The urban sustainable development becomes an essential goal presented in “2030 Agenda for Sustainable development” by the United Nations. Urban land use and land cover (LULC) change prediction, which is a significant indicator to evaluate urban construction strategy, urges to be solved. Remote sensing technique and neural network, are two practical tools widely utilized in urban LULC change prediction. Because of the rapid improvement of remote sensing technique, urban data can be periodically captured in a short time interval. A large amount of data cause the conventional neural networks method having bad efficiency in dealing with it. Moreover, as human activities are in high intensity in the urban area, society related factors require to be taken into consideration in urban land change trend prediction. In this article, a self-adaptive cellular-based deep learning analysis method by utilizing the multisourced data is proposed for urban LULC change prediction. Multisourced data, including weather related data, economy related data, construction related data, and remote sensing data, are normalized and formalized by the proposed self-adaptive cellular-based method. Deep learning long short term memory neural network, which is an advanced model of recurrent neural network and has a strong ability in dealing with sequence data, is utilized to urban LULC change prediction. Remote sensing data captured from 1984 to 2016 are utilized to conduct experiments. Experiment results illustrate that the proposed method can effectively and efficiently make LULC change prediction, and the accuracy is up to $\text{93.1}\text{\%}$ .

A high-intensity storm surge hazard exposes residents and facilities to inundation danger. A continuously operating robust inundation emergency logistics system is vital to guarantee lives and support the control of hazardous materials. A massive inundation area, large number of affected facilities, and instantly changing flooding situation bring tremendous challenges to rescue resource allocation. In this article, a rescue resource distribution scheduling of storm surge inundation logistics is proposed to quantitatively formulate the rescue time minimization problem in emergency logistics. The mixed-integer linear programming (MILP) method is proposed for the emergency logistics scheduling model validation and optimality comparison. To enhance the efficiency of creating a good quality allocation strategy when facing large-scale problems, a deep reinforcement learning algorithm—deep deterministic policy gradient (DDPG)—is utilized to search the solutions. Based on a rescue resource scheduling model of storm surge inundation logistics targeting storm surge Mangkhut in September, 2018, a case study of the Futian District, Shenzhen, China, was conducted to verify the correctness and efficiency of the MILP and the DDPG. The optimal schedule solution designed by the MILP had a duration of 3.5138 h, while the solution calculated by DDPG had a duration of 5.065 h for the rescue. The execution time of DDPG was stable and under a second, while the execution time of the MILP was over two hours.

A marine oil spill is an environmental pollution incident that generally has the attributes of a high speed, widespread, and long duration. It seriously threatens the marine ecological environment and related industries. It is vital to determine the source of the oil leakage so that it may be stopped and related hazards can be reduced. Oil spill accidents in the sea are generally located in offshore and navigation channels. With the rapid development of remote-sensing techniques, oil leak extraction using remote-sensing data has played an essential role in oil spill research. This article proposes a Monte Carlo-based deep Q-transfer-learning network (DQTN) offshore oil leak detection method that uses remote-sensing data. Remote-sensing data are utilized to continuously monitor a marine oil spill on the surface. The estuarine and coastal ocean model is utilized to simulate a marine oil spill event. The deep-Q-network method with offline transferred knowledge is then utilized to determine the marine oil spill source location. In an experiment, based on the Bohai oil spill incident on June 2, 2011, the effectiveness of the remote-sensing-based DQTN marine oil spill search algorithm is verified. The accuracy of the targeted oil spill point is up to 98.97%.

Storm surge hazard annually costs hundreds of lives and billions of dollars in damages in coastal countries. Simulating the storm surge events and alerting flood areas in advance are essential to guarantee the safety of citizens and urban infrastructures. However, due to the complexity of the urban environment, the accuracy of simulation faces challenges. By leveraging the facility of the Internet-of-Things (IoT) technique, the urban flood simulation procedure under storm surge hazard can be improved. In this paper, targeting a coastal city usually under the threaten of the storm surge, a deep reinforcement learning based storm surge flood simulation approach is proposed to simulate the flood situation under the specific level of the storm surge. By analyzing real-time urban flood data and weather data collected by the IoT system, the model of the urban flood can be constructed and refined. A case study based on storm surge happened on 1 July 2019 in Shenzhen is conducted to evaluate the effectiveness of the proposed model. The results demonstrate that the proposed method can effectively simulate the storm surge flood, and the achieved accuracy is up to 97.22%.