Constructing a knowledge graph of geological hazards literature can facilitate the reuse of geological hazards literature and provide a reference for geological hazard governance. Named entity recognition (NER), as a core technology for constructing a geological hazard knowledge graph, has to face the challenges that named entities in geological hazard literature are diverse in form, ambiguous in semantics, and uncertain in context. This can introduce difficulties in designing practical features during the NER classification. To address the above problem, this paper proposes a deep learning-based NER model; namely, the deep, multi-branch BiGRU-CRF model, which combines a multi-branch bidirectional gated recurrent unit (BiGRU) layer and a conditional random field (CRF) model. In an end-to-end and supervised process, the proposed model automatically learns and transforms features by a multi-branch bidirectional GRU layer and enhances the output with a CRF layer. Besides the deep, multi-branch BiGRU-CRF model, we also proposed a pattern-based corpus construction method to construct the corpus needed for the deep, multi-branch BiGRU-CRF model. Experimental results indicated the proposed deep, multi-branch BiGRU-CRF model outperformed state-of-the-art models. The proposed deep, multi-branch BiGRU-CRF model constructed a large-scale geological hazard literature knowledge graph containing 34,457 entities nodes and 84,561 relations.

Due to the trade-off of temporal resolution and spatial resolution, spatiotemporal image-fusion uses existing high-spatial-low-temporal (HSLT) and high-temporal-low-spatial (HTLS) images as prior knowledge to reconstruct high-temporal-high-spatial (HTHS) images. However, some existing spatiotemporal image-fusion algorithms ignore the issue that the spatial information of HTLS images is insufficient to support the acquisition of spatial information, which leads to the unsatisfactory accuracy of the fusion result. To introduce more spatial information, the algorithm in this article uses Cycle-generative adversarial networks (GANs) to simulate the change process of two HSLT images at k-1 and k+1, and to generate some simulated images between k-1 and k+1. Then, the generated images are selected under the help of HTLS images, and the selected ones are then enhanced with wavelet transform. Finally, the image with spatial information is introduced into the Flexible Spatiotemporal DAta Fusion (FSDAF) framework to improve the performance of spatiotemporal image-fusion. Extensive experiments on two real data sets demonstrate that our proposed method outperforms current state-of-the-art spatiotemporal image-fusion methods.

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 $\text{93.1}\text{\%}$ .

Big service is an extremely important application of service computing to provide predictive and needed services to humans. To operationalize big service, the heterogeneous data collected from Cyber-Physical-Social Systems (CPSS) must be processed efficiently. However, because of the rapid rise in the volume of data, faster and more efficient computational techniques are required. Therefore, in this paper, we propose a multi-order distributed high-order singular-value decomposition method (MDHOSVD) with its incremental computational algorithm. To realize the MDHOSVD, a tensor blocks unfolding integration regulation is proposed. This method allows for the efficient analysis of large-scale heterogeneous data in blocks in an incremental fashion. Using simulation an experimental results, the high efficiency of the proposed data processing and computational method, is demonstrated. Further, a case study about cyber-physical-social systems data processing is illustrated. The proposed MDHOSVD method speeds up data processing, scales with data volume, improves the adaptability and extensibility over data diversity and converts low-level data into actionable knowledge.

The continuous growth in the demand for air transportation exceeds the capacity of existing infrastructure, usually leading to unreliable flight schedules, i.e., long flight delays and uncertainties in arrival/ departure and taxi times. We tackle the problem in this paper by designing an air traffic control algorithm which can accommodate both airport throughput and flight quality of service in terms of flight delay on a given runway. The flight scheduling problem is formulated as an integer linear programming, and then converted to a multi-objective optimization problem which enables the computation of tradeoff between scheduling resolution and time complexity. Based on the multi-objective optimization, a heuristic algorithm considering uncertainties in flight arrival/departure time and taxi time is designed to achieve an improvement in airport throughput and a reduction in flight delay. Extensive simulations show that compared to benchmarking schemes, the proposed uncertaintyaware flight scheduling algorithm can improve the airport throughput and flight delay by up to 12.02% and 31.4%, respectively.

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.