target data. A transfer learning model is then be utilized to learn the features of the source data and, in conjunction with the target dataset, facilitate the prediction and regression of groundwater. Finally, the framework is applied at the watershed scale to predict and model catchment-scale surface water flow and groundwater head. In this framework, the thesis assesses the influence of various input variables on surface water prediction, explores the effect of groundwater layer heterogeneity, and validates the effectiveness of the deep transfer learning approach, particularly in catchment-scale predictions. The main conclusions are as follows: 1. The selection of model inputs greatly influences accuracy. The PCA method effectively enhances the precision of the deep RNN model, especially in scenarios with numerous input variables. It achieves this by distilling essential information, categorizing original data into several comprehensive variables. 2. The two-layer structure significantly influences groundwater flow responses to hydrological events. During recharge events with a less permeable upper layer, lateral discharge to the river is hindered, directing more groundwater downward into the more permeable lower layer. Conversely, when the upper layer is more permeable, greater lateral flow into the river occurs, with less downward flow into the less permeable lower layer. During a flood event with a less permeable upper layer, river water predominantly infiltrates the more permeable lower layer initially, then flows upward into the upper layer, creating a vertical flow. The direction of this flow reverses during the recession period. However, this phenomenon is not evident when the upper layer is more permeable than the lower layer. 3. The transfer learning method can enhance the capacity of analytical solutions for heterogeneous aquifers. By integrating analytical knowledge with the neural network, the analytical solution-transfer learning method significantly improves hydraulic head prediction accuracy. Even for very sparse training data, the analytical solution-transfer learning method still performs more satisfactorily than the traditional deep learning method. 4. The analytical solution-transfer learning method is also effective at the catchment scale. The analytical solution-transfer learning method can obtain more accuracy and robust results than traditional deep learning methods with the same training dataset

Spatial-Temporal-Aware Safe Multi-Agent Reinforcement Learning of Connected Autonomous Vehicles in Challenging Scenarios

Communication technologies enable coordination among connected and autonomous vehicles (CAVs). However, it remains unclear how to utilize shared information to improve the safety and efficiency of the CAV system. In this work, we propose a framework of constrained multi-agent reinforcement learning (MARL) with a parallel safety shield for CAVs in challenging driving scenarios. The coordination mechanisms of the proposed MARL include information sharing and cooperative policy learning, with Graph Convolutional Network (GCN)-Transformer as a spatial-temporal encoder that enhances the agent's environment awareness. The safety shield module with Control Barrier Functions (CBF)-based safety checking protects the agents from taking unsafe actions. We design a constrained multi-agent advantage actor-critic (CMAA2C) algorithm to train safe and cooperative policies for CAVs. With the experiment deployed in the CARLA simulator, we verify the effectiveness of the safety checking, spatial-temporal encoder, and coordination mechanisms designed in our method by comparative experiments in several challenging scenarios with the defined hazard vehicles (HAZV). Results show that our proposed methodology significantly increases system safety and efficiency in challenging scenarios.Comment: This paper has been accepted by the 2023 IEEE International Conference on Robotics and Automation (ICRA 2023). 6 pages, 5 figure

Verification of arbitrary entangled states with homogeneous local measurements

Quantum state verification (QSV) is the task of using local measurements only to verify that a given quantum device does produce the desired target state. Up to now, certain types of entangled states can be verified efficiently or even optimally by QSV. However, given an arbitrary entangled state, how to design its verification protocol remains an open problem. In this work, we present a systematic strategy to tackle this problem by considering the locality of what we initiate as the choice-independent measurement protocols, whose operators can be directly achieved when they are homogeneous. Taking several typical entangled states as examples, we demonstrate the explicit procedures of the protocol design using standard Pauli projections. Moreover, our framework can be naturally extended to other tasks such as the construction of entanglement witness, and even parameter estimation.Comment: 6+7 pages, 1 figure; Comments are welcome

Response of granite residual soil slopes under dry–wet cycles

Granite residual soil is widely distributed in the southern coastal areas of China, and the slopes of granite residual soil are prone to instability and failure under the alternating action of rainfall and drying, which will cause great disasters to human society. In order to study the response mechanism of granite residual soil slopes under the alternating action of rainfall–drying–static–rainfall (RDSR), this study conducted indoor scaling model tests to analyze the response during dry and wet cycles. This study presented the response process of the slope under the influence of dry and wet cycles and discussed the change laws of slope deformation, water content, and matric suction. The results show that, under the alternating action of rainfall–drying–static–rainfall, 1) the network cracks on the slope form a dominant channel for rainwater seepage, which is the main reason for the rapid decline in soil anti-sliding ability within a short time; 2) at a rainfall intensity of 1.7–2.4 mm/min, the erosion effect of rain on the slope is obviously stronger than that of osmotic erosion, and the surface erosion failure of the granite residual soil slope tends to occur without an obvious sliding surface; 3) after the loss of matric suction over a certain period, the phenomenon of channeling and loss failure on the slope serve as a sufficient condition for slope instability failure but is not a necessary condition. The above research results are expected to provide the basis and reference for preventing and controlling landslide hazards in granite residual soil slopes under similar conditions