Groundwater level forecasting is essential for groundwater monitoring and sustainable water resource management in urban regions. This study proposes a graph-based spatiotemporal forecasting framework for regional multi-well groundwater prediction us...
Maintaining reliable maritime connectivity is essential to resilient strategic-commodity supply chains, yet link dynamics and external disturbances are often modeled separately. This study develops a dynamic edge-weighted graph convolutional network&...
To overcome the limitations of spatiotemporal feature extraction that are inherent in conventional lightning warning algorithms relying solely on temporal analysis, we propose a novel prediction framework integrating a Graph Convolutional Network (GC...
With the acceleration of urbanization, public transit systems face prominent challenges, including insufficient passenger flow prediction accuracy and low scheduling efficiency. This study analyzes passenger flow variation patterns from both spatial...
With the increasing research of deep learning in the EEG field, it becomes more and more important to fully extract the characteristics of EEG signals. Traditional EEG signal classification prediction neither considers the topological structure betwe...
The manufacturing industry has been operating within a constantly evolving technological environment, underscoring the importance of maintaining the efficiency and reliability of manufacturing processes. Motor-related failures, especially bearing def...
Water distribution networks (WDNs) are critical infrastructures that directly impact urban development and citizens’ quality of life. Due to digitalization technologies, modern networks have evolved towards cyber-physical systems, allowing real...
Snow cover fraction (SCF) reflects the regional snow cover changes in arid mountainous areas. We developed a terrain-aware graph convolutional network–long short-term memory network (GCN-LSTM), where 9 meteorological stations in Xinjiang serve...
Due to the frailty of elderly individuals’ physical condition, falling can lead to severe bodily injuries. Effective fall detection can significantly reduce the occurrence of such incidents. However, current fall detection methods heavily rely...
Accurate and stable power-load forecasting is crucial for optimizing generation scheduling and ensuring the economic and secure operation of power grids. To address the issues of low prediction accuracy and poor robustness during abrupt load changes,...
Critical infrastructures (CI) are essential to modern society, providing vital services such as energy, water, and transportation. However, these systems are increasingly targeted by sophisticated cyberattacks, exploiting vulnerabilities in both IT (...
Temperature is an important part of meteorological factors, which are affected by local and surrounding meteorological factors. Aiming at the problems of significant prediction error and insufficient extraction of spatial features in current temperat...
Stock return prediction is a pivotal yet intricate task in financial markets, challenged by volatility and multifaceted dependencies. This study proposes a hybrid model integrating long short-term memory (LSTM) networks and graph convolutional networ...
The hydraulic pump plays a pivotal role in engineering machinery, and it is essential to continuously monitor its operating status. However, many vital signals for monitoring cannot be directly obtained in practical applications. To address this, we...
Getting soil moisture right matters for fighting drought and stopping land from turning into desert. Aiming at the problems of insufficient spatiotemporal modeling and redundant attention mechanisms in global soil moisture prediction, we built a new...
In recent years, the development and utilization of China’s wind energy resources have been greatly developed, but the large-scale wind power grid connection has brought threats to the safe and stable operation of the power grid. In order to en...
Utilizing neural network models to inverse time-domain electromagnetic signals enables rapid acquisition of electrical structures, a non-intrusive method widely applied in geological and environmental surveys. However, traditional multi-layer percept...
Background: The emission of air pollutants into the atmosphere is a global issue as it contributes to global warming and climate-related issues. Human activities like the burning of fossil fuel influence changes in weather patterns—resulting in...
Buildings are responsible for around 30% and 42% of the consumed energy at the global and European levels, respectively. Accurate building power consumption estimation is crucial for resource saving. This research investigates the combination of grap...
Emotion recognition is increasingly important for applications in mental health and personalized marketing. Traditional methods based on facial and vocal cues lack robustness due to voluntary control, motivating the use of EEG signals that capture ne...
As a convenient, economical, and eco-friendly travel mode, bike-sharing greatly improved urban mobility. However, it is often very difficult to achieve a balanced utilization of shared bikes due to the asymmetric spatio-temporal user demand distribut...
To overcome the limitations of traditional convolutional and recurrent neural networks in capturing spatio-temporal dynamics in flow fields on unstructured grids, this study proposes a novel Spatio-Temporal Graph Neural Network (ST-GNN) model that in...
Forecasting air quality plays a crucial role in preventing and controlling air pollution. It is particularly significant for improving preparedness for heavily polluted weather conditions and ensuring the health and safety of the population. In this...
Residences in poor regions always depend on rain-fed agriculture, so they urgently need suitable tools to make accurate meteorological predictions. Unfortunately, meteorological observations in these regions are usually sparse and irregularly distrib...
Despite considerable research efforts being devoted to the taxi cruising route recommendation (TCRR) problem, existing studies still have some shortcomings. To begin with, the competition and collaboration between taxis are not sufficiently taken int...
Existing research on sentiment classification primarily focuses on textual contextual semantics while neglecting word co-occurrence and syntactic structure information. It also emphasizes semantic analysis of sequential structures but overlooks non-l...
The water and shadow areas in SAR images contain rich information for various applications, which cannot be extracted automatically and precisely at present. To handle this problem, a new framework called Multi-Resolution Dense Encoder and Decoder (M...
Multiple station passenger flow prediction is crucial but challenging for intelligent transportation systems. Recently, deep learning models have been widely applied in multi-station passenger flow prediction. However, flows at the same station in di...
Predicting short-term urban water demand is essential for water resource management and directly impacts urban water resource planning and supply–demand balance. As numerous factors impact the prediction of short-term urban water demand and pre...
Sentiment analysis based on social media text is found to be essential for multiple applications such as project design, measuring customer satisfaction, and monitoring brand reputation. Deep learning models that automatically learn semantic and synt...
Sea surface temperature (SST) prediction plays an important role in scientific research, environmental protection, and other marine-related fields. However, most of the current prediction methods are not effective enough to utilize the spatial correl...
Geothermal energy, a clean and reliable renewable resource, is attracting growing global attention. Predicting geothermal productivity is essential for the sustainable management of geothermal systems in conventional pumping tests, but the water leve...
This study addresses the challenge of accurate short-term bus travel time prediction in data-constrained urban environments. While many existing models rely on extensive external data, we propose a data-efficient spatiotemporal framework that integra...
This study proposes a novel hybrid prediction model (QGCN-LSTM) that combines Quantum Graph Convolutional Networks (QGCN) with classical Long Short-Term Memory (LSTM). The model takes space-time data as input and employs a hierarchical graph-based qu...
The rapid expansion of the logistics industry has led to sustained growth in freight demand across regions. Analyzing the factors that influence truck travel demand and extracting loading/unloading locations from truck trajectory data can offer a the...
Global public safety is increasingly threatened by frequent road traffic accidents, making early accident risk prediction essential for intelligent transportation systems. However, incomplete multimodal data and insufficient spatiotemporal modeling l...
Accurate short-term traffic flow prediction in complex urban road networks is of great significance for capacity organization and dispatch optimization in intelligent transportation systems. Using publicly available historical taxi trip records relea...
Modeling and forecasting online illegal transactions require representations of recurrent temporal variation, uneven spatial activity, and changing local conditions. This paper studies a framework consisting of spatio-temporal assessment, adaptive pe...
The intent classification of Chinese questions about respiratory diseases (CQRD) can not only promote the development of smart medical care, but also strengthen epidemic surveillance. The major core of the intent classification of CQRD is text repres...
To fully leverage the spatiotemporal dynamic correlations in air traffic flow and enhance the accuracy of traffic flow prediction models, thereby providing a more precise basis for perceiving congestion situations in the air route network, a study wa...
Graph neural networks are often applied to load forecasting on the assumption that electrically related meter points should be modeled jointly, but this is rarely tested against a graph-free control, and building the graph usually requires a network...
Traffic flow prediction is an important part of the intelligent transportation system. Accurate traffic flow prediction is of great significance for strengthening urban management and facilitating people’s travel. In this paper, we propose a mo...
Traffic flow prediction is a key issue in intelligent transportation systems. The growing trend in data disclosure has created more potential sources for the input for predictive models, posing new challenges to the prediction of traffic flow in the...
Noise pollution in densely populated urban areas is a major issue that affects both quality of life and public health. This study explores and evaluates the application of deep learning techniques to predict urban noise levels, using the city of Madr...
In general, judging the use/idle state of the wireless spectrum is the foundation for cognitive radio users (secondary users, SUs) to access limited spectrum resources efficiently. Rich information can be mined by the inherent correlation of electrom...
Complex and diverse multi-attribute flight data can provide data-driven opportunities for airport flight delay prediction. However, it is a challenge to effectively and efficiently process multi-attribute flight data. This paper proposes a hybrid dyn...
Accurate and real-time forecasting of the price of oil plays an important role in the world economy. Research interest in forecasting this type of time series has increased considerably in recent decades, since, due to the characteristics of the time...
Malware remains a major threat to computer systems, posing serious risks to security and privacy by stealing sensitive data, disrupting services, and compromising system integrity. Traditional detection methods are often ineffective against rapidly e...
Lehman Brothers’ failure in 2008 demonstrated the importance of understanding interconnectedness in interbank networks. The interbank market plays a significant role in facilitating market liquidity and providing short-term funding for each oth...
In some projects, a lack of data causes problems for presenting an accurate prediction model for surface settlement caused by shield tunneling. Existing models often rely on large volumes of data and struggle to maintain accuracy and reliability in s...
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