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Globally, ocean dumping of chemical waste is a common method of disposal and relies on the assumption that dilution, diffusion, and dispersion at ocean scales will mitigate human exposure and ecosystem impacts. In southern California, extensive dumping of agrochemical waste, particularly chlorinated hydrocarbon contaminants such as DDT, via sewage outfalls and permitted offshore barging occurred for most of the last century. This study compiled a database of existing sediment and fish DDT measurements to examine how this unique legacy of regional ocean disposal translates into the contemporary contamination of the coastal ocean. We used spatiotemporal modeling to derive continuous estimates of sediment DDT contamination and show that the spatial signature of disposal (i.e., high loadings near historic dumping sites) is highly conserved in sediments. Moreover, we demonstrate that the proximity of fish to areas of high sediment loadings explained over half of the variation in fish DDT concentrations. The relationship between sediment and fish contamination was mediated by ecological predictors (e.g., species, trophic ecology, habitat use), and the relative influence of each predictor was context-dependent, with habitat exhibiting greater importance in heavily contaminated areas. Thus, despite more than half a century since the cessation of industrial dumping in the region, local ecosystem contamination continues to mirror the spatial legacy of dumping, suggesting that sediment can serve as a robust predictor of fish contamination, and general ecological characteristics offer a predictive framework for unmeasured species or locations.

Urban air pollution can vary sharply in space and time. However, few monitoring strategies can concurrently resolve spatial and temporal variation at fine scales. Here, we present a new measurement-driven spatiotemporal modeling approach that transcends the individual limitations of two complementary sampling paradigms: mobile monitoring and fixed-site sensor networks. We develop, validate, and apply this model to predict black carbon (BC) using data from an intensive, 100-day field study in West Oakland, CA. Our spatiotemporal model exploits coherent spatial patterns derived from a multipollutant mobile monitoring campaign to fill spatial gaps in time-complete BC data from a low-cost sensor network. Our model performs well in reconstructing patterns at fine spatial and temporal resolution (30 m, 15 min), demonstrating strong out-of-sample correlations for both mobile (Pearson's R ∼ 0.77) and fixed-site measurements (R ∼ 0.95) while revealing features that are not effectively captured by a single monitoring approach in isolation. The model reveals sharp concentration gradients near major emission sources while capturing their temporal variability, offering valuable insights into pollution sources and dynamics.

Accurate station-level numerical weather predictions are critical for disaster prevention and mitigation, with error correction playing an essential role. However, existing correction models struggle to effectively handle the high-dimensional features and complex dependencies inherent in meteorological data. To address these challenges, this paper proposes the dual-branch residual-guided multi-view attention fusion network (DRAF-Net), a novel deep learning-based correction model. DRAF-Net introduces two key innovations: (1) a dual-branch residual structure that enhances the spatial sensitivity of deep high-dimensional features and improves output stability by connecting raw data and shallow features to deep features, respectively; and (2) a multi-view attention fusion mechanism that models spatiotemporal influences, temporal dynamics, and spatial associations, significantly improving the representation of complex dependencies. The effectiveness of DRAF-Net was validated on two real-world datasets comprising observations and predictions from Chinese meteorological stations. It achieved an average RMSE reduction of 83.44% and an average MAE reduction of 84.21% across all eight variables, significantly outperforming other methods. Moreover, extensive studies confirmed the critical contributions of each key component, while visualization results highlighted the model's ability to eliminate anomalous values and improve prediction consistency. The code will be made publicly available to support future research and development. [ABSTRACT FROM AUTHOR]

Estimating surface-level PM2.5 concentrations at any given location is crucial for public health monitoring and cohort studies. Existing models and datasets for this purpose have limited precision, especially on high-concentration days. Additionally, due to the lack of open-source code, generating estimates for other areas and time periods remains cumbersome. We developed a novel deep learning-based model that improves the surface-level PM2.5 concentration estimates by capitalizing on the temporal dynamics of air quality. Specifically, we improve the estimation precision by developing a Long Short-Term Memory (LSTM) network with Attention and integrating multiple data sources, including in situ measurements, remotely sensed data, and wildfire smoke density observations, which improve the model's ability to capture high-concentration events. We rigorously evaluate the model against existing products, demonstrating a 2.2% improvement in overall RMSE, and a 9.8% reduction in RMSE on high-concentration days, highlighting the superior performance of our approach, particularly on high-concentration days. Using the model, we have produced a comprehensive dataset of PM2.5 estimates from 2005 to 2021 for the contiguous United States and are releasing an open-source framework to ensure reproducibility and facilitate further adaptation in air quality studies. [ABSTRACT FROM AUTHOR]

AbstractSpatiotemporal analysis and modeling have long been key foci of geographical information science. In a recent paper, Wu et al. expanded the local spatial modeling technique of multiscale geographically weighted regression (MGWR) to incorporate a temporal component. This new model is named multiscale geographically and temporally weighted regression (MGTWR). Despite the utility of expanding MGWR to incorporate a temporal weighting function in addition to the existing spatial one, the approach developed by Wu et al. has two limitations: the bandwidth selection algorithm cannot guarantee an optimal result and no formulation for the effective number of parameters (ENPs) in the model is given. To address these issues, this paper describes a procedure to derive an optimal temporal and an optimal spatial bandwidth for MGTWR and also develops a formula for the ENPs in the model. The former ensures the reliability of the model outputs while the latter is essential for making reliable inferences from the local parameter estimates generated in the calibration of models by MGTWR. These advances in the MGTWR framework are demonstrated through applications to simulated and real-world data. [ABSTRACT FROM AUTHOR]

Fish spawning phenology is a major concern for conservation and fisheries management. New intensive data sources, such as GPS-based tracking data and high-resolution catch declaration data, are becoming increasingly available in the field of marine ecology. These data benefit from high spatiotemporal resolution and open new research avenues for investigating the interannual variability in fish phenology. In this paper, we demonstrate how an integrated species distribution model informed by commercial catch data combined with spatiotemporal dimension reduction methods known as empirical orthogonal functions (EOFs) can be used to synthesize spatiotemporal signals in fish reproduction phenology. Specifically, we address the following questions: (1) Can we identify seasonal spatial patterns that can be interpreted in terms of reproductive phenology and essential habitats? (2) Can we identify changes in reproductive phenology over time? (3) Are these changes related to environmental drivers? The analysis illustrates the reproductive phenology of three key commercial species in the Bay of Biscay (sole, hake, and sea bass). The EOF analysis emphasized strong seasonal spatiotemporal patterns that correspond to reproduction patterns and feeding patterns. Based on this methodology, we identified seasonal variations in the timing of reproduction, and we related these variations to sea surface temperature, a key driver of fish reproduction. [ABSTRACT FROM AUTHOR]

In video action recognition, effective spatiotemporal modeling is crucial. However, traditional two-stream methods face challenges in integrating spatial information from RGB images and temporary information from optical flow without long-range temporal modelling. To address these limitations, we propose the Deep Fusion Module (DFM), which focuses on the deep fusion of spatial and temporal information and consists of two components. First, we propose an Attention Fusion Module (AFM) to effectively fuse the shallow features obtained from a two-stream network, thereby facilitating the integration of spatial and temporal information. Next, we incorporate a SpatioTemporal Module (STM), comprising a ConvGRU and a 1×1 convolution, to model long-range temporal dependency and fuse spatial-temporal features. Experiments on the UCF101 dataset show that our method achieves 96.5% accuracy, outperforming baseline two-stream models by 0.3%. [ABSTRACT FROM AUTHOR]

French Guiana lacks a dedicated model for developing an early warning system tailored to its entomological contexts. We employed a spatiotemporal modeling approach to predict the risk of Aedes aegypti larvae presence in local households in French Guiana. The model integrated field data on larvae, environmental data obtained from very high-spatial-resolution Pleiades imagery, and meteorological data collected from September 2011 to February 2013 in an urban area of French Guiana. The identified environmental and meteorological factors were used to generate dynamic maps with high spatial and temporal resolution. The study collected larval data from 261 different surveyed houses, with each house being surveyed between one and three times. Of the observations, 41% were positive for the presence of Aedes aegypti larvae. We modeled the Aedes larvae risk within a radius of 50 to 200 m around houses using six explanatory variables and extrapolated the findings to other urban municipalities during the 2020 dengue epidemic in French Guiana. This study highlights the potential of spatiotemporal modeling approaches to predict and monitor the evolution of vector-borne disease transmission risk, representing a major opportunity to monitor the evolution of vector risk and provide valuable information for public health authorities. [ABSTRACT FROM AUTHOR]

Developing a precise and interpretable spatiotemporal model is need for establishing evidence-based adaptation strategies on climate change-driven disasters. This study introduced a diagnostic prediction concept as a generalized modeling framework for enhancing modeling precision and interpretability and demonstrate a case study of estimating forest net primary production (NPP) in a mid-latitude region (MLR) by developing a diagnostic NPP diagnostic prediction model (DNPM). The diagnostic prediction concept starts with modeling meteorology and static environmental data, referred as a prognostic prediction part. Then, its outcome is refined with spatiotemporal residual calibration in the diagnostic prediction part, of which result undergo spatial, temporal, and spatiotemporally explicit validation methods. For the case of DNPM, a prognostic NPP prediction model (PNPM) was set, using a multilinear regression on SPEI 3, temperature, and static environmental features extracted from topography and soil by a random forest. Subsequently, during the diagnostic process of DNPM, we calibrated the primary outcome based on the temporal pattern captured at the time-series residual of PNPM. The results highlighted the superiority of the DNPM over the PNPM. Spatiotemporal validation showed that the DNPM achieved higher accuracy, with Pearson correlation coefficients ([Formula: see text]) ranging from 0.975 to 0.992 and root mean squared error (RMSE) between 38.99 and 70.23 gC/m2/year across all climate zones. Similarly, temporal validation indicated that DNPM outperformed the PNPM, with [Formula: see text] values of 0.233 to 0.494 and RMSE of 46.01 to 70.75 gC/m2/year, compared to the PNPM’s [Formula: see text] values of 0.192 to 0.406 and RMSE of 55.23 to 89.31 gC/m2/year. This study showed enhanced diagnostic prediction concept can be applied to diverse environmental modeling approaches, offering valuable insights for climate adaptation and forest policy formulation. By accurately predicting various environmental targets, including drought and forest NPP, this approach aids in making informed policy decisions across different scales.

We evaluated the spatiotemporal clustering of rapid diagnostic test−positive cholera cases in Uvira, eastern Democratic Republic of the Congo. We detected spatiotemporal clusters that consistently overlapped with major rivers, and we outlined the extent of zones of increased risk that are compatible with the radii currently used for targeted interventions.

This paper presents a systematic literature review, providing a comprehensive taxonomy of Data Augmentation (DA), Transfer Learning (TL), and Self-Supervised Learning (SSL) techniques within the context of Few-Shot Learning (FSL) for EEG signal classification. EEG signals have shown significant potential in various paradigms, including Motor Imagery, Emotion Recognition, Visual Evoked Potentials, Steady-State Visually Evoked Potentials, Rapid Serial Visual Presentation, Event-Related Potentials, and Mental Workload. However, challenges such as limited labeled data, noise, and inter/intra-subject variability have impeded the effectiveness of traditional machine learning (ML) and deep learning (DL) models. This review methodically explores how FSL approaches, incorporating DA, TL, and SSL, can address these challenges and enhance classification performance in specific EEG paradigms. It also delves into the open research challenges related to these techniques in EEG signal classification. Specifically, the review examines the identification of DA strategies tailored to various EEG paradigms, the creation of TL architectures for efficient knowledge transfer, and the formulation of SSL methods for unsupervised representation learning from EEG data. Addressing these challenges is crucial for enhancing the efficacy and robustness of FSL-based EEG signal classification. By presenting a structured taxonomy of FSL techniques and discussing the associated research challenges, this systematic review offers valuable insights for future investigations in EEG signal classification. The findings aim to guide and inspire researchers, promoting advancements in applying FSL methodologies for improved EEG signal analysis and classification in real-world settings. [ABSTRACT FROM AUTHOR]

Bayesian inference tasks continue to pose a computational challenge. This especially holds for spatiotemporal modeling, where high-dimensional latent parameter spaces are ubiquitous. The methodology of integrated nested Laplace approximations (INLA) provides a framework for performing Bayesian inference applicable to a large subclass of additive Bayesian hierarchical models. In combination with the stochastic partial differential equation (SPDE) approach, it gives rise to an efficient method for spatiotemporal modeling. In this work, we build on the INLA-SPDE approach by putting forward a performant distributed memory variant, INLADIST, for large-scale applications. To perform the arising computational kernel operations, consisting of Cholesky factorizations, solving linear systems, and selected matrix inversions, we present two numerical solver options: a sparse CPU-based library and a novel blocked GPU-accelerated approach which we propose. We leverage the recurring nonzero block structure in the arising precision (inverse covariance) matrices, which allows us to employ dense subroutines within a sparse setting. Both versions of INLADIST are highly scalable, capable of performing inference on models with millions of latent parameters. We demonstrate their accuracy and performance on synthetic as well as real-world climate dataset applications. [ABSTRACT FROM AUTHOR]

This work introduces an unsupervised framework for video anomaly detection, leveraging a hybrid deep learning model that combines a vision transformer (ViT) with a convolutional spatiotemporal relationship (STR) attention block. The proposed model addresses the challenges of anomaly detection in video surveillance by capturing both local and global relationships within video frames, a task that traditional convolutional neural networks (CNNs) often struggle with due to their localized field of view. We have utilized a pre-trained ViT as an encoder for feature extraction, which is then processed by the STR attention block to enhance the detection of spatiotemporal relationships among objects in videos. The novelty of this work is utilizing the ViT with the STR attention to detect video anomalies effectively in large and heterogeneous datasets, an important thing given the diverse environments and scenarios encountered in real-world surveillance. The framework was evaluated on three benchmark datasets, i.e., the UCSD-Ped2, CHUCK Avenue, and ShanghaiTech. This demonstrates the model's superior performance in detecting anomalies compared to state-of-the-art methods, showcasing its potential to significantly enhance automated video surveillance systems by achieving area under the receiver operating characteristic curve (AUC ROC) values of 95.6, 86.8, and 82.1. To show the effectiveness of the proposed framework in detecting anomalies in extra-large datasets, we trained the model on a subset of the huge contemporary CHAD dataset that contains over 1 million frames, achieving AUC ROC values of 71.8 and 64.2 for CHAD-Cam 1 and CHAD-Cam 2, respectively, which outperforms the state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Sea Surface Temperature (SST) is of great importance to study several major phenomena due to ocean interactions with other earth systems. Previous studies on SST based on statistical inference methods were less accurate for longer prediction lengths. A considerable number of studies in recent years involve machine learning for SST modeling. These models were able to mitigate this problem to some length by modeling SST patterns and trends. Sequence analysis by decomposition is used for SST forecasting in several studies. Ensemble Empirical Mode Decomposition (EEMD) has been proven in previous studies as a useful method for this. The application of EEMD in spatiotemporal modeling has been introduced as Multidimensional EEMD (MEEMD). The aim of this study is to employ fast MEEMD methods to decompose the SST spatiotemporal dataset and apply a Convolutional Long Short-Term Memory (ConvLSTM)-based model to model and forecast SST. The results show that the fast MEEMD method is capable of enhancing spatiotemporal SST modeling compared to the Linear Inverse Model (LIM) and ConvLSTM model without decomposition. The model was further validated by making predictions from April to May 2023 and comparing them to original SST values. There was a high consistency between predicted and real SST values. [ABSTRACT FROM AUTHOR]

Violence detection is a difficult task because it involves analyzing video clips from multiple security cameras, which are located in various places and operate continuously. When violent crimes occur, a system should be able to reliably detect them in real-time and immediately alert a surveillance team. Currently, researchers employ deep learning models to detect violent behavior. Notably, a large number of deep learning approaches are based on extracting spatio-temporal information from a video by exploiting either 3D Convolutional Neural Networks (CNNs) or multi-stream networks. Despite their success, these techniques require a lot of parameters than 2D CNNs and have high computational complexity. Therefore, we present a simple spatio-temporal attention mechanism combined with a 2D CNN for an effective violence detection system. We propose a Squeeze Temporal Attention block that allows a 2D CNN to learn spatiotemporal features in videos. This effective block uses squeeze and temporal attention modules to summarize a video stream into three channels. In addition, we introduce spatial attention and feature fusion modules to improve the performance of the proposed system. The spatial attention module, Entropy Spatial Module, utilizes an entropy filter and frame differences to focus on spatial regions of the video with more movement. The fusion module parallelizes two dense layers with a 2D CNN to effectively enhance the classifier's performance. As a result, our proposed model achieves improved performance results in terms of accuracy when compared to Long Short-Term Memory, multi-stream networks, and current 3D CNNs. [ABSTRACT FROM AUTHOR]

Microscale research on tourism flows is crucial for controlling such flows, analyzing resource-carrying capacity, and promoting sustainable development in the tourism industry. Current fine-grained monitoring of tourism flows using location-based big data faces challenges, such as inadequate user representation, data acquisition difficulties and spatiotemporal uncertainty. This study presents a method for the spatiotemporal modeling and estimation of regional tourism flows based on the collaborative perception of discrete surveillance videos. The method employed bridges the gap between physical and video image scenes by establishing collaborative perception relationships among multiple devices, thereby enabling the precise modeling and estimation of the dynamic spatiotemporal processes of population movement in the region. Empirical studies in real scenic areas confirm the adaptability of this technology to diverse geographical scenes and ensure the accuracy of the spatiotemporal flow estimation. This study addresses the challenges of high sampling costs and low spatiotemporal accuracy in regional fine-grained crowd estimation and offers technical support for near real-time dynamic crowd modeling and monitoring. The experimental results have the potential to assist in applications, such as tourism flow management, dynamic regulation and the risk analysis of group activities in scenic areas. [ABSTRACT FROM AUTHOR]

Short-term residential load forecasting (STRLF) holds great significance for the stable and economic operation of distributed power systems. Different households in the same region may exhibit similar consumption patterns owing to the analogous environmental parameters. Incorporating the spatiotemporal correlations can enhance the load forecasting performance of individual households. To this end, a spatiotemporal graph attention (STGA)-enabled Transformer is proposed for multivariate, multi-step residential load forecasting in this paper. Specifically, the multiple residential loads are cast to a graph and a Transformer with a graph sequence-to-sequence (Seq2Seq) structure is employed to model the multi-step load forecasting problem. Gated fusion-based STGA blocks are embedded in the encoder and decoder of the Transformer to extract dynamic spatial correlations and non-linear temporal patterns among multiple residential loads. A transform attention block is further designed to transfer historical graph observations into future graph predictions and alleviate the error propagation between the encoder and decoder. The embedding of multiple attention modules in the Seq2Seq framework allows us to capture the spatiotemporal correlations between residents and achieve confident inference of load values several steps ahead. Numerical simulations on residential data from three different regions demonstrate that the developed Transformer method improves multi-step load forecasting by 14.7% at least, compared to the state-of-the-art benchmarks.

Predicting the trajectory of geographical events, such as wildfire spread, presents a formidable task due to the dynamic associations among influential biophysical factors. Geo-events like wildfires frequently display short and long-range spatial and temporal correlations. Short-range effects are the direct contact and near-contact spread of the fire front. Long-range effects are represented by processes such as spotting, where firebrands carried by the wind ignite fires distant from the flaming front, altering the shape and speed of an advancing fire front. This study addresses these modeling challenges by clearly defining and analyzing the scale-dependent spatiotemporal dynamics that influence wildfire spread, focusing on the interplay between biophysical factors and fire behavior. We propose two unique attention-based spatiotemporal models using Convolutional Long Short-Term Memory (ConvLSTM) networks. These models are designed to learn and capture a range of local to global and short and long-range spatiotemporal correlations. The proposed models were tested on two datasets: a high-resolution wildfire spread dataset produced with a semi-empirical percolation model and a satellite observed wildfire spread data in California 2012–2021. Results indicate that attention-based models accurately predict fire front movements that are consistent with known wildfire spread-biophysical dynamics. Our research suggests there is considerable potential for attention mechanisms to capture the spatiotemporal behavior of wildfire spread, with model transferability, that can guide rapid deployment of wildfire management operations. We also highlight directions for future studies that focus on how the self-attention mechanism could enhance model performance for a range of geospatial applications.

Spam messages affect the normal communication of mobile phone users and public security. Fake base station (FBS) is the main channel for sending spam messages. Accurate spatiotemporal prediction of spam messages from FBS is important for implementing short-term spam prevention measures by the police. However, existing models used for spam messages modeling often focus on capturing spatial dependencies based on distance, thus failing to fully exploit spatial dependencies hidden in the data. Meanwhile, they encounter difficulties in mitigating the impact of excessive zero data. Therefore, based on the crowdsourced spam message data, a temporal graph-convolutional-network model (FBS-TGCN) is developed for the spatiotemporal prediction of spam messages from FBS. This model includes graph convolutional network (GCN) and gated temporal convolutional network (TCN) to capture the spatial and temporal dependencies alternatively. We further combine a weighted adjacency matrix and a self-adaptive adjacency matrix to capture both distance-based spatial dependencies and hidden spatial dependencies. A weighted loss function is used to mitigate the influence of excessive zero data. Experimental results using the spam dataset in Beijing demonstrate the effectiveness of the proposed model when compared to the baseline models, especially in the prediction of spam messages quantity and high spamming areas. [ABSTRACT FROM AUTHOR]

Deep learning models have a high application value in runoff forecasting, but their prediction mechanism is difficult to interpret and their computational cost is high when dealing with complex hydrological relationships, limiting their feasibility in hydrological process mechanism analysis. To address these concerns, the paper first introduces an attention mechanism (AM) for building a long short-term memory network (LSTM) model with AM in the hidden layer (AM-LSTM). The AM-LSTM model employs attention layers to improve information extraction from hidden layers, resulting in a more accurate representation of the relationships between runoff-related elements. Furthermore, in the hidden layers of the AM-LSTM model, interpretable spatiotemporal attention units are established, which not only improves the model's prediction accuracy but also provides interpretability to the forecasting process. Furthermore, parallelization techniques are used in the paper to address the issue of model runtime cost. Simultaneously, to address the accuracy degradation caused by parallelization, the paper employs wavelet denoising (WD) techniques and builds the WD-AM-LSTM model. This accomplishment enables the runoff forecasting model to predict runoff in real time and with high accuracy. Based on validation using ten-day runoff data from the Huanren Reservoir in the Hun River's middle reaches, the results show that, with two layers and an eight-batch size, the AM-LSTM model outperforms the LSTM model in capturing spatiotemporal runoff features. During the model testing phase, the AM-LSTM model improves the MAE, RMSE, and NSE performance metrics by 8.46%, 13%, and 3.82%, respectively. The WD-AM-LSTM model effectively mitigates the noise impact caused by data parallelization under the conditions of two layers and a batch size of 512, achieving the same level of prediction performance while reducing computational cost by 92.01%. By incorporating attention mechanisms and wavelet denoising techniques, this study obtains high-speed and accurate predictions with interpretable results. It expands the deep learning models' applicability in ten-day runoff forecasting work. [ABSTRACT FROM AUTHOR]

Traffic-related urban air pollution is a pressing concern in Tehran, Iran, with severe health implications. This study aimed to create a dynamic spatiotemporal model to predict changes in urban traffic-related air pollution in Tehran using a land use regression (LUR) model. Two datasets were employed to model the spatiotemporal distribution of gaseous traffic-related pollutants—sulfur dioxide (SO2), nitrogen dioxide (NO2), and carbon monoxide (CO). The first dataset incorporated remote sensing data, including land surface temperature (LST), the normalized difference vegetation index (NDVI), apparent thermal inertia (ATI), population density, altitude, land use, road density, road length, and distance to highways. The second dataset excluded remote sensing data, relying solely on population density, altitude, land use, road density, road length, and distance to highways. The LUR model was constructed using both datasets at three different buffer distances: 250, 500, and 1000 m. Evaluation based on the R2 index revealed that the 1000-m buffer distance achieved the highest accuracy. Without remote sensing data, R2 values for CO, NO2, and SO2 pollutants were respectively spring (0.77, 0.79, 0.51), summer (0.59, 0.71, 0.59), and winter (0.41, 0.52, 0.59). With remote sensing data, R2 values were respectively spring (0.82, 0.84, 0.74), summer (0.72, 0.87, 0.62), and winter (0.53, 0.59, 0.72). Incorporating remote sensing data notably improved the accuracy of modeling CO, NO2, and SO2 during all three seasons. The central, southern, and southeastern regions of Tehran consistently exhibited the highest pollutant concentrations throughout the year, while the northern areas maintained comparatively better air quality. [ABSTRACT FROM AUTHOR]

The article presents results of using remote sensing images and machine learning to map and assess land potential based on time-series of potential Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) composites. Land potential here refers to the potential vegetation productivity in the hypothetical absence of short-term anthropogenic influence, such as intensive agriculture and urbanization. Knowledge on this ecological land potential could support the assessment of levels of land degradation as well as restoration potentials. Monthly aggregated FAPAR time-series of three percentiles (0.05, 0.50 and 0.95 probability) at 250 m spatial resolution were derived from the 8-day GLASS FAPAR V6 product for 2000-2021 and used to determine long-term trends in FAPAR, as well as to model potential FAPAR in the absence of human pressure. CCa 3 million training points sampled from 12,500 locations across the globe were overlaid with 68 bio-physical variables representing climate, terrain, landform, and vegetation cover, as well as several variables representing human pressure including: population count, cropland intensity, nightlights and a human footprint index. The training points were used in an ensemble machine learning model that stacks three base learners (extremely randomized trees, gradient descended trees and artificial neural network) using a linear regressor as meta-learner. The potential FAPAR was then projected by removing the impact of urbanization and intensive agriculture in the covariate layers. The results of strict cross-validation show that the global distribution of FAPAR can be explained with an R2 of 0.89, with the most important covariates being growing season length, forest cover indicator and annual precipitation. From this model, a global map of potential monthly FAPAR for the recent year (2021) was produced, and used to predict gaps in actual vs. potential FAPAR. The produced global maps of actual vs. potential FAPAR and long-term trends were each spatially matched with stable and transitional land cover classes. The assessment showed large negative FAPAR gaps (actual lower than potential) for classes: urban, needle-leave deciduous trees, and flooded shrub or herbaceous cover, while strong negative FAPAR trends were found for classes: urban, sparse vegetation and rainfed cropland. On the other hand, classes: irrigated or post-flooded cropland, tree cover mixed leaf type, and broad-leave deciduous showed largely positive trends. The framework allows land managers to assess potential land degradation from two aspects: as an actual declining trend in observed FAPAR and as a difference between actual and potential vegetation FAPAR. [ABSTRACT FROM AUTHOR]

In perfusion MRI, image voxels form a spatially organized network of systems, all exchanging indicator with their immediate neighbors. Yet the current paradigm for perfusion MRI analysis treats all voxels or regions‐of‐interest as isolated systems supplied by a single global source. This simplification not only leads to long‐recognized systematic errors but also fails to leverage the embedded spatial structure within the data. Since the early 2000s, a variety of models and implementations have been proposed to analyze systems with between‐voxel interactions. In general, this leads to large and connected numerical inverse problems that are intractible with conventional computational methods. With recent advances in machine learning, however, these approaches are becoming practically feasible, opening up the way for a paradigm shift in the approach to perfusion MRI. This paper seeks to review the work in spatiotemporal modelling of perfusion MRI using a coherent, harmonized nomenclature and notation, with clear physical definitions and assumptions. The aim is to introduce clarity in the state‐of‐the‐art of this promising new approach to perfusion MRI, and help to identify gaps of knowledge and priorities for future research. [ABSTRACT FROM AUTHOR]

The most often utilized construction material worldwide is concrete. Extensive experiments are carried out each year to study the physical, mechanical, and chemical properties of concrete, which are costly and time-consuming. This study focuses on avoiding redundant tests by applying the machine learning (ML) method to predict temperature-dependent polymer concrete qualities. In this work, as a strong tool of the ML method, a deep neural network (DNN) is used to examine the five temperature-dependent mechanical characteristics of concrete, including Poisson's ratio, Young's modulus, specific heat, coefficient of thermal expansion, and thermal conductivity. A 5-fold cross-validation method was used to verify the strategy in this research and get rid of split bias in testing and training. This study demonstrates the material properties of temperature-dependent polymer concrete followed by mathematical modeling of a steel-polymer concrete panel used for various civil applications. Temperature-dependent equations are determined using constitutive heat transfer and Cartesian coordinates. In addition, a thermal shock load acts on the upper part, and the lower part becomes an isothermal state with no heat flow. In order to account for the limited speed at which temperature waves travel, two distinct theories of generalized thermoelasticity are employed: the Lord-Shulman (LS) and the Green-Lindsay (GL) theories. The Fast Laplace Inverse Transform Method (FLITM) is used to transfer the results from the Laplace domain to the time domain. In addition, a three-dimensional differential quadrature approach (3D-DQA) using three Chebyshev-Gaussian-Robat functions for solving temperature-dependent equations is presented. In conclusion, some suggestions for improving the stability of polymer concrete panels are detailed and will be compiled in a future manual.

Existing regulatory pollutant monitoring networks rely on a small number of centrally located measurement sites that are purposefully sited away from major emission sources. While informative of general air quality trends regionally, these networks often do not fully capture the local variability of air pollution exposure within a community. Recent technological advancements have reduced the cost of sensors, allowing air quality monitoring campaigns with high spatial resolution. The 100×100 black carbon (BC) monitoring network deployed 100 low-cost BC sensors across the 15 km2 West Oakland, CA community for 100 days in the summer of 2017, producing a nearly continuous site-specific time series of BC concentrations which we aggregated to one-hour averages. Leveraging this dataset, we employed a hierarchical spatio-temporal model to accurately predict local spatio-temporal concentration patterns throughout West Oakland, at locations without monitors (average cross-validated hourly temporal R 2=0.60). Using our model, we identified spatially varying temporal pollution patterns associated with small-scale geographic features and proximity to local sources. In a sub-sampling analysis, we demonstrated that fine scale predictions of nearly comparable accuracy can be obtained with our modeling approach by using ~30% of the 100×100 BC network supplemented by a shorter-term high-density campaign.

The article presents results of using remote sensing images and machine learning to map and assess land potential based on time-series of potential Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) composites. Land potential here refers to the potential vegetation productivity in the hypothetical absence of short–term anthropogenic influence, such as intensive agriculture and urbanization. Knowledge on this ecological land potential could support the assessment of levels of land degradation as well as restoration potentials. Monthly aggregated FAPAR time-series of three percentiles (0.05, 0.50 and 0.95 probability) at 250 m spatial resolution were derived from the 8-day GLASS FAPAR V6 product for 2000–2021 and used to determine long-term trends in FAPAR, as well as to model potential FAPAR in the absence of human pressure. CCa 3 million training points sampled from 12,500 locations across the globe were overlaid with 68 bio-physical variables representing climate, terrain, landform, and vegetation cover, as well as several variables representing human pressure including: population count, cropland intensity, nightlights and a human footprint index. The training points were used in an ensemble machine learning model that stacks three base learners (extremely randomized trees, gradient descended trees and artificial neural network) using a linear regressor as meta-learner. The potential FAPAR was then projected by removing the impact of urbanization and intensive agriculture in the covariate layers. The results of strict cross-validation show that the global distribution of FAPAR can be explained with an R2 of 0.89, with the most important covariates being growing season length, forest cover indicator and annual precipitation. From this model, a global map of potential monthly FAPAR for the recent year (2021) was produced, and used to predict gaps in actual vs. potential FAPAR. The produced global maps of actual vs. potential FAPAR and long-term trends were each spatially matched with stable and transitional land cover classes. The assessment showed large negative FAPAR gaps (actual lower than potential) for classes: urban, needle-leave deciduous trees, and flooded shrub or herbaceous cover, while strong negative FAPAR trends were found for classes: urban, sparse vegetation and rainfed cropland. On the other hand, classes: irrigated or post-flooded cropland, tree cover mixed leaf type, and broad-leave deciduous showed largely positive trends. The framework allows land managers to assess potential land degradation from two aspects: as an actual declining trend in observed FAPAR and as a difference between actual and potential vegetation FAPAR.

Focused Ultrasound (FUS)-triggered nano-sized drug delivery, as a smart stimuli-responsive system for treating solid tumors, is computationally investigated to enhance localized delivery of drug and treatment efficacy. Integration of thermosensitive liposome (TSL), as a doxorubicin (DOX)-loaded nanocarrier, and FUS, provides a promising drug delivery system. A fully coupled partial differential system of equations, including the Helmholtz equation for FUS propagation, bio-heat transfer, interstitial fluid flow, drug transport in tissue and cellular spaces, and a pharmacodynamic model is first presented for this treatment approach. Equations are then solved by finite element methods to calculate intracellular drug concentration and treatment efficacy. The main objective of this study is to present a multi-physics and multi-scale model to simulate drug release, transport, and delivery to solid tumors, followed by an analysis of how FUS exposure time and drug release rate affect these processes. Our findings not only show the capability of model to replicate this therapeutic approach, but also confirm the benefits of this treatment with an improvement of drug aggregation in tumor and reduction of drug delivery in healthy tissue. For instance, the survival fraction of tumor cells after this treatment dropped to 62.4%, because of a large amount of delivered drugs to cancer cells. Next, a combination of three release rates (ultrafast, fast, and slow) and FUS exposure times (10, 30, and 60 min) was examined. Area under curve (AUC) results show that the combination of 30 min FUS exposure and rapid drug release leads to a practical and effective therapeutic response. [ABSTRACT FROM AUTHOR]

Accurate PM2.5 concentration prediction is essential for environmental control management, therefore numerous air quality monitoring stations have been established, which generate multiple time series with spatio-temporal correlation. However, the statistical distribution of data from different monitoring stations varies widely, which needs to provide higher flexibility in the feature extraction stage. Moreover, the spatio-temporal correlation and mutation characteristics of the time series are difficult to capture. To this end, an adaptive spatio-temporal prediction network (ASTP-NET) is proposed, in which the encoder adaptively extracts the input data features, then captures the spatio-temporal dependencies and dynamic changes of the time series, the decoder part maps the encoded features into a predicted future time series representation, while an objective function is proposed to measure the overall fluctuations of the model's multi-step prediction. In this paper, ASTP-NET is evaluated based on the Xi'an air quality dataset, and the results show that the model outperforms other baseline methods. [ABSTRACT FROM AUTHOR]

Although increasing hidden layers can improve the ability of a neural network in modeling complex nonlinear relationships, deep layers may result in degradation of accuracy due to the problem of vanishing gradient. Accuracy degradation limits the applications of deep neural networks to predict continuous variables with a small sample size and/or weak or little invariance to translations. Inspired by residual convolutional neural network in computer vision, we developed an encoder-decoder full residual deep network to robustly regress and predict complex spatiotemporal variables. We embedded full shortcuts from each encoding layer to its corresponding decoding layer in a systematic encoder-decoder architecture for efficient residual mapping and error signal propagation. We demonstrated, theoretically and experimentally, that the proposed network structure with full residual connections can successfully boost the backpropagation of signals and improve learning outcomes. This novel method has been extensively evaluated and compared with four commonly used methods (i.e., plain neural network, cascaded residual autoencoder, generalized additive model, and XGBoost) across different testing cases for continuous variable predictions. For model evaluation, we focused on spatiotemporal imputation of satellite aerosol optical depth with massive nonrandomness missingness and spatiotemporal estimation of atmospheric fine particulate matter [Formula: see text] (PM2.5). Compared with the other approaches, our method achieved the state-of-the-art accuracy, had less bias in predicting extreme values, and generated more realistic spatial surfaces. This encoder-decoder full residual deep network can be an efficient and powerful tool in a variety of applications that involve complex nonlinear relationships of continuous variables, varying sample sizes, and spatiotemporal data with weak or little invariance to translation.

French Guiana lacks a dedicated model for developing an early warning system tailored to its entomological contexts. We employed a spatiotemporal modeling approach to predict the risk of Aedes aegypti larvae presence in local households in French Guiana. The model integrated field data on larvae, environmental data obtained from very high-spatial-resolution Pleiades imagery, and meteorological data collected from September 2011 to February 2013 in an urban area of French Guiana. The identified environmental and meteorological factors were used to generate dynamic maps with high spatial and temporal resolution. The study collected larval data from 261 different surveyed houses, with each house being surveyed between one and three times. Of the observations, 41% were positive for the presence of Aedes aegypti larvae. We modeled the Aedes larvae risk within a radius of 50 to 200 m around houses using six explanatory variables and extrapolated the findings to other urban municipalities during the 2020 dengue epidemic in French Guiana. This study highlights the potential of spatiotemporal modeling approaches to predict and monitor the evolution of vector-borne disease transmission risk, representing a major opportunity to monitor the evolution of vector risk and provide valuable information for public health authorities.

Land use regression (LUR) models are widely used in epidemiological and environmental studies to estimate humans’ exposure to air pollution within urban areas. However, the early models, developed using linear regressions and data from fixed monitoring stations and passive sampling, were primarily designed to model traditional and criteria air pollutants and had limitations in capturing high-resolution spatiotemporal variations of air pollution. Over the past decade, there has been a notable development of multi-source observations from low-cost monitors, mobile monitoring, and satellites, in conjunction with the integration of advanced statistical methods and spatially and temporally dynamic predictors, which have facilitated significant expansion and advancement of LUR approaches. This paper reviews and synthesizes the recent advances in LUR approaches from the perspectives of the changes in air quality data acquisition, novel predictor variables, advances in model-developing approaches, improvements in validation methods, model transferability, and modeling software as reported in 155 LUR studies published between 2011 and 2023. We demonstrate that these developments have enabled LUR models to be developed for larger study areas and encompass a wider range of criteria and unregulated air pollutants. LUR models in the conventional spatial structure have been complemented by more complex spatiotemporal structures. Compared with linear models, advanced statistical methods yield better predictions when handling data with complex relationships and interactions. Finally, this study explores new developments, identifies potential pathways for further breakthroughs in LUR methodologies, and proposes future research directions. In this context, LUR approaches have the potential to make a significant contribution to future efforts to model the patterns of long- and short-term exposure of urban populations to air pollution.

Objective: Vehicular lane-changing is one of the riskiest driving maneuvers. Since vehicular automation is quickly becoming a reality, it is crucial to be able to identify when such a maneuver can turn into a risky situation. Recently, it has been shown that a qualitative approach: the Point Descriptor Precedence (PDP) representation, is able to do so. Therefore, this study aims to investigate whether the PDP representation can detect hazardous micro movements during lane-changing maneuvers in a situation of structural congestion in the morning and/or evening.Method: The approach involves analyzing a large real-world traffic dataset using the PDP representation and adding safety distance points to distinguish subtle movement patterns.Results: Based on these subtleties, we label four out of seven and five out of nine lane-change maneuvers as risky during the selected peak and the off-peak traffic hours respectively.Conclusions: The results show that the approach can identify risky movement patterns in traffic. The PDP representation can be used to check whether certain adjustments (e.g., changing the maximum speed) have a significant impact on the number of dangerous behaviors, which is important for improving road safety. This approach has practical applications in penalizing traffic violations, improving traffic flow, and providing valuable information for policymakers and transport experts. It can also be used to train autonomous vehicles in risky driving situations. [ABSTRACT FROM AUTHOR]

House price prediction has traditionally been approached using linear or spatial linear hedonic models and focused on big cities. In this study, we developed a flexible spatiotemporal model (FSTM) to explore the spatiotemporal characteristics of the residential house price and the impact factors in middle-small cities. The FSTM integrated both spatial and temporal components of the residential house price, accounted for its spatiotemporal characteristics, and reproduced its spatial variability and temporal trends. The results showed that the governmental policy had a significant influence on the house price and led to the characteristics being different from those in big cities. The significant factors also included the density of roads, the density of banks, density of supermarkets, the area used by public and user shared area within a building. This study implied that FSTM provided the potential for spatiotemporal prediction of the residential house price in the middle-small cities.

This paper develops an extension of spatiotemporal models that handle count data using nonhomogeneous Poisson processes. In this new proposal, we incorporate a seasonal cycle component in the definition of the intensity function to control possible effects produced by the occurrence of the event of interest in regular periods. The seasonal cycle can cause problems in estimating the shape parameter of the Weibull and generalized Goel intensity functions. This shape parameter serves to confront the research hypothesis that seeks to identify a trend in the occurrence rate of an event of interest. In the case of the Weibull intensity function, a value significantly equal to one of the shape parameters indicates a constant rate of occurrence, less than one indicates a decreasing rate, and greater than one indicates an increasing rate. In the case of the Goel intensity function, parameter values less than or equal to one indicate a decreasing occurrence rate, and values greater than one indicate the presence of a change point. We also built a spatial model using the Musa-Okumoto intensity function as an alternative to approximate counting processes for which there is a decreasing trend in the occurrence rate of the event of interest. We estimated the parameters of the proposed method from a Bayesian perspective. Finally, we fitted the proposed model and compared it with other approximations to analyze the frequency of extreme rainfall in the northern region of the states of Maranhão and Piauí in northeastern Brazil over ten years. Among the main results, we found that (1) the proposed method has proven superior in terms of fit and prediction performance than the other models, and (2) unlike other approximations, the proposed model does not detect changes in the rate of extreme rainfall occurrences. [ABSTRACT FROM AUTHOR]

With the rapid development of social networks, video data has been growing explosively. As one of the important social mediums, spatiotemporal characteristics of videos have attracted considerable attention in recommendation system and video understanding. In this paper, we discuss the video moment retrieval (VMR) task, which locates moments in a video based on different textual queries. Existing methods are of two pipelines: 1) proposal-free approaches are mainly in modifying multi-modal interaction strategy; 2) proposal-based methods are dedicated to designing advanced proposal generation paradigm. Recently, contrastive representation learning has been successfully applied to the field of video understanding. From a new perspective, we propose a new VMR framework, named spatiotemporal contrastive network (STCNet), to learn discriminative boundary features of video grounding by contrast learning. To be specific, we propose a boundary matching sampling module for dense negative sample sampling. The contrast learning can refine the feature representations in the training phase without any additional cost in inference. On three public datasets, Charades-STA, ActivityNet Captions and TACoS, our proposed method performs competitive performance. [ABSTRACT FROM AUTHOR]

Taxi demand prediction is essential to build efficient traffic transportation systems for smart city. It helps to properly allocate vehicles, ease the traffic pressure and improve passengers' experience. Traditional taxi demand prediction methods mostly rely on time-series forecasting techniques, which cannot model the nonlinearity embedded in data. Recent studies start to combine the Euclidean spatial features through grid-based methods. By considering the spatial correlations among different regions, we can capture how the temporal events have impacts on those with adjacent links or intersections and improve prediction precision. Some graph-based models are proposed to encode the non-Euclidean correlations as well. However, the temporal periodicity of data is often overlooked, and the study units are usually constructed as oversimplified grids. In this paper, we define places with specific semantic and humanistic experiences as study units, using a fuzzy set method based on adaptive kernel density estimation. Then, we introduce dual temporal gated multi-graph convolution network to predict the future taxi demand. Specifically, multi-graph convolution is used to model spatial correlations with graphs, including the neighborhood, functional similarities and landscape similarities based on street view images. As for the temporal dependencies modeling, we design the dual temporal gated branches to capture information hidden in both previous and periodic observations. Experiments on two real-world datasets show the effectiveness of our model over the baselines. [ABSTRACT FROM AUTHOR]

In 2022, the 22nd International Conference on Aquatic Invasive Species returned to Europe as a hybrid event. The conference welcomed representatives from 41 countries, including the largest group of students and early career professionals of any ICAIS conference. The theme of the conference was "Global Climate Change Amplifies Aquatic Invasive Species Impacts." Keynote speakers discussed ongoing invasions and the damaging synergy between climate change and invasions, presented on the value of risk assessment, outreach, and education, highlighted new ways to estimate the economic costs of invasions, and told attendees about some of the work of NGOs in managing invasions. This special issue includes a selection of papers that were presented at the conference, along with a few related papers that were not presented which touch on the risk of specific vectors, improvements in survey techniques to detect new or spreading invaders, and advances in management approaches to control AIS. [ABSTRACT FROM AUTHOR]

Functional data are generally curves indexed over a time domain, and land-use regression (LUR) is a promising spatial technique for generating high-resolution spatial estimation of retrospective long-term air pollutants. We developed a methodology for the novel functional land-use regression (FLUR) model, which provides high-resolution spatial and temporal estimations of retrospective pollutants. Long-term fine particulate matter (PM2.5) in the megacity of Tehran, Iran, was used as the practical example. The hourly measured PM2.5 concentrations were averaged for each hour and in each air monitoring station. Penalized smoothing was employed to construct the smooth PM2.5 diurnal curve using averaged hourly data in each of the 30 stations. Functional principal component analysis (FPCA) was used to extract FPCA scores from pollutant curves, and LUR models were fitted on FPCA scores. The mean of all PM2.5 diurnal curves had a maximum of 39.58 µg/m3 at 00:26 a.m. and a minimum of 29.27 µg/m3 at 3:57 p.m. The FPCA explained about 99.5% of variations in the observed diurnal curves across the city using just three components. The evaluation of spatially predicted long-term PM2.5 diurnal curves every 15 min provided a series of 96 high-resolution exposure maps. The presented methodology and results could benefit future environmental epidemiological studies. [ABSTRACT FROM AUTHOR]

Complex nonlinear distributed parameter systems (DPSs) exist widely in advanced industrial thermal processes. The modeling of such highly nonlinear systems is a challenge for traditional time/space-separation-based methods since they employ linear methods for the model reduction and spatiotemporal reconstruction, which may lead to an inefficient application of the nonlinear spatial structure features represented by the spatial basis functions. To overcome this problem, a novel spatiotemporal modeling framework composed of nonlinear temporal domain transformation and nonlinear spatiotemporal domain reconstruction is proposed in this paper. Firstly, local nonlinear dimension reduction based on the locally linear embedding technique is utilized to perform nonlinear temporal domain transformation of the spatiotemporal output of nonlinear DPSs. In this step, the original spatiotemporal data can be directly transformed into low-order time coefficients. Then, the extreme learning machine (ELM) method is utilized to establish a temporal model. Finally, through the spatiotemporal domain reconstruction based on the kernel-based ELM method, the prediction of the temporal dynamics obtained from the temporal model can be reconstructed back to the spatiotemporal output. The effectiveness and performance of the proposed method are demonstrated in experiments on the thermal processes of a snap curing oven and a lithium-ion battery. [ABSTRACT FROM AUTHOR]

Discovering patterns and detecting anomalies in individual travel behavior is a crucial problem in both research and practice. In this paper, we address this problem by building a probabilistic framework to model individual spatiotemporal travel behavior data (e.g., trip records and trajectory data). We develop a two-dimensional latent Dirichlet allocation (LDA) model to characterize the generative mechanism of spatiotemporal trip records of each traveler. This model introduces two separate factor matrices for the spatial dimension and the temporal dimension, respectively, and use a two-dimensional core structure at the individual level to effectively model the joint interactions and complex dependencies. This model can efficiently summarize travel behavior patterns on both spatial and temporal dimensions from very sparse trip sequences in an unsupervised way. In this way, complex travel behavior can be modeled as a mixture of representative and interpretable spatiotemporal patterns. By applying the trained model on future/unseen spatiotemporal records of a traveler, we can detect her behavior anomalies by scoring those observations using perplexity. We demonstrate the effectiveness of the proposed modeling framework on a real-world license plate recognition (LPR) data set. The results confirm the advantage of statistical learning methods in modeling sparse individual travel behavior data. This type of pattern discovery and anomaly detection applications can provide useful insights for traffic monitoring, law enforcement, and individual travel behavior profiling. [ABSTRACT FROM AUTHOR]

This study presents a deep-learning-based forecast model for spatial and temporal prediction of pluvial flooding. The developed model can produce the flooding situation for the upcoming time steps as a sequence of flooding maps. Thus, a dynamic overview of the forthcoming flooding situation is generated to support the decision of crisis management actors. The influence of different input data, data formats, and model setups on the prediction results was investigated. Data from multiple sources were considered as follows: precipitation information, spatial information, and an overflow forecast. In addition, models with different layers and network architectures such as convolutional layers, graph convolutional layers, or generative adversarial networks (GANs) were considered and evaluated. The data required to train and test the models were generated using a coupled hydrodynamic 1D/2D model. The model setup with the inclusion of all available input variables and an architecture with graph convolutional layers presented, in general, the best results in terms of root mean square error (RMSE) and critical success index (CSI). The prediction results of the final model showed a high agreement with the simulation results of the hydrodynamic model, with drastic reductions in computation time, making this model suitable for integration into an early warning system for pluvial flooding. [ABSTRACT FROM AUTHOR]