Investigating lake drought prevention using a DRL-based method

Abstract

Drought and decrease in the level of lakes in recent years due to global warming and excessive use of water resources feeding lakes is of great importance and this research has provided a structure to investigate this issue. First, the information required for simulating lake drought is provided with strong references and necessary assumptions. Entity-Component-System (ECS) structure has been used for simulation which can consider assumptions flexibly in simulation. Three major users (i.e., Industry, agriculture, and Domestic users) consume water from ground water and surface water (i.e., streams, rivers and lakes). Lake Mead has been considered for simulation, and the information necessary to investigate its drought has also been provided. The results are presented in the form of scenario-based design and optimal strategy selection. For optimal strategy selection a deep reinforcement algorithm is developed to select the best set of strategies among all possible projects. These results can provide a better view of how to plan to prevent lake drought.

How hospitals response to disasters; a conceptual deep reinforcement learning approach

“…Deep reinforcement learning is an approach to dynamically optimize the problem while the agents try to take actions to maximize the rewards and minimize the costs and is used in different elds [6]. We use the method implemented in Investigating lake drought prevention using a DRL-based method which is illustrated in the next sessions [7]. In each time step, the agent uses a reward-maximization policy to lower the penalties.…”

Development of a Signal-Free Intersection Control System for CAVs and Corridor Level Impact Assessment

“…According to the astounding performance of deep reinforcement learning (DRL) in decision making in stochastic environments within the last few years in different areas from image processing to drought prevention, this relatively new method is deployed in this paper as an optimization logic to manage vehicles at the intersection [4]. In this method, each approaching vehicle to the intersection is considered a separate agent capable of coordinating with other agents if required.…”